# Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

arXiv: https://arxiv.org/abs/1906.09427 · HTML: https://mlsanity.com/paper/1906.09427/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2019.

## Built on by (most cited first, 30 of 36)

- [Sign and Basis Invariant Networks for Spectral Graph Representation Learning](https://mlsanity.com/paper/2202.13013/) (2022)
- [Reconstruction for Powerful Graph Representations](https://mlsanity.com/paper/2110.00577/) (2021)
- [Does GNN Pretraining Help Molecular Representation?](https://mlsanity.com/paper/2207.06010/) (2022)
- [ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations](https://mlsanity.com/paper/2103.01436/) (2021)
- [Ordered Subgraph Aggregation Networks](https://mlsanity.com/paper/2206.11168/) (2022)
- [Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness](https://mlsanity.com/paper/2401.08514/) (2024)
- [Equivariant Polynomials for Graph Neural Networks](https://mlsanity.com/paper/2302.11556/) (2023)
- [SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks](https://mlsanity.com/paper/2203.13913/) (2022)
- [On the Stability of Expressive Positional Encodings for Graphs](https://mlsanity.com/paper/2310.02579/) (2023)
- [Probabilistically Rewired Message-Passing Neural Networks](https://mlsanity.com/paper/2310.02156/) (2023)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Efficient Subgraph GNNs by Learning Effective Selection Policies](https://mlsanity.com/paper/2310.20082/) (2023)
- [Factor Graph Neural Networks](https://mlsanity.com/paper/2308.00887/) (2023)
- [Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products](https://mlsanity.com/paper/2402.08450/) (2024)
- [On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers](https://mlsanity.com/paper/2404.03380/) (2024)
- [TextAtari: 100K Frames Game Playing with Language Agents](https://mlsanity.com/paper/2506.04098/) (2025)
- [Bridging Theory and Practice in Link Representation with Graph Neural Networks](https://mlsanity.com/paper/2506.24018/) (2025)
- [PF-GNN: Differentiable particle filtering based approximation of universal graph representations](https://mlsanity.com/paper/2401.17752/) (2024)
- [Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression](https://mlsanity.com/paper/2209.13410/) (2022)
- [Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors](https://mlsanity.com/paper/2410.09737/) (2024)
- [LEAP: Local ECT-Based Learnable Positional Encodings for Graphs](https://mlsanity.com/paper/2510.00757/) (2025)
- [Graph2Graph Learning with Conditional Autoregressive Models](https://mlsanity.com/paper/2106.03236/) (2021)
- [Machine Learning for Screening Large Organic Molecules](https://mlsanity.com/paper/2211.15415/) (2022)
- [Learning Generalized Medical Image Representations through Image-Graph Contrastive Pretraining](https://mlsanity.com/paper/2405.09594/) (2024)
- [Instructional Text Across Disciplines: A Survey of Representations, Downstream Tasks, and Open Challenges Toward Capable AI Agents](https://mlsanity.com/paper/2410.18529/) (2024)
- [Positional Encoding meets Persistent Homology on Graphs](https://mlsanity.com/paper/2506.05814/) (2025)
- [Path-Based Gradient Boosting for Graph-Level Prediction](https://mlsanity.com/paper/2605.08102/) (2026)
- [SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation](https://mlsanity.com/paper/2111.05936/) (2021)
- [Bayesian Deep Learning for Graphs](https://mlsanity.com/paper/2202.12348/) (2022)
- [Generated Graph Detection](https://mlsanity.com/paper/2306.07758/) (2023)

## Abstract (arXiv)

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 footnotehttps://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.