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

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

npj Computational Materials  9: 177 (2023).

Editorial Summary: Artificial Intelligence Enables Reverse Design of Materials

Efficient crystal structure prediction (CSP) is a key challenge in materials science, which involves finding structure-property relationships for substable crystalline polymorphs in a complex configuration space. With the application of AI and machine learning techniques, especially reinforcement learning (RL), the optimization process in high-dimensional search spaces has been able to improve efficiency and accuracy, driving a new paradigm in materials design and discovery. These methods not only accelerate the discovery of globally optimal solutions, but also help to explore and utilize local minima, providing broader possibilities for materials innovation. A team lead by Prof. Subramanian K. R. S. Sankaranarayanan from Center for Nanoscale Materials, Argonne National Laboratory, introduced CASTING which is a workflow that implements a continuous action space tree-based RL search algorithm for CSP in a high-dimensional search space. The authors discuss the important algorithmic modifications that are needed in the MCTS to successfully apply it to continuous search space inverse problems associated with structure and topology predictions. To showcase the efficacy of the CASTING framework, the authors apply CASTING to a wide range of representative systems—single-component metallic systems such as Ag and Au, covalent systems such as C, binary systems such as h-BN and C-H, and multicomponent perovskite systems such as doped NNO. Additionally, the authors perform the inverse design of super-hard carbon phases using multi-objective optimization. The authors demonstrate the scalability, accuracy of sampling, and speed of convergence of CASTING on complex material science problems. The authors discuss the impact of the various RL hyperparameters on search performance. CASTING is also deployed to sample stable and metastable polymorphs across systems with dimensionality ranging from 3D (bulk) to low dimensional systems such as 0D (clusters) and 2D (sheets). Comparisons to other metaheuristic search algorithms such as genetic algorithms, basin hopping, and random sampling are also shown—the MCTS is demonstrated to have a superior performance in terms of the solution quality and the speed of convergence. The authors expect MCTS to perform well, especially for complex search landscape with multiple objectives, multiple species, and multi-dimensional systems. Overall, the authors successfully demonstrate the development and application of an RL techniques such as MCTS for inverse materials design and discovery problems related to structure and topology predictions.