About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
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Presentation Title |
Neural Network Reactive Force Field for C, H, N, O Systems |
Author(s) |
Pilsun Yoo, Michael Sakano, Saaketh Desai, Mahbubul Islam, Peilin Liao, Alejandro Strachan |
On-Site Speaker (Planned) |
Pilsun Yoo |
Abstract Scope |
Reactive force fields with physics-based functions parametrized from first-principles data have been in active development and use for nearly two decades in a wide range of phenomena including chemistry at extreme conditions to operations of electrochemical devices and catalysis. These methods have provided invaluable insights and semi-quantitative understanding. However, reactive force fields result in inaccurate quantitative predictions due to intrinsic limitations of functional forms. Machine learning force fields try to address this shortcoming using physics-agnostic but flexible models trained with extensive data. We developed a neural network reactive force field(NNRF) for C,H,N,O systems to study chemical reactions of high energy nitramine 1,3,5-Trinitroperhydro-1,3,5-triazine(RDX). Training data was collected using a semi-automated iterative procedure to include relevant chemical reaction paths until NNRF predicts energy and forces in a desired accuracy. The predictions of NNRF for vibrational properties, and kinetics of thermal decomposition were quantitatively closer to experimental data than available reactive force fields. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Modeling and Simulation, |