Conference Tools for 2021 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	Accelerating High Throughput Materials Simulation Studies Using Machine Learning Based Application Programming Interface (API)
Author(s)	Jason Gibson, Stephen Xie, Richard Hennig
On-Site Speaker (Planned)	Jason Gibson
Abstract Scope	Materialsweb.org is an online database of density functional theory (DFT) calculations emphasizing 2D materials with thousands of electronic structure calculations and multiple GASP runs of select systems. We present an API that utilizes this data to facilitates the computation of various, proven ML representations, including Smooth Overlap of Atomic Positions and symmetry functions, which require only the structure of the material, or ML descriptors, such as MAGPIE, which requires only chemical composition. These descriptors can then serve as inputs to the pre-trained ML models that utilize neural networks, random forests, and kernel ridge regression to predict potential energy surfaces and scalar properties such as formation energy and band gaps. A structure search with GASP produces thousands of configurations for a particular system and, in turn, thousands of data points. This data is used to train the ML models allowing accurate predictions utilizing only structural information of select systems. The data from the electronic structure contain a diverse set of materials systems that allow predictions of a variety of materials using only information about the chemical composition. All software will be freely available under the open-source Apache License 2.0.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Modeling and Simulation,

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