Calculations on Equation of State - PVT

This is the wikipedia link. Surprisingly there are several of these calcs that were developed quite recently.  

Keep Miles' papers in mind:
http://milesmathis.com/waals.pdf
http://milesmathis.com/boltz.pdf
http://milesmathis.com/stark.pdf
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Equation of state

In physics and thermodynamics, an equation of state is a thermodynamic equation relating state variables which describe the state of matter under a given set of physical conditions, such as pressure, volume, temperature (PVT), or internal energy.[1] Equations of state are useful in describing the properties of fluids, mixtures of fluids, solids, and the interior of stars.

https://en.wikipedia.org/wiki/Equation_of_state#Van_der_Waals_equation_of_state

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Elliott, Suresh, Donohue equation of state

The Elliott, Suresh, and Donohue (ESD) equation of state was proposed in 1990.[18] The equation seeks to correct a shortcoming in the Peng–Robinson EOS in that there was an inaccuracy in the van der Waals repulsive term. The EOS accounts for the effect of the shape of a non-polar molecule and can be extended to polymers with the addition of an extra term (not shown). The EOS itself was developed through modeling computer simulations and should capture the essential physics of the size, shape, and hydrogen bonding.




Noting the relationships between Boltzmann's constant and the Universal gas constant, and observing that the number of molecules can be expressed in terms of Avogadro's number and the molar mass, the reduced number density η {\displaystyle \eta } \eta can be expressed in terms of the molar volume as

The shape parameter q {\displaystyle q} q appearing in the Attraction term and the term Y {\displaystyle Y} Y are given by

   q = 1 + k 3 ( c − 1 ) {\displaystyle q=1+k_{3}(c-1)} q=1+k_3(c-1) (and is hence also equal to 1 for spherical molecules).

   Y = exp ⁡ ( ϵ k T ) − k 2 {\displaystyle Y=\exp \left({\frac {\epsilon }{kT}}\right)-k_{2}} Y=\exp\left(\frac{\epsilon}{kT}\right) - k_2

where ϵ {\displaystyle \epsilon } \epsilon is the depth of the square-well potential and is given by

   ϵ k = 1.000 + 0.945 ( c − 1 ) + 0.134 ( c − 1 ) 2 1.023 + 2.225 ( c − 1 ) + 0.478 ( c − 1 ) 2 {\displaystyle {\frac {\epsilon }{k}}={\frac {1.000+0.945(c-1)+0.134(c-1)^{2}}{1.023+2.225(c-1)+0.478(c-1)^{2}}}} \frac{\epsilon}{k} =\frac{1.000+0.945(c-1)+0.134(c-1)^2}{1.023+2.225(c-1)+0.478(c-1)^2}

   z m {\displaystyle z_{m}} z_{m}, k 1 {\displaystyle k_{1}} k_{1}, k 2 {\displaystyle k_{2}} k_{2} and k 3 {\displaystyle k_{3}} k_{3} are constants in the equation of state:
   z m = 9.49 {\displaystyle z_{m}=9.49} z_m = 9.49 for spherical molecules (c=1)
   k 1 = 1.7745 {\displaystyle k_{1}=1.7745} k_1 = 1.7745 for spherical molecules (c=1)
   k 2 = 1.0617 {\displaystyle k_{2}=1.0617} k_2 = 1.0617 for spherical molecules (c=1)
   k 3 = 1.90476. {\displaystyle k_{3}=1.90476.} k_3 = 1.90476.

The model can be extended to associating components and mixtures of nonassociating components. Details are in the paper by J.R. Elliott, Jr. et al. (1990).[18]

Cubic-Plus-Association

The Cubic-Plus-Association (CPA) equation of state combines the Soave-Redlich-Kwong equation with an association term from Wertheim theory.[19] The development of the equation began in 1995 as a research project that was funded by Shell, and in 1996 an article was published which presented the CPA equation of state.[19][20]

In the association term X A {\displaystyle X^{A}} {\displaystyle X^{A}} is the mole fraction of molecules not bonded at site A.

CPA Equation Society
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Cubic-Plus-Association Equation of State for Water-Containing Mixtures: Is ‘‘Cross Association’’ Necessary?

Zhidong Li and Abbas Firoozabadi
Reservoir Engineering Research Institute (RERI), Palo Alto, CA 94306
DOI 10.1002/aic.11784
Published online June 4, 2009 in Wiley InterScience (www.interscience.wiley.com).

We have recently proposed an accurate version of the cubic-plus-association (CPA) equation of state (EOS) for water-containing mixtures which combines the Peng-Robinson equation (PR) for the physical interactions and the thermodynamic perturbation theory for the hydrogen bonding of water molecules.

Despite the significant improvement, the water composition in the nonaqueous phase is systematically underestimated for some systems where the nonwater species are methane and ethane at very high pressures, unsaturated hydrocarbons, CO2, and H2S. We attribute the deficiency to the neglect of the ‘‘cross association’’ between water and those nonwater molecules. In this work, the accuracy is drastically improved by treating methane, ethane, unsaturated hydrocarbons, CO2and H2S as ‘‘pseudo-associating’’ components and describing the cross association with water in the framework of the perturbation theory. It isshown that the cross association is more significant for the nonaqueous phase. In addition to binary mixtures, reliable predictions are achieved for H2O/C1/CO2/H2S quaternary mixture in two and three phases

https://www.eng.yale.edu/aflab/archive/pdf/5water_HC21.pdf

.VVC2009 American Institute of Chemical Engineers
AIChE J, 55: 1803–1813, 2009Keywords: petroleum, phase equilibrium, aqueous solutions

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Cr6 wrote,

This is the wikipedia link. Surprisingly there a several of these calcs that were developed quite recently.

Keep Miles' papers in mind:
http://milesmathis.com/waals.pdf
http://milesmathis.com/boltz.pdf

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Equation of state

...

The wiki page does list many state equations. Elliott, Suresh, Donohue equation of state and Cubic-Plus-Association Equation of State for Water-Containing Mixtures: Is ‘‘Cross Association’’ Necessary? contains college degrees of chemistry in and of themselves.
 
I must ask. Are you providing information that would be useful in determining how much hydrogen some algae might be producing? So that such discussion would occur here rather than the other thread?

Well even if you didn’t, thanks. My copy of boltz.pdf was the original new paper from http://milesmathis.com/updates.html from 6/7/2013 and not the 6/11/2013 updated boltz.pdf paper which doesn’t appear on the Updates page. Glad I got to read the updated paper, of course I’ll need several re-reads to feel like I truly understand it.

Especially nice if those state equations - mostly modifiers I suppose - were replaced with charge field equations.
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Hi LTAM,

I was going to post this in the algae O&G thread but it looked like a group of interesting equations that can only handle certain molecular states accurately. This is definitely advanced and doesn't look exactly complete even today. I was hoping a new discussion could start on it. I may need to clean up the math syntax.  The Wikipedia page refers to several papers based on laser experiments. Someday a cleaner Miles-photon based explanation will prevail. In my lifetime? I just don't know. I didn't know there were two boltz papers. This was intended to show that PVT is really tricky as molecular changes occur with each variable and bonding shifts unexpectedly especially with H2O.

Cr6 wrote. I was hoping a new discussion could start on it

Sorry for the delay Cr6. My schedule has been off for weeks.

That wiki wall of EoS PVT math you posted, unreadable till you cleaned the syntax (thank you), remains indecipherable to me. Of course we have Miles’ papers (and models) in mind.

Can we model the EoS PVT with respect to charged particles? Sure, but with varying degrees of ‘accuracy’.  Approximating physics/modeling, distinguishing between molecules, in aqueous solution or not – seems a bit ambitious, to say the least. On the other hand, modeling a simple charged particle gas (i.e. hydrogen) should be doable. A worthy project, needs plenty of thought. No guarantees of course.

Any objection or friendly suggestion?

Anyone?

Can we model the EoS PVT with respect to charged particles? Sure, but with varying degrees of ‘accuracy’.  Approximating physics/modeling, distinguishing between molecules, in aqueous solution or not – seems a bit ambitious, to say the least. On the other hand, modeling a simple charged particle gas (i.e. hydrogen) should be doable. A worthy project, needs plenty of thought. No guarantees of course

I agree there LTAM. The building up from the most fundamental particle gas(es) would be a good start. I think these approaches are good at first with the modeling until the heat and pressure reach critical points where the model just collapses -- because in practical terms...it is not C.F. based. How methane forms deep in the earth (i.e., serpentization, critical water temps) is still a bit of a mystery to even seasoned researchers.

Cr6 wrote. I agree there LTAM. The building up from the most fundamental particle gas(es) would be a good start.

I agree. In that case, let’s back up a bit. A gas is some collection of charged particles. The first requirement is a working charged particle model.

A charged particle is a photon large enough to be recycling charge, such as electrons, protons, and neutrons. A distinction being, even at the poles, the charged particle surface is penetrable by photons but not by electrons.

I might hope to consider an electron ‘gas’, the overall goal is to model a molecular gas, starting with Hydrogen.

At which point one must read (one of my favorites).  

http://milesmathis.com/updates.html
http://milesmathis.com/
268b. The Stark Effect. http://milesmathis.com/stark.pdf Not only do I show charge field causes of both the shift and the split, I show a new cause of spectral lines—one that has nothing to do with electron orbitals. 40pp.

Don’t let that 40pp scare you, I think it should read 14pp.

The paper does a fine job describing how a c.f. gas creates spectral emissions. Due to the main emission field present, and a second applied electrical - linear field. Plenty to consider, for example, I need to understand the quantization of electron and ion energy levels; or, how rapidly moving gas particles and their polar vortices manage to intercept all passing photons.

I’m thinking of having another go at my earlier threejs Boids work; but a big problem is Windows 10 CORS policy prevents me from accessing three.js textures across my directory; even though I downloaded and installed a recent threejs version I cannot view webgl_gpgpu_birds or most other examples found at, https://threejs.org/examples/#webgl_animation_cloth

No good alternatives come to mind.
.

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https://en.wikipedia.org/wiki/Boids

Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference. [1] The name "boid" corresponds to a shortened version of "bird-oid object", which refers to a bird-like object.[2] Incidentally, "boid" is also a New York Metropolitan dialect pronunciation for "bird".

As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. The rules applied in the simplest Boids world are as follows:
• separation: steer to avoid crowding local flockmates
• alignment: steer towards the average heading of local flockmates
• cohesion: steer to move towards the average position (center of mass) of local flockmates

More complex rules can be added, such as obstacle avoidance and goal seeking.

I gave Birds a big intro way back when I first mentioned it here at the site, but I don’t recall it being called Boids. I'd say Birds makes an excellent starting point for modeling a gas. Replace the small set of bird interaction rules with charged particle interactions. During each time interval, each charged particle position is updated and its velocity is recalculated according to those rules.


That’s what I did, which I referred to as Boids. In my code, each individual particle interacts with every other particle via ‘long range’ attraction, aka gravity, and close range charge repulsion. I also added perfect inelastic collisions between particles. The result is a group of particles in a large transparent ‘box’ that interact in interesting ways. My main complaint at the time was that I had no mechanism to alter an individual particles’ spin axis direction – as occurs in precession. Anyway, I went ahead and updated boids so that it could run with the more recent threejs version 109, and gave it a new project folder.

Miles indicated that the charge field creates order in a gas. Reviewing my boids effort today, I’d say the main deficiency is the lack of a charge field; like the upwardly traveling earth emission photons which hold a gas aloft about me. Those photons might appear as a uniformly distributed upwardly traveling ‘rain’, much smaller than the charged particle. Each collision between charge field photon and charged particle (i.e. electron) transfers a good deal of energy. I suspect that’s a good place to start looking for a mechanism that would turn and align charged particles.
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Hi LTAM,

Boids looks really cool.  I see the trouble with this is representing "state" of molecule-atomic motions with the C.F.  - Boids might provide a solution or at least represent it.

Sorry I didn't see your earlier post from June 29 or else I would have responded sooner.  

LTAM wrote:A charged particle is a photon large enough to be recycling charge, such as electrons, protons, and neutrons. A distinction being, even at the poles, the charged particle surface is penetrable by photons but not by electrons.

I might hope to consider an electron ‘gas’, the overall goal is to model a molecular gas, starting with Hydrogen.

At which point one must read (one of my favorites).  

http://milesmathis.com/updates.html
http://milesmathis.com/
268b. The Stark Effect. http://milesmathis.com/stark.pdf Not only do I show charge field causes of both the shift and the split, I show a new cause of spectral lines—one that has nothing to do with electron orbitals. 40pp.

Don’t let that 40pp scare you, I think it should read 14pp.

The paper does a fine job describing how a c.f. gas creates spectral emissions. Due to the main emission field present, and a second applied electrical - linear field. Plenty to consider, for example, I need to understand the quantization of electron and ion energy levels; or, how rapidly moving gas particles and their polar vortices manage to intercept all passing photons.

You mention modeling gases and pointed Miles Stark.pdf paper on Spectral emissions and how the Polar Vortex shapes output/atomic reactions.  
Okay, for the next item after Hydrogen maybe Methane? -- just to see what can be done. The only reason is that Methane can be created abiotically with Ironoxides (FEO3) and Calcites-Carbonates deep underground with the right temperature and pressure. There isn't a fundamental detailed explanation to indicate what is happening with certain deep earth chemical reactions... it is just too remote to monitor-measure. Some experimenters had success with Diamond Anvils (pressure) and lasers (temperature) in lab experiments.  Findings are below.  It would be noble effort to determine this with Methane-Hydrogen with the C.F.
These papers had me thinking about Modeling PVT with the C.F and then I saw your post!  





Details on the reactions to create Methane abiotically based on Thomas Gold's early work/ideas:
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22-Aug-2005
The search for methane in Earth's mantle

DOE/Lawrence Livermore National Laboratory

In one experiment, a sample of iron oxide, calcite, and water is heated to 600°C at a pressure of about 2 gigapascals. Raman spectra of the sample show a carbon-hydrogen (C-H) stretching vibration at 2,932 centimeters-1, which is the industry-standard signature for methane.

Click here for a high resolution photograph.

Petroleum geologists have long searched beneath Earth's surface for oil and gas, knowing that hydrocarbons form from the decomposition of plants and animals buried over time. However, methane, the most plentiful hydrocarbon in Earth's crust, is also found where biological deposits seem inadequate or improbable--for example, in great ocean rifts, in igneous and metamorphic rocks, and around active volcanoes. Some scientists thus wonder whether untapped reserves of natural gas may exist in Earth's mantle.

A collaboration of researchers from Lawrence Livermore and Argonne national laboratories, Carnegie Institution's Geophysical Laboratory, Harvard University, and Indiana University at South Bend is finding that methane may also be formed from nonbiological processes. Experiments and calculations conducted by the team indicate that Earth's mantle may provide the temperature and pressure conditions necessary to produce methane.

The idea that methane could be formed nonbiogenically came from observing the solar system. In the 1970s, astronomer Thomas Gold proposed that methane must form from nonbiogenic materials as well as from biological decomposition because large amounts of methane and other hydrocarbons could be detected in the atmospheres of Jupiter, Saturn, Uranus, and Neptune. In fact, in studying Titan, Saturn's largest moon, researchers found seven different hydrocarbons. At the time Gold proposed this theory, conventional geochemists argued that hydrocarbons could not possibly reside in Earth's mantle.

They reasoned that at the mantle's depth--which begins between 7 and 70 kilometers below Earth's surface and extends down to 2,850 kilometers deep--hydrocarbons would react with other elements and oxidize into carbon dioxide. (Oil and gas wells are drilled between 5 and 10 kilometers deep.)

However, more recent research using advanced high-pressure thermodynamics has shown that the pressure and temperature conditions of the mantle would allow hydrocarbon molecules to form and survive at depths of 100 to 300 kilometers. Because of the mantle's vast size, its hydrocarbon reserves could be much larger than those in Earth's crust.

Simulating Thermochemical Conditions

Livermore's work on the methane research, led by chemist Larry Fried, uses a thermodynamics code called CHEETAH to simulate chemical reactions using data from the collaboration's experiments. Fried developed CHEETAH in 1993 for the Department of Defense (DoD) to predict the performance of different explosives formulations. Since then, Fried and his colleagues have continued to improve the code. (See S&TR, May 1999, Leveraging Science and Technology in the National Interest; June 1999, Unraveling the Mystery of Detonation; July/August 2003, A New Generation of Munitions; July/August 2004, Going to Extremes.)

https://www.eurekalert.org/features/doe/2005-08/drnl-tsf082205.php

https://www.pnas.org/content/101/39/14023

Also:
Stability of hydrocarbons at deep Earth pressures
https://www.pnas.org/content/108/17/6843
https://www.llnl.gov/news/methane-deep-earth-possible-new-source-energy

Keep in mind that Methane is eaten by bacteria. This too is happening as methane seeps up into traps/seals. Hard to say if it helps with Oil formation.

https://helix.northwestern.edu/blog/2017/09/bacteria-eat-methane

Interesting paper on the reactions involved:

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Methane-Consuming Bacteria Could Be the Future of Fuel

Discovery illuminates how bacteria turn methane gas into liquid methanol

May13th,2019
Northwestern University


methaneThe primary metabolic enzyme in methanotrophic bacteria, particulate methane monooxygenase (pMMO), catalyzes the methane-to-methanol conversion at a site with one copper ionCREDIT: Northwestern University

Known for their ability to remove methane from the environment and convert it into a usable fuel, methanotrophic bacteria have long fascinated researchers. But how, exactly, these bacteria naturally perform such a complex reaction has been a mystery.

Now an interdisciplinary team at Northwestern University has found that the enzyme responsible for the methane-methanol conversion catalyzes this reaction at a site that contains just one copper ion.

This finding could lead to newly designed, human-made catalysts that can convert methane—a highly potent greenhouse gas—to readily usable methanol with the same effortless mechanism.

"The identity and structure of the metal ions responsible for catalysis have remained elusive for decades," said Northwestern's Amy C. Rosenzweig, co-senior author of the study. "Our study provides a major leap forward in understanding how bacteria methane-to-methanol conversion."

"By identifying the type of copper center involved, we have laid the foundation for determining how nature carries out one of its most challenging reactions," said Brian M. Hoffman, co-senior author.

The study was published on Friday, May 10 in the journal Science. Rosenzweig is the Weinberg Family Distinguished Professor of Life Sciences in Northwestern's Weinberg College of Arts and Sciences. Hoffman is the Charles E. and Emma H. Morrison Professor of Chemistry at Weinberg.

By oxidizing methane and converting it to methanol, methanotrophic bacteria (or "methanotrophs") can pack a one-two punch. Not only are they removing a harmful greenhouse gas from the environment, they are also generating a readily usable, sustainable fuel for automobiles, electricity and more.

Current industrial processes to catalyze a methane-to-methanol reaction require tremendous pressure and extreme temperatures, reaching higher than 1,300 degrees Celsius. Methanotrophs, however, perform the reaction at room temperature and "for free."

https://www.labmanager.com/news/methane-consuming-bacteria-could-be-the-future-of-fuel-1876

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Methanol's properties:

http://www.oilfieldwiki.com/wiki/Methanol

Down the road it would be cool to create Probablistic models with the C.F.:

Molecular Geometry Prediction using a Deep Generative Graph Neural Network

Elman Mansimov, Omar Mahmood, Seokho Kang & Kyunghyun Cho
Scientific Reports volume 9, Article number: 20381 (2019) Cite this article

Abstract

A molecule’s geometry, also known as conformation, is one of a molecule’s most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations.

Introduction

The three-dimensional (3-D) coordinates of atoms in a molecule are commonly referred to as the molecule’s geometry or conformation. The task, known as conformation generation, of predicting possible valid coordinates of a molecule, is important for determining a molecule’s chemical and physical properties1. Conformation generation is also a vital part of applications such as generating 3-D quantitative structure-activity relationships (QSAR), structure-based virtual screening and pharmacophore modeling2. Conformations can be determined in a physical setting using instrumental techniques such as X-ray crystallography as well as using experimental techniques. However, these methods are typically time-consuming and costly.

A number of computational methods have been developed for conformation generation over the past few decades2. Typically this problem is approached by using a force field energy function to calculate a molecule’s energy, and then minimizing this energy with respect to the molecule’s coordinates. This hand-designed energy function yields an approximation of the molecule’s true potential energy observed in nature based on the molecule’s atoms, bonds and coordinates. The minimum of this energy function corresponds to the molecule’s most stable configuration. Although this approach has been commonly used to generate a geometrically diverse set of conformations with certain conformations being similar to the lowest-energy conformations, it has been shown that molecule force field energy functions are often a crude approximation of actual molecular energy3.

In this paper, we propose a deep generative graph neural network that learns the energy function from data in an end-to-end fashion by generating molecular conformations that are energetically favorable and more likely to be observed experimentally4. This is done by maximizing the likelihood of the reference conformations of the molecules in the dataset. We evaluate and compare our method with conventional molecular force field methods on three databases of small molecules by calculating the root-mean-square deviation (RMSD) between generated and reference conformations. We show that conformations generated by our model are on average far more likely to be close to the reference conformation compared to those generated by conventional force field methods i.e. the variance of the RMSD between generated and reference conformations is lower for our method. Despite having lower variance, we show that our method does not generate geometrically similar conformations. We also show that our approach is computationally faster than force field methods.

A disadvantage of our model is that in general for a given molecule, the best conformation generated by our model lies further away from the reference conformation compared to the best conformation generated by force field methods. We show that for the QM9 small molecule dataset, the best of both methods can be combined by using the conformations generated by the deep generative graph neural network as an initialization to the force field method.

more at link: https://www.nature.com/articles/s41598-019-56773-5

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Autoencoding Undirected Molecular Graphs With Neural Networks

Jeppe Johan Waarkjær Olsen, Peter Ebert Christensen, Martin Hangaard Hansen,
and Alexander Rosenberg Johansen∗

Department of Computing, Technical University of Denmark
E-mail: aler@dtu.dk


Abstract

Discrete structure rules for validating molecular structures are usually limited to
fulfilment of the octet rule or similar simple deterministic heuristics. We propose a
model, inspired by language modeling from natural language processing, with the ability to learn from a collection of undirected molecular graphs, enabling fitting of any
underlying structure rule present in the collection.

We introduce an adaption to the popular Transformer model, which can learn relationships between atoms and bonds. To our knowledge, the Transformer adaption is the first model that is trained to solve the unsupervised task of recovering partially observed molecules. In this work, we assess how different degrees of information impacts performance w.r.t. to fitting the QM9 dataset, which conforms to the octet rule, and to fitting the ZINC dataset, which contains hypervalent molecules and ions requiring the model to learn a more complex structure rule. More specifically, we test a full discrete graph with bond order information, full discrete graph with only connectivity, a bag-of-neighbors, a bag-of-atoms, and a count-based unigram statistics.

https://arxiv.org/pdf/2001.03517.pdf

Chromium6 wrote:

Down the road it would be cool to create Probablistic models with the C.F.:

and  

Okay, for the next item after Hydrogen maybe Methane? -- just to see what can be done.

Airman. Nothing works for the time being. First electrons, then protons, and alphas (positive and negative protons, 2 neutrons and 2 electrons(?)) would come before Methane. Methane would be wonderful.

To be positive, I like the word ‘conformation’ in your quote of Molecular Geometry Prediction using a Deep Generative Graph Neural Network. I guess it means the unique physical configuration of a molecule. Everything else in that paper quote seems like highly sophisticated programming enabling your computer to do all the quess-work for you. The other paper, Autoencoding Undirected Molecular Graphs With Neural Networks and the popular “Transformer model”, that can learn sounds like something to be frightened of.

A possible way forward, I need to simplify further. Consider a single charged particle (someday a gas molecule) - in a charge field of photons.

Each 'boid' is an individual charge field photon, such as the photons of the Earth’s upward emission field. As photons leave the volume of space around the charged particle, they are replaced with new photons in accordance with the local charge field density and direction. Photon sizes may change as a result of collisions.

I’ll give it more consideration and maybe start a new project.  
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Let's try to build a new project on this then.

Found this of interest that builds up a rules engine. Might need it X-Y-Z not just X-Y.

https://community.fico.com/s/sudoku-rules-episode-1

Classical Reference: http://ww2.chemistry.gatech.edu/~lw26/structure/molecular_interactions/mol_int.html#B

Chromium6 wrote.

Let's try to build a new project on this then.  

Airman. OK.

Chromium6 wrote.

Found this of interest that builds up a rules engine. Might need it X-Y-Z not just X-Y.

https://community.fico.com/s/sudoku-rules-episode-1

Fernando Donati Jorge wrote.

As luck would have it, I happen to be the product manager for one of the greatest inference engines on the planet, FICO Blaze Advisor. I set myself the challenge of using the optimized inference engine, along with a few other advanced features, of FICO’s industry-leading decision rules management solution to solve Sudoku puzzles.
…
The basic Sudoku rule was clearly not sufficient to solve the Medium puzzle. I was going to have to think of another strategy. I knew the answer was somewhere in the partially completed puzzle. But the solution has to wait for the next episode.

To try this out for yourself, download a free, 90-day trial of Blaze Advisor.

Airman. A cliffhanger eh?


Here’s a screen capture of the program output created by Fernando Donati Jorge.

Today is 16 July, more than a month later, where’s Episode 2?

I must admit, unlike those neural net programming papers I found the Sudoku article extremely interesting. I can come up additional rules such as assuming values. I’m sure I could program such a solver.

Thanks for the discussion Cr6, reassuring me my head is still normal. I’ll grant you I certainly like to modify tools to fit the occasion – such as my particle engine (Boids), to particle problems; I don’t see how I might apply Sudoku Rules – or a 9x9x9 x,y and z analog might apply to a charge particle.

Thanks for that Molecular Interactions link I just noticed. Must review.

My latest thoughts were of photons within the charge particle interior. Of course we’re familiar with Nevyn’s Proton as bphoton in stacked spin motion model. As I’ve indicated in the past, I like the idea of entraining photons within the charge particle’s stacked spins. I think I might be able be able to simulate that idea.
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I was looking at Sudoku rules engines to see how to apply it to a large cube and create "conformity" with Nevyn's stacked spins model like you mention. Essentially it is applying Miles-Nevyn's rules in a manner like Sudoku does to a cube.

Basically, on this face with this atom what rules can be applied for allowed bonding with that atom over there? +,-, null,0 to X a c.f. strength number per small square? ...These are the sudoku numbers we are working with.  This of course would be very simplified. I hope this makes some sense without over simplification. At this point I was trying to take motion out of it and reduce it to allowed or not allowed with that other atom-molecule. similar to a sudoku square for photons in a snapshot in time.  

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Giving it more thought Cr6, fair enough, I think I see what you're saying. A three-D (six-sided cube) set of Sudoku puzzles suggests perfectly valid sudoku-like rules might represent an atom’s six charge current flows: (2 main (axial - up/down), and 4 secondary (carousal - front/back, left/right). Each flow is centered on its own external cube-face indicating one particular charge channel’s atomic interface ‘values’. Such an idea may help describe molecular bonds. The actual rules are not yet defined. Most importantly, in any case, the mathematics must agree with the physical situation.

I usually think of electron or proton charge particles as spherical or toroidal or the proton may appear as a disc in atomic diagrams. He(2) is an alpha (two discs) - please pardon my repetitions. Larger atoms can be of stacks of protons and alphas in the six charge directions I tend to think of as octahedral (6 vertices). The face centered charge cube perspective is interesting. Playing with charge powered Atomic blocks.

If I'm misunderstanding you – nothing unusual about that - please let me know. I’m still concentrating, albeit slowly, on single charge particles and their constituent photons.
.

He LTAM,

No worries. You see what I'm getting at. I was looking at structurally what approaches could be used to make modeling C.F. bonding easier. I just thought this up. May not be applicable for a boids-photon approach.

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Thanks for your time, effort and motivating inputs Cr6.

Please pardon my delay. I’m slowly working through what I hope are just initial problem/decisions, mainly re-familiarizing myself with the code. Birds (the particle engine), is a well-developed threejs.org example that I certainly didn’t understand when I first started playing with it. I really must work at understanding it better. I may need to rewrite it. I’m still considering how I might implement the few project ideas I've already mentioned. At present I'm nowhere near making any sort of positive progress. Call it review mode.
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Status update. I’m messing with the particle engine, making new particles, but still very much stuck at the conceptualization stage. For one thing, before coding angular momentum exchanges I believe a review of Miles’ work is in order, such as re-reading Angular Velocity and Angular Momentum*. A wonderful paper with plenty to learn, such as, get rid of the radians and stick with tangential and angular velocities. Unless I’m sadly mistaken, I guess the most important point is that the angular velocity is an inverse function of the size of the radius.

And before I can attempt to portray electrons entraining photons, I must read and understand Miles’ The Electron Radius as a Function of c.

Miles wrote. Even if the trapping of photons by large spins is just an accident, caused by no intention of any electron, the trapping could still function to keep the electron viable.

A great paper, it’s got all kinds of important insight and numbers pertaining to electrons, such as ‘Energy expressed as electron radii’.  

I may not understand them but I'm trying.

P.S. Here's another recycling quote from The Electron Radius as a Function of c.

The only difference is, at the size of the electron, the particle becomes large enough to begin “eating” smaller particles. The big outer spins are large enough to trap and intake smaller photons, recyling them. These recycled photons then become the charge field.

*
http://milesmathis.com/index.html
2. Angular Velocity and Angular Momentum. http://milesmathis.com/angle.html Both current equations are shown to be false. 5pp.
256.The Electron Radius as a Function of c http://milesmathis.com/elec3.html I show the flaw in the current equation for the classical electron radius: a scaling constant has been left out, giving us a radius too large by 252x. 6pp.
257. The Electron Radius is e^2. http://milesmathis.com/elecrad.pdf It is also 1/c2 (using novel dimensional analysis. 2pp.
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Status update. Since last time, wondering what the heck a model of a charge particle or electron or proton should look like, I hit bottom. For positive motivation I re-read the first ten chapters of The Un-Unified Field and Other Problems. As usual, I found many applicable quotes like my most recent post script addition “particles large enough to eat smaller particles”.

The “Spin Stacking” image above shows my guess as to what the charge particle stacked spin motion mechanism looks like, showing the relative size/spin levels between say a bphoton (blue sphere, neg z spin axis) and an electron (gray Torus, neg z spin axis), or between an electron (blue sphere, …) and a proton (gray Torus, …). Seems like way too few stacked spins, Miles mentioned a mass level difference between the electron and proton. Which may or may not include higher level A spins as per our earlier discussions. The upwardly directed (with its source in the neg z direction) local charge field photons and their spins, should cause the gray charge particle to orient its top spin orthogonal to those incoming photons.

The toruses are full with photons and smaller particles. The charge particle would appear as masses of photons rotating within each torus spin level. You may recall I’ve tried sharing my idea a couple of times here without success. After a charged particle’s outermost spin level is created, there is plenty of room to capture particles. After the new level is filled, as a new photon enters (from any direction) a given stack spin level torus, another photon is forced out, generally tangentially outward from the light speed tangential spin velocity at the particle’s equator; or something like that.

I don't imagine its easy, but its worth a try.

P.S. I flipped the image's z orientation, so that the neq z direction is 'down'. And corrected to - After the new level 'is filled', ... .  
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Hi LTAM...thanks for your dedication to outlining this model for stack spins conceptually it is not easy and thanks for the lost of papers to focus with. It takes a lot of focus, time and effort. Found this recent paper on methane generation at the nano level.  Hints to me that this will become a long term research focus.
....

Fuel
Volume 277, 1 October 2020, 118234
Review article

Review of impact of nanoparticle additives on anaerobic digestion and methane generation

Author links open overlay panel
C.M.Ajaya
Marc A.Rosenberg

https://doi.org/10.1016/j.fuel.2020.118234
Get rights and content

Highlights
• Influence of nanoparticles on biogas and methane production is examined.
• Effect on interspecies electron transfer in anaerobic digestion is elucidated.
• Role of conductive nanoparticles on biogas generation is emphasized.


Abstract

Anaerobic digestion is a globally used biochemical process to convert the organic matter present in wastes into an energy rich biogas with methane as the major constituent. Implementing additives in the anaerobic digestion process enhances biogas production. Recent studies have revealed that nanoparticles additives influence the anaerobic digestion. Interspecies electron transfer (IET) is the main mechanism behind methane production. Apart from traditional IET routes, like IET through hydrogen and formate, direct interspecies electron transfer (DIET) through conductive materials have gained importance. This paper reviews DIET via abiotic conductive materials and describes the impact of various nanoparticle additives on anaerobic digestion. The paper also discusses the positive and negative impacts of nanoscale materials on biogas production. This study confirms that proper screening of nanoparticles based on physicochemical characteristics and other factors supportive of DIET can enhance the performance of the anaerobic digestion process, thereby increasing biogas generation.

https://www.sciencedirect.com/science/article/abs/pii/S0016236120312308

More on DIET: https://www.sciencedirect.com/topics/engineering/direct-interspecies-electron-transfer

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Sorry Cr6, over the last few weeks, I haven’t gotten very far, I’ve been stuck at a single question - what does a charged particle look like? Since my progress is so slow and doesn’t involve pressure, volume or temperature I’ll move it its own thread.
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Looks like they are starting to build "reactors" to convert source methane to "syngas" and "syn-fuels". The EU is funding a lot this with grants apparently:
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Development of a bifunctional hierarchically structured zeolite based nano-catalyst using 3D-technology for direct conversion of methane into aromatic hydrocarbons via methane dehydroaromatization

The EU-funded ZEOCAT-3D project developed a new technology for directly converting methane into high-value aromatic compounds – benzene and naphthalene – and hydrogen. The new process can more efficiently convert methane from stranded sources into shippable liquid fuels and could also help the industry reduce greenhouse gas emissions.

An alternative, single-step gas-to-liquids process

The conversion of methane – the principal component of natural gas and biogas – to fuels and starting materials for the chemical industry is called gas-to-liquids. Most technologies involve converting methane and carbon dioxide into a mixture of hydrogen molecules and carbon monoxide, so-called syngas. From syngas, various products such as olefins, gasoline, diesel and oxygenates can be obtained using the well-established Fischer-Tropsch process. Alternatively, syngas can be converted into synthetic fuels and other important products through methanol-to-gasoline or methanol-to-olefin processes.

“These commercial approaches are feasible at large scales but involve multiple steps for methane conversion. Until now, no direct processes have been developed at an industrial scale and commercialised,” remarks project coordinator Maria Tripiana. What’s more, syngas conversion is energy-intensive and expensive, while oxygen needs to be removed from syngas before being converted into hydrocarbons.

“In ZEOCAT-3D, we proposed a more viable and environmentally friendly method for methane conversion that eliminates intermediate steps. We used a chemical reaction called dehydroaromatisation that directly converts methane into aromatic compounds and hydrogen,” explains Tripiana. “As alternatives to oil, benzene and naphthalene are very interesting raw materials for the production of liquid fuels and high-value chemicals. Furthermore, hydrogen is extracted as a coproduct, which could serve for ammonia production or in fuel cells.”

The keys to success: 3D-printed catalysts and advanced reactor design
Existing catalysts used to speed up methane dehydroaromatisation are not very efficient. ZEOCAT-3D’s novel catalysts tackled two big challenges standing in the way of this chemical reaction: difficulty in obtaining the desired high-value compounds as unwanted by-products are often formed (poor selectivity), and quick catalyst deactivation owing to carbon deposition in the catalyst pores, a process known as coking.

“Using hierarchical modelling and simulations, we showed that if you control the nanoparticle size, morphology and degree of agglomeration, coking is no longer a threat,” stresses Tripiana.

“In our case, we used digital light processing to synthesise 3D zeolites with higher catalytic activity. We were the first to demonstrate novel hierarchical zeolites embedding four distinct pore structures. Bringing together two or more zeolite pore topologies at the mesoscale offers the opportunity to optimise nanoparticle transport and selective conversion of reaction intermediates,” adds Tripiana.

The catalytic reactor prototype integrated a purification system yielding methane above 95 % purity, a hydrogen-selective ceramic membrane and a filtration system removing particulates entrained in the product flow (either carbonaceous or ash). This compact, modular reactor now treats 4 normal litres per minute of gas flow and produces 40 grams per hour of high-value products.

ZEOCAT-3D provided new insights into the design of highly efficient catalysts and reactors for the production of valuable products, potentially reducing greenhouse gas emissions, which could be a building block for a sustainable circular economy. ZEOCAT-3D outcomes will guide future projects in bringing the proposed technology to a higher maturity level.

https://cordis.europa.eu/article/id/443350-a-one-step-green-and-economical-way-to-convert-methane-to-liquid-fuels
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The company in the paper above is creating an AI platform for conversion modeling.
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Finding new electrode materials for reversible fuel cell technologies.

Materials+AI


Project KNOWSKITE-X: Knowledge-driven fine-tuning of perovskite-based electrode materials for reversible chemicals-to-power devices.
The project targets a knowledge-based methodological entry to the finding of new generation electrode materials based on perovskites for reversible SOFC/SOEC technologies.

Such technologies are archetypal complex systems: the physico-chemical processes at play involve surface electrochemical reactions, ionic diffusion, charge collection and conduction, which all occur timely within a very limited region. Hence, true in-depth understanding of the key parameters requires characterization at the right place, at the right time frame and under the proper operating conditions. The price to pay for achieving this multiply-relevant characterization is the involvement of non-trivial, advanced characterization techniques. The project’s multi-scale modelling approach contributes to turn experimental datasets into a genuine scientific description and make time-saving predictions.

In the project, the coupling between theoretical and experimental activities is made real by the choice of partners, who are all active in genuinely articulate theory and practice to understand active systems. To provide unifying concepts and to widen the project’s outcomes, intensive collaboration with knowledge discovery using machine-learning and deep learning methods is planned and AI-enabled tools will be used to compensate the smallness of relevant datasets. Such efforts are intended in view of building strong correlations capable of establishing robust composition-structure-activity-performance relations and hence, lead the way to knowledge-based predictions.

By doing this, the project also targets the implementation of simplified testing protocols and tools operable by industrial stakeholders, which results can be augmented thanks to the knowledge-based pivotal correlations implemented. To this end, dedicated efforts are made in certifying the interoperability and usability of the project’s advances in the form of harmonized documentation and open science sharing.

Our main tasks

Kinetic modelling.
Large scale modelling.
Machine learning and hybrid modelling.
Strategies relevant to industrial life and innovation requirements.
Integration of modelling to deep learning to achieve “augmented characterisation”.
Interoperability of data and data management plan.
Harmonised workflows.
Data platform, knowledge architectures and open repository for data transfer.
Open science knowledge infrastructure.

https://idener.ai/idener-project/knowskite-x/

Related to what they are hypothesizing about Methane formed in the deep mantle 7-10km+ with heavy pressure from olivine and ringwoodite (spinel oxides):
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https://www.llnl.gov/news/methane-deep-earth-possible-new-source-energy

Ringwoodite

Ringwoodite is a high-pressure phase of Mg2SiO4 (magnesium silicate) formed at high temperatures and pressures of the Earth's mantle between 525 and 660 km (326 and 410 mi) depth. It may also contain iron and hydrogen. It is polymorphous with the olivine phase forsterite (a magnesium iron silicate).

Ringwoodite is notable for being able to contain hydroxide ions (oxygen and hydrogen atoms bound together) within its structure. In this case two hydroxide ions usually take the place of a magnesium ion and two oxide ions.[5]

Combined with evidence of its occurrence deep in the Earth's mantle, this suggests that there is from one to three times the world ocean's equivalent of water in the mantle transition zone from 410 to 660 km deep.[6][7]

This mineral was first identified in the Tenham meteorite in 1969,[8] and is inferred to be present in large quantities in the Earth's mantle.

Olivine, wadsleyite, and ringwoodite are polymorphs found in the upper mantle of the earth. At depths greater than about 660 kilometres (410 mi), other minerals, including some with the perovskite structure, are stable. The properties of these minerals determine many of the properties of the mantle.

https://en.wikipedia.org/wiki/Ringwoodite

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Methane is the most plentiful hydrocarbon in Earth’s crust and is a main component of natural gas. However, oil and gas wells are typically only drilled 5 to 10 kilometers beneath the surface. These depths correspond to pressures of a few thousand atmospheres.

Using a diamond anvil cell, the scientists squeezed materials common at Earth’s surface — iron oxide (FeO), calcite (CaCO3) (the primary component of marble) and water to pressures ranging from 50,000 to 110,000 atmospheres and temperatures more than 2,500 degrees Fahrenheit — to create conditions similar to those found deep within Earth.

Methane (CH4) formed by combining the carbon in calcite with the hydrogen in water. The reaction occurred over a range of temperatures and pressures. Methane production was most favorable at 900 degrees Fahrenheit and 70,000 atmospheres of pressure.

The experiments show that a non-biological source of hydrocarbons may lie in Earth’s mantle and was created from reactions between water and rock — not just from the decomposition of living organisms.

"The results demonstrate that methane readily forms by the reaction of marble with iron-rich minerals and water under conditions typical in Earth’s upper mantle," said Laurence Fried, of November 12, 2007 "This suggests that there may be untapped methane reserves well below Earth’s surface. Our calculations show that methane is thermodynamically stable under conditions typical of Earth’s mantle, indicating that such reserves could potentially exist for millions of years."

The study is published in the Sept. 13-17 early, online edition of the PNAS.

The mantle is a dense, hot layer of semi-solid rock approximately 2,900 kilometers thick. The mantle, which contains more iron, magnesium and calcium than the crust, is hotter and denser because temperature and pressure inside Earth increase with depth. Because of the firestorm-like temperatures and crushing pressure in Earth’s mantle, molecules behave very differently than they do on the surface.

"When we looked at the samples under these pressures and temperatures, they revealed optical changes indicative of methane formation," Fried said. "At temperatures above 2,200 degrees Fahrenheit, we found that the carbon in calcite formed carbon dioxide rather than methane. This implies that methane in the interior of Earth might exist at depths between 100 and 200 kilometers. This has broad implications for the hydrocarbon reserves of the planet and could indicate that methane is more prevalent in the mantle than previously thought. Due to the vast size of Earth’s mantle, hydrocarbon reserves in the mantle could be much larger than reserves currently found in Earth’s crust."

https://www.llnl.gov/news/methane-deep-earth-possible-new-source-energy