SC1MC-2022: A database of transition metal complexes for training ML models to predict one-site entropies and mutual information

TL;DR

This paper introduces an extended database of transition metal complexes designed to train machine learning models for predicting electronic correlation measures like entropies and mutual information, aiming to reduce computational costs.

Contribution

The paper presents SC1MC-2022, an expanded database including artificial mono transition metal complexes, facilitating transferability of ML models for electronic correlation predictions.

Findings

Database includes reference data for ML training.

Enables estimation of electronic correlation measures.

Aims to reduce computational costs for similar systems.

Abstract

We introduce a new version of the database SC1MC (SC1MC-2022), obtained by extension of the recent SC1MC-2020, which includes artificial mono transition metal complexes. The database involves reference data used as inputs for training of machine learning models, one- and two-site entropies, and mutual information obtained at the DMRG level for canonical and split-localised orbitals. The purpose of this database is to obtain as much as possible information about the electronic correlation structure, which could be exploited by machine learning models to estimate these important information without a significant computational cost for any similar type of systems with some degree of transferability.