O1NumHess: a fast and accurate seminumerical Hessian algorithm using only O(1) gradients

TL;DR

O1NumHess introduces a novel seminumerical Hessian algorithm that computes the Hessian matrix with only O(1) gradients by exploiting low-rank properties, significantly reducing computational cost while maintaining accuracy.

Contribution

The paper presents a new Hessian calculation method that reduces gradient evaluations to O(1) by leveraging off-diagonal low-rank structures, outperforming traditional approaches.

Findings

O1NumHess achieves comparable accuracy to conventional methods.

It is faster than both traditional numerical and analytic Hessians.

Requires only about 100 gradients for large systems.

Abstract

In this work, we describe a new algorithm, O1NumHess, to calculate the Hessian of a molecular system by finite differentiation of gradients calculated at displaced geometries. Different from the conventional seminumerical Hessian algorithm, which requires gradients at $O(N_{\mathrm{atom}})$ displaced geometries (where $N_{\mathrm{atom}}$ is the number of atoms), the present approach only requires $O(1)$ gradients. Key to the reduction of the number of gradients is the exploitation of the off-diagonal low-rank (ODLR) property of Hessians, namely the blocks of the Hessian that correspond to two distant groups of atoms have low rank. This property reduces the number of independent entries of the Hessian from $O(N_{\mathrm{atom}}^2)$ to $O(N_{\mathrm{atom}})$, such that $O(1)$ gradients already contain enough information to uniquely determine the Hessian. Numerical results on model systems (long alkanes and polyenes), transition metal reactions (WCCR10) and non-covalent complexes (S30L-CI) using the BDF program show that O1NumHess gives frequency, zero-point energy, enthalpy and Gibbs free energy errors that are only about two times those of conventional double-sided seminumerical Hessians. Moreover, O1NumHess is always faster than the conventional numerical Hessian algorithm, frequently even faster than the analytic Hessian, and requires only about 100 gradients for sufficiently large systems. An open-source implementation of this method, which can also be applied to problems irrelevant to computational chemistry, is available on GitHub.