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Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials
Kamal Singh Nayal1, Dana O'Connor2, Roman Zubatyuk1
1Department of Chemistry, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States.
Crystal Growth & Design
|November 10, 2025
Summary
Machine-learned interatomic potentials (MLIPs) accelerate crystal structure prediction by training on molecular clusters. This approach accurately ranks crystal stability without expensive periodic calculations, proving effective for diverse chemical applications.
Area of Science:
- Materials Chemistry
- Computational Materials Science
- Crystallography
Background:
- Accurate crystal structure prediction (CSP) is crucial for materials discovery but computationally expensive.
- Traditional methods rely on first-principles calculations, which become prohibitive for large systems requiring millions of energy evaluations.
- The Cambridge Crystallographic Data Centre's (CCDC) blind tests highlight the need for more efficient CSP methodologies.
Purpose of the Study:
- To develop and validate a computationally efficient approach for crystal structure prediction.
- To demonstrate the effectiveness of machine-learned interatomic potentials (MLIPs) trained on molecular clusters for CSP.
- To assess the performance of the AIMNet2 MLIPs in accurately characterizing the CSP landscape and ranking crystal stability.
Main Methods:
- Training target-specific AIMNet2 machine-learned interatomic potentials (MLIPs) on density functional theory (DFT) calculations of molecular clusters (n-mers).
- Utilizing gas-phase dispersion-corrected DFT reference data for training the MLIPs.
- Applying the trained MLIPs to assess the relative stability of candidate crystal structures in CSP workflows.
Main Results:
- MLIPs trained on n-mer data successfully extended to crystalline environments, accurately characterizing the CSP landscape.
- The methodology correctly ranked candidate crystal structures by relative stability, demonstrating its efficacy.
- AIMNet2 potentials showed strong performance across diverse chemical systems relevant to pharmaceuticals, optoelectronics, and agrochemicals.
Conclusions:
- Target-specific MLIPs offer a significant acceleration of CSP workflows.
- This approach effectively captures the physics of thermodynamic crystal stability using only molecular cluster data, avoiding costly periodic calculations.
- AIMNet2 MLIPs present a promising and efficient alternative to full DFT calculations for routine CSP tasks.
