# Shotgun crystal structure prediction using machine-learned formation energies

arXiv: https://arxiv.org/abs/2305.02158 · HTML: https://mlsanity.com/paper/2305.02158/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 78 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 2 of 2)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Crystal structure prediction with machine learning-based element substitution](https://mlsanity.com/paper/2201.11188/) (2022)

## Built on by (most cited first, 4 of 4)

- [Siamese Foundation Models for Crystal Structure Prediction](https://mlsanity.com/paper/2503.10471/) (2025)
- [Substitution-Based Analysis of Structural Novelty for Generative Models of Materials](https://mlsanity.com/paper/2606.23166/) (2026)
- [CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines](https://mlsanity.com/paper/2504.06878/) (2025)
- [CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction](https://mlsanity.com/paper/2512.18251/) (2025)

## Abstract (arXiv)

Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is computationally less intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures.