# Atomistic structure learning

arXiv: https://arxiv.org/abs/1902.10501 · HTML: https://mlsanity.com/paper/1902.10501/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

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

- [Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials](https://mlsanity.com/paper/2312.11708/) (2023)
- [Scalable learning of potentials to predict time-dependent Hartree-Fock dynamics](https://mlsanity.com/paper/2408.04765/) (2024)
- [Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles](https://mlsanity.com/paper/2511.12260/) (2025)
- [AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning](https://mlsanity.com/paper/2605.28287/) (2026)
- [Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors](https://mlsanity.com/paper/2609.33407/) (2026)

## Abstract (arXiv)

One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom. The algorithm takes no prior data or knowledge on atomic interactions but inquires a first-principles quantum mechanical program for physical properties. Using reinforcement learning, the algorithm accumulates knowledge of chemical compound space for a given number and type of atoms and stores this in the neural network, ultimately learning the blueprint for the optimal structural arrangement of the atoms for a given target property. ASLA is demonstrated to work on diverse problems, including grain boundaries in graphene sheets, organic compound formation and a surface oxide structure. This approach to structure prediction is a first step toward direct manipulation of atoms with artificially intelligent first principles computer codes.