机器学习潜力通过从有机晶体结构预测景观的积极学习.
Patrick W V Butler1, Roohollah Hafizi1, Graeme M Day1
1School of Chemistry, University of Southampton, Southampton SO17 1BJ, U.K.
The journal of physical chemistry. A
|January 26, 2024
概括
通过主动学习训练的机器学习潜能 (MLIP) 提高有机晶体结构预测 (CSP) 的效率. 这种方法提高了能量排名的准确性,减少了对昂贵的密度函数理论 (DFT) 计算的依赖.
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.
- 晶体学 晶体学是指结晶学.
背景情况:
- 对潜在的有机晶体结构进行精确的能量排名至关重要,但计算成本昂贵.
- 高密度函数理论 (DFT) 方法是准确的,但对于探索大型晶体能量景观来说太慢了.
- 经验力场提供速度,但缺乏可靠结构排名所需的准确性.
研究的目的:
- 调查积极学习用于训练机器学习的原子间潜力 (MLIPs) 进行有机晶体结构预测 (CSP).
- 开发一种高度自动化的工作流程,将主动学习与CSP采样方法相结合.
- 为了提高排名晶体结构能量的效率和准确性.
主要方法:
- 积极学习策略被用来训练MLIP使用CSP数据集.
- 使用了层次的方法,从力场开始,到MLIPs.
- 在蒙特卡洛模拟中进行飞行训练,用于模拟超出能源最小值的结构.
主要成果:
- 通过积极学习进行训练的开发的MLIP在重新排名大型,多样化的晶体结构景观时实现了接近DFT的准确性.
- 自动化工作流程有效地探索了各种各样的晶体包装空间.
- MLIP显著减少了对昂贵的DFT计算的需求,提高了计算效率.
结论:
- 经过积极学习培训的MLIP为有机CSP提供了一个计算效率高,准确的替代方案.
- 这种方法提高了在晶体结构预测中的能量排名的可靠性.
- 这种方法可以扩展到远离格子能量最小值的建模结构.
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