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

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A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.

Taoyong Cui¹, Yonghong Han¹, Haojun Jia²

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

Scientific Data

|December 4, 2025

Summary

This summary is machine-generated.

Researchers developed HORM, a large dataset of quantum chemistry Hessians for reactive systems. This enables more efficient and accurate machine-learning interatomic potentials (MLIPs) for chemical reaction modeling.

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Area of Science:

Computational Chemistry
Materials Science
Chemical Physics

Background:

Transition-state (TS) characterization is crucial for reaction modeling.
Conventional DFT methods are computationally expensive for large-scale simulations.
Machine-learning interatomic potentials (MLIPs) offer a computationally cheaper alternative but often lack Hessian data for TS optimization.

Purpose of the Study:

To introduce HORM, the largest quantum chemistry Hessian dataset for reactive systems.
To enable accurate TS characterization using MLIPs.
To improve the efficiency and robustness of MLIPs for chemical reaction modeling.

Main Methods:

Compiled HORM, a dataset of 1.84 million quantum-chemistry Hessian matrices at the ωB97x/6-31G(d) level.
Developed Hessian-informed training with stochastic row sampling to efficiently incorporate second-order information.
Evaluated MLIPs across diverse architectures and force-learning schemes.

Main Results:

HORM significantly enhances MLIPs trained with Hessian data.
Achieved up to 63% lower Hessian mean absolute error.
Demonstrated up to 200x improvement in TS-search efficiency compared to MLIPs trained without Hessians.

Conclusions:

HORM addresses critical data and methodological gaps in reactive MLIP development.
Enables the creation of more accurate and robust MLIPs for chemical reactions.
Facilitates scalable exploration of complex reaction networks.