# CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning

arXiv: https://arxiv.org/abs/2402.13221 · HTML: https://mlsanity.com/paper/2402.13221/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 97 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields](https://mlsanity.com/paper/2206.07697/) (2022)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [Crystal Diffusion Variational Autoencoder for Periodic Material Generation](https://mlsanity.com/paper/2110.06197/) (2021)
- [The Open Catalyst 2020 (OC20) Dataset and Community Challenges](https://mlsanity.com/paper/2010.09990/) (2020)
- [Global Self-Attention as a Replacement for Graph Convolution](https://mlsanity.com/paper/2108.03348/) (2021)
- [The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts](https://mlsanity.com/paper/2206.08917/) (2022)
- [GraphEBM: Molecular Graph Generation with Energy-Based Models](https://mlsanity.com/paper/2102.00546/) (2021)
- [Predicting materials properties without crystal structure: Deep representation learning from stoichiometry](https://mlsanity.com/paper/1910.00617/) (2019)
- [CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials](https://mlsanity.com/paper/2301.05852/) (2023)
- [MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling](https://mlsanity.com/paper/2309.05934/) (2023)
- [IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery](https://mlsanity.com/paper/1907.03222/) (2019)
- [Remember to correct the bias when using deep learning for regression!](https://mlsanity.com/paper/2203.16470/) (2022)
- [IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation](https://mlsanity.com/paper/2307.06698/) (2023)

## Built on by (most cited first, 5 of 5)

- [UniIF: Unified Molecule Inverse Folding](https://mlsanity.com/paper/2405.18968/) (2024)
- [deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models](https://mlsanity.com/paper/2502.02189/) (2025)
- [Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework](https://mlsanity.com/paper/2506.00302/) (2025)
- [Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials](https://mlsanity.com/paper/2512.19494/) (2025)
- [ED-CSP: Crystal Structure Prediction from Electron Diffraction](https://mlsanity.com/paper/2608.06448/) (2026)

## Abstract (arXiv)

Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to address. Moving to inorganic nanomaterials increases complexity as the scale of number of nodes within each graph can be broad (10 to 105). The bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. However, the most exciting applications of graph ML will be in their generative capabilities, which is currently not at par with other domains such as images or text. We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). We define 11 property prediction tasks and 6 structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods and use these benchmarking results to highlight areas which need future work. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale -- both on the individual graph level and of the dataset as a whole -- and the only nanomaterials datasets with high structural and elemental diversity.