# Distributed Representations of Atoms and Materials for Machine Learning

arXiv: https://arxiv.org/abs/2107.14664 · HTML: https://mlsanity.com/paper/2107.14664/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features](https://mlsanity.com/paper/2007.08026/) (2020)
- [Predicting materials properties without crystal structure: Deep representation learning from stoichiometry](https://mlsanity.com/paper/1910.00617/) (2019)
- [Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning](https://mlsanity.com/paper/1902.00925/) (2019)
- [De-Conflated Semantic Representations](https://mlsanity.com/paper/1608.01961/) (2016)
- [Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models](https://mlsanity.com/paper/1806.07139/) (2018)

## Built on by (most cited first, 8 of 8)

- [Interpretable and Explainable Machine Learning for Materials Science and Chemistry](https://mlsanity.com/paper/2111.01037/) (2021)
- [Robust Concept Erasure via Kernelized Rate-Distortion Maximization](https://mlsanity.com/paper/2312.00194/) (2023)
- [Formula graph self-attention network for representation-domain independent materials discovery](https://mlsanity.com/paper/2201.05649/) (2022)
- [Cached Operator Reordering: A Unified View for Fast GNN Training](https://mlsanity.com/paper/2308.12093/) (2023)
- [CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction](https://mlsanity.com/paper/2409.04737/) (2024)
- [Data-driven approach to the design of complexing agents for trivalent transuranium elements](https://mlsanity.com/paper/2509.21362/) (2025)
- [Element2Vec: Build Chemical Element Representation from Text for Property Prediction](https://mlsanity.com/paper/2510.13916/) (2025)
- [Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2604.04636/) (2026)

## Abstract (arXiv)

The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required. We derive distributed representations of compounds from their chemical formulas only, via pooling operations of distributed representations of atoms. These compound representations are evaluated on ten different tasks, such as the prediction of formation energy and band gap, and are found to be competitive with existing benchmarks that make use of structure, and even superior in cases where only composition is available. Finally, we introduce a new approach for learning distributed representations of atoms, named SkipAtom, which makes use of the growing information in materials structure databases.