Papers › Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
Arif Ullah, Yuxinxin Chen, Pavlo O. Dral
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections