Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

Abstract

We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of 12 quantum mechanical properties of 119,487 organic molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The Alchemy dataset expands the volume and diversity of existing molecular datasets. Our extensive benchmarks of the state-of-the-art graph neural network models on Alchemy clearly manifest the usefulness of new data in validating and developing machine learning models for chemistry and material science. We further launch a contest to attract attentions from researchers in the related fields. More details can be found on the contest website \footnote{https://alchemy.tencent.com}. At the time of benchamrking experiment, we have generated 119,487 molecules in our Alchemy dataset. More molecular samples are generated since then. Hence, we provide a list of molecules used in the reported benchmarks.

Authors are listed in alphabetical order

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• Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
• Graph Neural Network Architecture Search for Molecular Property Prediction
• On the equivalence of molecular graph convolution and molecular wave function with poor basis set
• Physical machine learning outperforms "human learning" in Quantum Chemistry
• Reconstruction for Powerful Graph Representations
• Ab initio machine learning in chemical compound space
• Graph Mixture Density Networks
• Neuralizing Efficient Higher-order Belief Propagation
• SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation
• A Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse ansatz via Machine Learning and Cheminformatics
• Graph2Graph Learning with Conditional Autoregressive Models