化学中的深度强化学习:一篇回顾
Bhuvanesh Sridharan1, Animesh Sinha1, Jai Bardhan1
1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India.
Journal of computational chemistry
|May 3, 2024
概括
强化学习 (RL) 正在为分子生成和优化等任务彻底改变计算化学. 这篇评论探讨了RL的应用,算法和化学的未来方向.
科学领域:
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 强化学习 (RL) 在各种计算化学领域都取得了重大成功.
- 关键的应用领域包括分子生成,几何优化和回合成路径搜索.
研究的目的:
- 激励RL在化学中的应用.
- 为化学问题提供RL形式主义的基本理解.
- 审查最近的进展,并激发新的RL应用.
主要方法:
- 讨论与化学相关的RL形式主义.
- 从最近的文献中分析解决方案配方和算法.
- 不同方法及其基础RL算法的比较.
主要成果:
- 概述RL在化学中的应用现状.
- 识别积极研究的开放问题.
- 对利用RL应对化学挑战的见解.
结论:
- RL是推进计算化学的强大工具.
- 了解RL原则使化学家能够解决复杂的问题.
- 该审查旨在促进RL驱动的化学研究中的创新.
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