Reinforcement Learning for Adsorbate–Substrate Modeling in Silico – the Genesis of RLMaterial software

Vol 2, 2023 - 166229
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Abstract

Modeling functional materials requires understanding the substrate@adsorbate interactions. In this work, a reinforcement learning method for adsorbate@substrate structural determination was developed and implemented in the RLMaterial code. The software was used to study the glycine interactions with boron nitride (BN) and phenylboronic acid in cyclodextrin.

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Institutions
  • 1 Universidade Federal do Espírito Santo, Brazil
  • 2 University of Calgary
  • 3 National Research Council of Canada
Track
  • Development and/or implementation of Method/Theory
Keywords
Reinforcement Learning; chemical adsorption; host-guest chemistry; DFTB