Reinforcement learning for in silico determination of adsorbate—substrate structures

• Published in: Journal of Computational Chemistry
• Main Authors: Lourenço, Maicon Pierre, Hostaš, Jiří, Bellinger, Colin, Tchagang, Alain, Salahub, Dennis R.
• Other Authors: Fundação de Amparo à Pesquisa e Inovação do Espírito Santo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Natural Sciences and Engineering Research Council of Canada
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2024
• Subjects: Orca
• Online Access: http://dx.doi.org/10.1002/jcc.27322; https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.27322

Abstract Reinforcement learning (RL) methods have helped to define the state of the art in the field of modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the discovery of novel algorithms. In this work, we present a RL method, based on Q‐learning, for the structural determination of adsorbate@substrate models in silico, where the minimization of the energy landscape resulting from adsorbate interactions with a substrate is made by actions on states (translations and rotations) chosen from an agent's policy. The proposed RL method is implemented in an early version of the reinforcement learning software for materials design and discovery (RLMaterial), developed in Python3.x. RLMaterial interfaces with deMon2k, DFTB+, ORCA, and Quantum Espresso codes to compute the adsorbate@substrate energies. The RL method was applied for the structural determination of (i) the amino acid glycine and (ii) 2‐amino‐acetaldehyde, both interacting with a boron nitride (BN) monolayer, (iii) host‐guest interactions between phenylboronic acid and β ‐cyclodextrin and (iv) ammonia on naphthalene. Density functional tight binding calculations were used to build the complex search surfaces with a reasonably low computational cost for systems (i)–(iii) and DFT for system (iv). Artificial neural network and gradient boosting regression techniques were employed to approximate the Q‐matrix or Q‐table for better decision making (policy) on next actions. Finally, we have developed a transfer‐learning protocol within the RL framework that allows learning from one chemical system and transferring the experience to another, as well as from different DFT or DFTB levels.