A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Banik, Suvo; Loefller, Troy; Manna, Sukriti; Chan, Henry; Srinivasan, Srilok; Darancet, Pierre; Hexemer, Alexander; Sankaranarayanan, Subramanian K. R. S.

doi:10.1038/s41524-023-01128-y

Title: A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Journal Article · 29 September 2023 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-023-01128-y · OSTI ID:2007113

Banik, Suvo; Loefller, Troy;

;

; Darancet, Pierre;

;

Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: Air Force Office of Scientific Research (AFOSR); US Air Force Office of Scientific Research (AFOSR); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231; AC02-06CH11357

OSTI ID:: 2007113

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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