Machine learning enables predictive modeling of 2-D materials
Machine learning enables predictive modeling of 2-D materials
by Cr6 Wed Dec 21, 2016 12:37 am
Machine learning enables predictive modeling of 2-D materials
Date:
December 7, 2016
Source:
DOE/Argonne National Laboratory
Summary:
Scientists have used machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a 2-D material made up of a one-atom-thick sheet of tin.
https://www.sciencedaily.com/releases/2016/12/161207184615.htm
The badlands of stanene: Stanene is softer and consequently much more rippled than its cousins graphene and silicene.
Credit: Mathew Cherukara, Badri Narayanan and Subramanian Sankaranarayanan/Argonne National Laboratory
Machine learning, a field focused on training computers to recognize patterns in data and make new predictions, is helping doctors more accurately diagnose diseases and stock analysts forecast the rise and fall of financial markets. And now materials scientists have pioneered another important application for machine learning -- helping to accelerate the discovery and development of new materials.
Researchers at the Center for Nanoscale Materials and the Advanced Photon Source, both U.S. Department of Energy (DOE) Office of Science User Facilities at DOE's Argonne National Laboratory, announced the use of machine learning tools to accurately predict the physical, chemical and mechanical properties of nanomaterials.
In a study published in The Journal of Physical Chemistry Letters, a team of researchers led by Argonne computational scientist Subramanian Sankaranarayanan described their use of machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a two-dimensional (2-D) material made up of a one-atom-thick sheet of tin.
The study reveals for the first time an approach to materials modeling that applies machine learning and is more accurate at predicting material properties compared to past models.
"Predictive modeling is particularly important for newly discovered materials, to learn what they're good for, how they respond to different stimuli and also how to effectively grow the material for commercial applications -- all before you invest in costly manufacturing," said Argonne postdoctoral researcher Mathew Cherukara, one of the lead authors of the study.
Date:
December 7, 2016
Source:
DOE/Argonne National Laboratory
Summary:
Scientists have used machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a 2-D material made up of a one-atom-thick sheet of tin.
https://www.sciencedaily.com/releases/2016/12/161207184615.htm
The badlands of stanene: Stanene is softer and consequently much more rippled than its cousins graphene and silicene.
Credit: Mathew Cherukara, Badri Narayanan and Subramanian Sankaranarayanan/Argonne National Laboratory
Machine learning, a field focused on training computers to recognize patterns in data and make new predictions, is helping doctors more accurately diagnose diseases and stock analysts forecast the rise and fall of financial markets. And now materials scientists have pioneered another important application for machine learning -- helping to accelerate the discovery and development of new materials.
Researchers at the Center for Nanoscale Materials and the Advanced Photon Source, both U.S. Department of Energy (DOE) Office of Science User Facilities at DOE's Argonne National Laboratory, announced the use of machine learning tools to accurately predict the physical, chemical and mechanical properties of nanomaterials.
In a study published in The Journal of Physical Chemistry Letters, a team of researchers led by Argonne computational scientist Subramanian Sankaranarayanan described their use of machine learning tools to create the first atomic-level model that accurately predicts the thermal properties of stanene, a two-dimensional (2-D) material made up of a one-atom-thick sheet of tin.
The study reveals for the first time an approach to materials modeling that applies machine learning and is more accurate at predicting material properties compared to past models.
"Predictive modeling is particularly important for newly discovered materials, to learn what they're good for, how they respond to different stimuli and also how to effectively grow the material for commercial applications -- all before you invest in costly manufacturing," said Argonne postdoctoral researcher Mathew Cherukara, one of the lead authors of the study.
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