SciVerseGym is a new open Gymnasium environment that frames sequential crystal discovery as an MDP with local/global actions, configurable evaluators, and support for RL, Bayesian optimization, and related workflows.
Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery
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Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
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SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery
SciVerseGym is a new open Gymnasium environment that frames sequential crystal discovery as an MDP with local/global actions, configurable evaluators, and support for RL, Bayesian optimization, and related workflows.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.