PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
This work addresses the need for accurate and efficient atomic-scale simulations across diverse materials, though it appears incremental as it builds on existing universal MLIP approaches with enhancements in dataset diversity and consistency.
The authors tackled the problem of machine-learning interatomic potentials (MLIPs) being biased toward low-energy configurations by introducing PET-MAD, a lightweight universal model trained on a diverse dataset of inorganic and organic solids, which achieved competitive accuracy with state-of-the-art MLIPs for inorganic solids and reliability for molecules, organic materials, and surfaces.
Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent ''universal'' models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable MLIP trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but highly-consistent level of electronic-structure theory, we assess PET-MAD's accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with state-of-the-art MLIPs for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.