用强化学习框架统一晶体发现流程，支持化学合理编辑与自动评估。
SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery
- 将晶体设计建模为马尔可夫决策过程，支持元素替换、原子位移等操作。
- 集成机器学习势函数与ASE兼容计算器，实现高效结构评估与反馈。
- 开源可扩展环境，适合强化学习、贝叶斯优化等闭环材料搜索研究者使用。
机器学习的原子间势函数现可实现高效的原子级评估，用于交互式材料发现，但闭环晶体搜索方法仍分散在定制化流程中，涵盖编辑、弛豫、评分、约束和记录等环节。我们提出 SciVerseGym，一个兼容 Gymnasium 的序列晶体发现环境，将晶体设计建模为马尔可夫决策过程。智能体观测原子结构，执行化学合理的编辑操作，并从可配置评估器获取反馈。SciVerseGym 支持局部与全局动作，包括元素替换、晶格扰动、原子位移、空位创建和原子插入，同时支持可配置化学空间、结构池、原子与图基观测、自定义奖励、可选弛豫及稳定性和声子相关诊断。每一步执行编辑，使用机器学习势函数或任何 ASE 兼容计算器评估候选结构，并返回标准 (obs, reward, terminated, truncated, info) 元组。通过解耦智能体逻辑与材料基础设施，SciVerseGym 提供了一个开放、可复现且可扩展的测试平台，适用于强化学习、贝叶斯优化、进化搜索及语言代理工作流在闭环晶体发现中的应用。代码已公开：https://github.com/Bin-Cao/SciVerseGym。
原文摘要 · Abstract (English)
Machine-learned interatomic potentials now enable efficient atomistic evaluation for interactive materials discovery, yet closed-loop crystal search methods remain fragmented across bespoke pipelines for editing, relaxation, scoring, constraints, and bookkeeping. We introduce SciVerseGym, a Gymnasium-compatible environment for sequential crystal discovery that frames crystal design as a Markov decision process. Agents observe an atomistic structure, apply chemically meaningful edits, and receive feedback from a configurable evaluator. SciVerseGym supports local and global actions, including elemental substitution, lattice perturbation, atomic displacement, vacancy creation, and atom insertion, along with configurable chemical spaces, structure pools, atomistic and graph-based observations, custom rewards, optional relaxation, and stability or phonon-related diagnostics. Each step applies an edit, evaluates the candidate using a machine-learned interatomic potential or any ASE-compatible calculator, and returns the standard (obs, reward, terminated, truncated, info) tuple. By decoupling agent logic from materials infrastructure, SciVerseGym provides an open, reproducible, and extensible testbed for reinforcement learning, Bayesian optimization, evolutionary search, and language-agent workflows in closed-loop crystal discovery. Code is available at: https://github.com/Bin-Cao/SciVerseGym.
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