Machine learning intermolecular transfer integrals with compact atomic cluster representations

TL;DR

This paper develops a machine learning approach using atomic cluster expansion with symmetries to efficiently predict intermolecular charge transfer integrals in organic semiconductors, reducing computational costs.

Contribution

It extends the Atomic Cluster Expansion to model transfer integrals with symmetries, enabling data-efficient predictions for organic semiconductor molecules.

Findings

Effective transfer integral predictions for conjugated semiconductors

Demonstrates data efficiency with symmetry-aware models

Applicable to molecules like ethylene, thiophene, naphthalene

Abstract

Calculating intermolecular charge transfer integrals in organic semiconductors requires substantial computer resource for each individual calculation. We might alternatively construct a machine learning model for transfer integrals, which model the full six-degrees of freedom for the relative position of dimer pairs, trained on representative calculations for the molecules of interest. Recent developments have produced effective machine learning force fields, which model the total energy of atomic assemblies. We extend the Atomic Cluster Expansion (ACE) with the correct symmetries for transfer (kinetic-energy) integrals. Combined with a spherical harmonic basis makes, this forms a strong inductive bias and makes for a data efficient model. We introduce coarse-grained and heavy-atom representations, and assess the methodology on representative conjugated semiconductors: ethylene, thiophene, and naphthalene.