ArticleScientific data2023
MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.Science advances · 2026Article
- Optimizing cross-domain transfer for universal machine learning interatomic potentials.Nature communications · 2026Article
- Machine-learned density functional based quantum chemical computations for ethane: performance of DeepMind 21 on potential energy surface and molecular properties.Journal of molecular modeling · 2025Article
- Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br.Scientific data · 2025Article
- Deep Learning-Driven Intelligent Fluorescent Probes: Advancements in Molecular Design for Accurate Food Safety Detection.Foods (Basel, Switzerland) · 2025Review
- Dataset exploring the atomic scale structure and ionic dynamics of polyanion sodium cathode materials.Scientific data · 2025Article
- QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules.Scientific data · 2025Article
- QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information.Scientific data · 2024Article
- MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods.Scientific data · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Well curated extensive datasets have helped spur intense molecular machine learning (ML) method development activities over the last few years, encouraging nonchemists to be part of the effort as well. QM9 dataset is one of the benchmark databases for small molecules with molecular energies based on B3LYP functional. G4MP2 based energies of these molecules were published later. To enable a wide variety of ML tasks like transfer learning, delta learning, multitask learning, etc. with QM9 molecules, in this article, we introduce a new dataset with QM9 molecule energies estimated with 76 different DFT functionals and three different basis sets (228 energy numbers for each molecule). We additionally enumerated all possible A ↔ B monomolecular interconversions within the QM9 dataset and provided the reaction energies based on these 76 functionals, and basis sets. Lastly, we also provide the bond changes for all the 162 million reactions with the dataset to enable structure- and bond-based reaction energy prediction tools based on ML.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.