强化学习用于在基中确定吸附物-基质结构.
Maicon Pierre Lourenço1, Jiří Hostaš2,3, Colin Bellinger3
1Departamento de Química e Física-Centro de Ciências Exatas, Naturais e da Saúde-CCENS-Universidade Federal do Espírito Santo, Alegre, Brasil.
Journal of computational chemistry
|February 15, 2024
概括
本研究引入了一种强化学习 (RL) 方法,用于确定adsorbate@substrate结构. 该方法使用Q学习和人工智能来有效地探索和预测材料科学中的稳定配置.
科学领域:
- 计算化学是一种计算化学.
- 材料科学 是一种材料科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 强化学习 (RL) 在人工智能方面取得了最先进的结果,特别是在像AlphaGo.Go这样的突破之后.
- 确定吸附剂在基板上的稳定结构对于理解表面相互作用和设计新材料至关重要.
- 传统的结构确定方法在计算上可能昂贵且耗时.
研究的目的:
- 介绍一种基于RL的新方法,用于在基中测定adsorbate@substrate模型的结构.
- 在材料设计的RLMaterial软件中开发和实施RL方法.
- 证明该方法在各种化学系统中的适用性.
主要方法:
- 基于Q学习的强化学习方法被用于导航adsorbate@substrate相互作用的能量格局.
- RL代理执行动作 (转换,旋转) 以最大限度地减少能量,以学习政策为指导.
- 材料软件接口与计算化学代码 (deMon2k,DFTB+,ORCA,量子埃斯普雷索) 进行能量计算.
- 人工神经网络和梯度增强回归被用来近似Q矩阵,以实现高效的决策.
- 密度功能紧密结合 (DFTB) 和密度功能理论 (DFT) 用于能源计算.
主要成果:
- 通过RL方法,成功地确定了化单层上的甘氨酸和2-氨基甲基的结构.
- 它还阐明了乙烯酸和β-环氧化之间的宿主-客人相互作用,以及纳夫他林上的氨.
- 使用机器学习技术 (ANN,梯度提升) 提高了Q矩阵近似的效率.
- 成功开发了一个转移学习协议,使不同化学系统和计算层次之间的知识转移成为可能.
结论:
- 开发的RL方法提供了一种高效和有效的方法来确定adsorbate@substrate系统的结构.
- RLMaterial提供了一个多功能平台,通过人工智能驱动的模拟加速材料发现.
- 转移学习能力提高了RL在计算化学中的适应性和预测能力.
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