计算化学的生成AI:预测新兴现象的路线图
Pratyush Tiwary1,2, Lukas Herron2,3, Richard John4
1Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742.
概括
生成型人工智能 (AI) 为分子采样和模拟提供了新的计算化学工具. 未来的人工智能模型必须整合化学原理,以准确预测新型现象.
科学领域:
- 计算化学的计算化学
- 人工智能的人工智能
背景情况:
- 生成型人工智能正在迅速发展,在计算化学方面提供了潜在的突破.
- 当前的人工智能方法在分子结构采样,力场开发和模拟加速方面表现有前途.
研究的目的:
- 为计算化学中生成性AI方法提供结构化的概述.
- 突出AI在预测化学现象中的应用和挑战.
主要方法:
- 综述生成AI和计算化学的基本概念.
- 讨论广泛使用的AI方法:自动编码器,生成对抗网络,强化学习,流模型和语言模型.
主要成果:
- 生成性人工智能已经在分子采样,力场开发和加速模拟方面取得了进展.
- 选择的应用包括力场开发和蛋白质/RNA结构预测.
结论:
- 生成型人工智能面临着成为真正预测性的挑战,特别是对于新出现的化学现象.
- 未来的人工智能模型需要整合核心化学原理,特别是统计力学,以满足预测标准.
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