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Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in

Aditya Koneru^1,2, Rohit Batra², Sukriti Manna^1,2

¹Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States.

The Journal of Physical Chemistry Letters

|February 17, 2022

Summary

This summary is machine-generated.

We developed a multi-reward reinforcement learning (RL) method to create a flexible bond-order potential (BOP) for 2D phosphorene. This advanced model accurately predicts material properties and phase transitions, aiding in materials design.

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Area of Science:

Computational Materials Science
Machine Learning in Materials
Condensed Matter Physics

Background:

Developing accurate interatomic potentials is crucial for simulating materials.
Traditional potentials often struggle with complex bonding environments and phase transitions.
Ab initio data offers high accuracy but is computationally expensive for large-scale simulations.

Purpose of the Study:

To introduce a novel multi-reward reinforcement learning (RL) approach for training flexible bond-order potentials (BOPs).
To apply this method to 2D phosphorene, a material with diverse polymorphs and potential applications.
To enable efficient and accurate prediction of structural, energetic, and dynamical properties for materials design.

Main Methods:

Utilized a continuous action space Monte Carlo tree search algorithm for RL.
Employed a multiobjective optimization scheme for high-dimensional materials design.
Trained a BOP model using ab initio data for 2D phosphorene polymorphs.
Performed molecular dynamics simulations to study temperature and strain rate effects on phase transitions.

Main Results:

The RL-trained BOP model accurately captured various properties of 2D phosphorene polymorphs, including structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions.
The model successfully simulated the phase transition from black (α-P) to blue phosphorene (β-P).
Observed a decrease in critical strain for the α-P to β-P transition with increasing temperature.

Conclusions:

The developed RL-based BOP framework is a general, scalable, and efficient approach for materials design.
The model provides accurate predictions for 2D phosphorene, facilitating further research into its properties and applications.
The study elucidated atomistic mechanisms influencing temperature-dependent phase transitions in 2D phosphorene.