| Autor: |
Aditya Koneru, Rohit Batra, Sukriti Manna, Troy D. Loeffler, Henry Chan, Michael Sternberg, Anthony Avarca, Harpal Singh, Mathew J. Cherukara, Subramanian K. R. S. Sankaranarayanan |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
The Journal of Physical Chemistry Letters. 13:1886-1893 |
| ISSN: |
1948-7185 |
| DOI: |
10.1021/acs.jpclett.1c03551 |
| Popis: |
We introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (α-P) to blue phosphorene (β-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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