Weighted Active Space Protocol for Multireference Machine-Learned Potentials

TL;DR

This paper introduces WASP, a systematic method for consistent active space selection in multireference calculations, enabling the training of machine-learned potentials for complex catalytic reactions with multireference character.

Contribution

The paper presents WASP, a novel active space protocol integrated with active learning to efficiently train machine learning potentials on multireference electronic structure data.

Findings

Successfully applied to TiC+-catalyzed methane activation

Enables accurate modeling of multireference catalytic dynamics

Overcomes active space sensitivity issues in multireference ML training

Abstract

Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application to train machine learning-based interatomic potentials (MLPs) for catalytic dynamics has been challenging due to the sensitivity of multireference calculations to the underlying active space, which complicates achieving consistent energies and gradients across diverse nuclear configurations. To overcome this limitation, we introduce the Weighted Active-Space Protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrate the method on the TiC+-catalyzed C-H activation of methane, a reaction that poses challenges for Kohn-Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a new paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.