Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

TL;DR

The paper introduces Alchemy, a large and diverse quantum chemistry dataset of organic molecules, to facilitate benchmarking and development of machine learning models in chemistry and material science.

Contribution

It provides a new extensive dataset with quantum properties for organic molecules, expanding existing resources and enabling better model validation and development.

Findings

State-of-the-art graph neural networks perform well on Alchemy

The dataset enhances validation of ML models in chemistry

Benchmark results demonstrate dataset's usefulness

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.