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Molecular Models

Molecular Models

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Updated: Jan 9, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

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Published on: April 12, 2019

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

此摘要是机器生成的。

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.

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Last Updated: Jan 9, 2026

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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.