# AI-Driven Expansion and Application of the Alexandria Database

arXiv: https://arxiv.org/abs/2512.09169 · HTML: https://mlsanity.com/paper/2512.09169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 9 Dec 2025.

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

- [Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction](https://mlsanity.com/paper/2502.12147/) (2025)
- [Orb: A Fast, Scalable Neural Network Potential](https://mlsanity.com/paper/2410.22570/) (2024)
- [MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling](https://mlsanity.com/paper/2309.05934/) (2023)
- [Artificial Intelligence and Generative Models for Materials Discovery -- A Review](https://mlsanity.com/paper/2508.03278/) (2025)

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

- [CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction](https://mlsanity.com/paper/2512.18251/) (2025)
- [OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery](https://mlsanity.com/paper/2609.26402/) (2026)
- [Thermodynamic assessment of machine learning models for solid-state synthesis prediction](https://mlsanity.com/paper/2602.04075/) (2026)
- [Perspective: Towards sustainable exploration of chemical spaces with machine learning](https://mlsanity.com/paper/2604.00069/) (2026)
- [PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction?](https://mlsanity.com/paper/2610.16480/) (2026)

## Abstract (arXiv)

We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.