Force-free identification of minimum-energy pathways and transition states for stochastic electronic structure theories

TL;DR

This paper introduces a force-free, stochastic approach for identifying minimum-energy pathways and transition states in electronic structure calculations, enabling high-accuracy PES mapping without force computations.

Contribution

It develops a modified surrogate Hessian method for stochastic theories, allowing efficient MEP and TS identification without force calculations, validated with QMC and DFT comparisons.

Findings

Successfully identified MEPs and TSs using force-free QMC methods.

Validated results against DFT and coupled cluster NEB calculations.

Proposed a hybrid DFT-QMC scheme for improved thermodynamic and kinetic predictions.

Abstract

Stochastic electronic structure theories, e.g., Quantum Monte Carlo methods, enable highly accurate total energy calculations which in principle can be used to construct highly accurate potential energy surfaces. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged-elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method - previously developed for QMC structural optimization - to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and on saddle points, we show that it is possible to identify MEPs and TSs using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia molecule and an SN2 reaction. We validate our results using Density Functional Theory- and coupled cluster-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities - free energy differences, rate constants, and equilibrium constants - that incorporates stochastically-optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations, at significantly reduced computational expense.