# Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules

arXiv: https://arxiv.org/abs/2502.05970 · HTML: https://mlsanity.com/paper/2502.05970/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Crystal Diffusion Variational Autoencoder for Periodic Material Generation](https://mlsanity.com/paper/2110.06197/) (2021)
- [MatterGen: a generative model for inorganic materials design](https://mlsanity.com/paper/2312.03687/) (2023)
- [Predicting materials properties without crystal structure: Deep representation learning from stoichiometry](https://mlsanity.com/paper/1910.00617/) (2019)
- [Scalable Diffusion for Materials Generation](https://mlsanity.com/paper/2311.09235/) (2023)
- [Learning to Extrapolate: A Transductive Approach](https://mlsanity.com/paper/2304.14329/) (2023)
- [Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study](https://mlsanity.com/paper/2401.08032/) (2024)
- [Interpretable models for extrapolation in scientific machine learning](https://mlsanity.com/paper/2212.10283/) (2022)
- [A General Approach for Determining Applicability Domain of Machine Learning Models](https://mlsanity.com/paper/2406.05143/) (2024)
- [Advancing Extrapolative Predictions of Material Properties through Learning to Learn](https://mlsanity.com/paper/2404.08657/) (2024)

## Built on by (most cited first, 7 of 7)

- [BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models](https://mlsanity.com/paper/2505.01912/) (2025)
- [Geometric Mixture Models for Electrolyte Conductivity Prediction](https://mlsanity.com/paper/2510.15403/) (2025)
- [Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces](https://mlsanity.com/paper/2512.10989/) (2025)
- [Vendi Novelty Scores for Out-of-Distribution Detection](https://mlsanity.com/paper/2602.10062/) (2026)
- [From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry](https://mlsanity.com/paper/2605.03205/) (2026)
- [AutoMatBench: An Automatic Optimization Toolkit for the Acceleration of Material Properties Prediction Benchmarking](https://mlsanity.com/paper/2607.11526/) (2026)
- [Physics-residual machine learning predicts oxygen-evolution catalyst activity beyond the training range from sparse polarization measurements](https://mlsanity.com/paper/2609.23549/) (2026)

## Abstract (arXiv)

Discovery of high-performance materials and molecules requires identifying extremes with property values that fall outside the known distribution. Therefore, the ability to extrapolate to out-of-distribution (OOD) property values is critical for both solid-state materials and molecular design. Our objective is to train predictor models that extrapolate zero-shot to higher ranges than in the training data, given the chemical compositions of solids or molecular graphs and their property values. We propose using a transductive approach to OOD property prediction, achieving improvements in prediction accuracy. In particular, the True Positive Rate (TPR) of OOD classification of materials and molecules improved by 3x and 2.5x, respectively, and precision improved by 2x and 1.5x compared to non-transductive baselines. Our method leverages analogical input-target relations in the training and test sets, enabling generalization beyond the training target support, and can be applied to any other material and molecular tasks.