AI-Driven Expansion and Application of the Alexandria Database

📝 Original Info

• Title: AI-Driven Expansion and Application of the Alexandria Database
• ArXiv ID: 2512.09169
• Date: 2025-12-09
• Authors: Théo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck, Antoine Loew, Tiago F. T. Cerqueira, Hai-Chen Wang, Anton Bochkarev, Yury Lysogorskiy, Aldo H. Romero, Ralf Drautz, Silvana Botti, Miguel A. L. Marques

📝 Abstract

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 str...

📄 Full Content

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