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一个大规模的分子Hessian数据库,用于优化反应式机器学习的原子间潜力

Taoyong Cui¹, Yonghong Han¹, Haojun Jia²

¹Deep Principle Inc., Cambridge, MA, 02139, USA.

Scientific data

|December 4, 2025

概括

研究人员开发了HORM,这是一个用于反应系统的量子化学Hessian大数据集. 这使得化学反应建模的机器学习原子间潜力 (MLIP) 更有效,更准确.

科学领域:

计算化学的计算化学
材料科学材料科学材料科学
化学物理化学物理

背景情况:

过渡状态 (TS) 特性对于反应建模至关重要.
传统的DFT方法对于大规模的模拟来说在计算上是昂贵的.
机器学习的原子间潜力 (MLIP) 提供了一个计算上更便宜的替代方案,但往往缺乏用于TS优化的Hessian数据.

研究的目的:

为了介绍HORM,量子化学最大的Hessian对反应系统的数据集.
以使用MLIP来实现准确的TS表征.
提高化学反应建模MLIP的效率和稳定性.

主要方法:

编译了HORM,一个包含184万个量子化学赫森矩阵的数据集,在 ωB97x/6-31G(d) 级别.
开发了Hessian-informed培训与随机行采样,以有效地纳入二次信息.
在各种架构和强力学习方案中评估了MLIP.

主要成果:

HORM显著增强了使用赫塞尼亚数据训练的MLIP.
达到了高达63%的低赫塞尼亚平均绝对误差.
与没有hessians培训的MLIP相比,TS搜索效率提高了多达200倍.

结论:

HORM解决了反应性MLIP开发中的关键数据和方法差距.
能够为化学反应创建更准确,更强大的MLIP.
促进复杂反应网络的可扩展探索.

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