Learning with Delayed Rewards-A Case Study on Inverse Defect Design in 2D Materials.

Autor: Banik S; Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.; Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States., Loeffler TD; Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.; Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States., Batra R; Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States., Singh H; Research and Development, Sentient Science Corporation, West Lafayette, Indiana 47906, United States., Cherukara MJ; Advanced Photon Source, Argonne National Laboratory, Lemont, Illinois 60439, United States., Sankaranarayanan SKRS; Center for Nanoscale Materials, Argonne National Laboratory, Lemont, Illinois 60439, United States.; Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States.
Jazyk: angličtina
Zdroj: ACS applied materials & interfaces [ACS Appl Mater Interfaces] 2021 Aug 04; Vol. 13 (30), pp. 36455-36464. Date of Electronic Publication: 2021 Jul 21.
DOI: 10.1021/acsami.1c07545
Abstrakt: Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. Overall, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.
Databáze: MEDLINE
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