Fig. 1 Schematic illustration of the nature of the search space (discrete vs. continuous) in materials applications.
阿貢國(guó)家實(shí)驗(yàn)室納米材料中心的Subramanian K. R. S. Sankaranarayanan教授及其團(tuán)隊(duì)開(kāi)發(fā)的CASTING，是一個(gè)針對(duì)高維搜索空間內(nèi)約束滿足問(wèn)題（CSP）的工作流程，它采用了基于連續(xù)動(dòng)作空間樹(shù)的強(qiáng)化學(xué)習(xí)（RL）搜索算法。
Fig. 2 MCTS working as crystal structure optimizer.
Fig. 8 Convergence with size-dependent diversity in nanoclusters of Gold (Au).
Editorial Summary
To reverse design of materials??Please ask the AIEfficient 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.?Fig. 9 Exploring 2D polymorphs with CASTING.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.?Fig. 10 Comparison of the performance of CASTING with commonly used optimizers in crystal structure prediction.?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.?Fig. 11 Exploration of the configurational space of hydrogen doped Neodymium Nickel Oxide (NNO) with CASTING framework.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.?Fig. 12 Inverse design of super hard phases of Carbon (C).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.?This article was recently published in npj Computational Materials9: 177 (2023).
原文Abstract及其翻譯
A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery（連續(xù)動(dòng)作空間樹(shù)搜索用于材料發(fā)現(xiàn)的逆向設(shè)計(jì)（CASTING）框架）
Suvo Banik,?Troy Loefller,?Sukriti Manna,?Henry Chan,?Srilok Srinivasan,?Pierre Darancet,?Alexander Hexemer?&?Subramanian K. R. S. Sankaranarayanan?
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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