在AI时代的化工实验:从量子化学到机器学习,再回
Abdulrahman Aldossary1, Jorge Arturo Campos-Gonzalez-Angulo1, Sergio Pablo-García1,2
1Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada.
Advanced materials (Deerfield Beach, Fla.)
|May 25, 2024
概括
人工智能 (AI) 和机器学习 (ML) 正通过加速分子性质预测来改变计算化学. 这篇评论探讨了AI/ML.
科学领域:
- 计算化学计算化学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的计算化学方法与解决分子施罗丁格方程的复杂性和成本作斗争.
- 分子系统的规模不断增加,加剧了计算方面的挑战.
- 人工智能 (AI) 和机器学习 (ML) 为in silico实验提供了有希望的替代方案.
研究的目的:
- 审查ML在计算化学中的演变和整合,用于能源和财产预测.
- 要突出从数据驱动模型到结合物理规律的AI的旅程.
- 概述化学人工智能的未来研究方向和创新.
主要方法:
- 审查现有的计算化学方法.
- 对各种机器学习模型的分析,应用于化学预测.
- 探索AI/ML与量子力学原理的整合.
主要成果:
- 人工智能和机器学习显著提高了探索化学空间的可扩展性和速度.
- 当前的ML模型在可重复性和可转移性方面面临挑战.
- 正在向学习或结合量子力学规律的AI模型取得进展.
结论:
- 人工智能和机器学习正在改变化学和材料科学中的体实验.
- 最终的目标是开发能够对施罗丁格方程提供准确和可转移的解决方案的AI.
- 需要进一步的研究来提高AI模型在化学中的强度和物理基础.
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