# Interpretable and Explainable Machine Learning for Materials Science and Chemistry

arXiv: https://arxiv.org/abs/2111.01037 · HTML: https://mlsanity.com/paper/2111.01037/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Machine learning modeling of superconducting critical temperature](https://mlsanity.com/paper/1709.02727/) (2017)
- [Distributed Representations of Atoms and Materials for Machine Learning](https://mlsanity.com/paper/2107.14664/) (2021)

## Built on by (most cited first, 26 of 26)

- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [Multimodal Foundation Models for Material Property Prediction and Discovery](https://mlsanity.com/paper/2312.00111/) (2023)
- [Interpretable Machine Learning in Physics: A Review](https://mlsanity.com/paper/2503.23616/) (2025)
- [xChemAgents: Agentic AI for Explainable Quantum Chemistry](https://mlsanity.com/paper/2505.20574/) (2025)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization](https://mlsanity.com/paper/2603.24382/) (2026)
- [Synthetic data enable experiments in atomistic machine learning](https://mlsanity.com/paper/2211.16443/) (2022)
- [Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction](https://mlsanity.com/paper/2304.06253/) (2023)
- [Rethinking Robustness of Model Attributions](https://mlsanity.com/paper/2312.10534/) (2023)
- [Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients](https://mlsanity.com/paper/2401.04978/) (2024)
- [Incorporating Background Knowledge in Symbolic Regression using a Computer Algebra System](https://mlsanity.com/paper/2301.11919/) (2023)
- [Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics](https://mlsanity.com/paper/2501.02932/) (2025)
- [Learning simple heuristic rules for classifying materials based on chemical composition](https://mlsanity.com/paper/2505.02361/) (2025)
- [Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores](https://mlsanity.com/paper/2512.01080/) (2025)
- [Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits](https://mlsanity.com/paper/2305.19889/) (2023)
- [Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions](https://mlsanity.com/paper/2404.04224/) (2024)
- [Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability](https://mlsanity.com/paper/2509.03547/) (2025)
- [MEIDNet: Multimodal generative AI framework for inverse materials design](https://mlsanity.com/paper/2601.22009/) (2026)
- [Accurate predictive model of band gap with selected important features based on explainable machine learning](https://mlsanity.com/paper/2503.04492/) (2025)
- [Mechanistic Interpretability in the Presence of Architectural Obfuscation](https://mlsanity.com/paper/2506.18053/) (2025)
- [Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials](https://mlsanity.com/paper/2601.00503/) (2026)
- [Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework](https://mlsanity.com/paper/2602.21533/) (2026)
- [Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale](https://mlsanity.com/paper/2603.13445/) (2026)
- [Benchmarking bandgap prediction in semiconductors under experimental and realistic evaluation settings](https://mlsanity.com/paper/2604.25568/) (2026)
- [Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents](https://mlsanity.com/paper/2606.29459/) (2026)
- [Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap](https://mlsanity.com/paper/2607.12113/) (2026)

## Abstract (arXiv)

While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust on model predictions and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need of uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science and chemistry problems.