Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials

TL;DR

This review discusses the landscape of quantum chemical data sets and databases crucial for training machine learning potentials in computational chemistry, highlighting their characteristics, challenges, and future needs.

Contribution

It provides a comprehensive overview of existing quantum chemical data resources and emphasizes the importance of standardization, accessibility, and sustainability for future development.

Findings

Key data sets vary in chemical diversity and electronic structure methods used.

Challenges include data growth, standardization, and long-term accessibility.

Recommendations focus on developing sustainable, interoperable, and user-friendly data platforms.

Abstract

The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials. In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.