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PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

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arxiv 2503.14118 v2 pith:BUMXTSSW submitted 2025-03-18 cond-mat.mtrl-sci cs.LGphysics.chem-ph

Arslan Mazitov , Filippo Bigi , Matthias Kellner , Paolo Pegolo , Davide Tisi , Guillaume Fraux , Sergey Pozdnyakov , Philip Loche
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Michele Ceriotti
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classification cond-mat.mtrl-scics.LGphysics.chem-ph
keywords accuracypet-madmaterialsmechanicalquantumadvancedcalculationsdeliver
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures

    cs.LG 2026-02 conditional novelty 6.0 of 10

    A bond-deformation benchmark plus a force-smoothness metric is proposed to detect PES artifacts and guide MLIP architecture design, with improvements shown on a new Transformer-style model.

  2. OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure

    cond-mat.mtrl-sci 2025-09 conditional novelty 6.0 of 10

    OpenCSP is an open pressure-diverse dataset and model suite that matches or beats larger universal atomistic models on high-pressure crystal structure prediction with far fewer training data.

  3. Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery

    cs.CE 2026-06 conditional novelty 5.0 of 10

    An agentic HPC skill automates NEB microkinetics, recovers from common failures, and benchmarks ~12 universal MLIPs against DFT for CO2 sublimation on graphite.

  4. Fine-tuning MLIP foundation models: strategies for accuracy and transferability

    physics.chem-ph 2026-06 unverdicted novelty 5.0 of 10

    Systematic tests show naive fine-tuning excels for single-task accuracy while multihead replay best preserves out-of-distribution robustness in MLIP adaptation.

  5. Simultaneous Learning of Static and Dynamic Charges

    physics.chem-ph 2026-01 conditional novelty 5.0 of 10

    Independent learning of static and dynamic charges in water systems achieves comparable accuracy to coupled models with environment-dependent screening but at lower computational cost.

  6. AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials

    physics.comp-ph 2025-09 unverdicted novelty 5.0 of 10

    AiiDA-TrainsPot introduces an automated workflow for training neural-network interatomic potentials via calibrated active learning on carbon allotropes and alloy phase transitions.

  7. Toward Exascale AI for Science: A Scalable AI Skill for Autonomous Microkinetics Discovery

    cs.CE 2026-06 unverdicted novelty 3.0 of 10

    Introduces a scalable AI skill framework for autonomous microkinetics discovery that automates workflows and evaluates surrogate reliability.

  8. Six Open Questions in Machine-Learned Interatomic Potential Foundation Models

    cond-mat.mtrl-sci 2026-06 unverdicted novelty 2.0 of 10

    This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.

  9. SLUSCHI-UP: A Web Infrastructure for SLUSCHI Melting-Temperature Calculations Using Universal Machine-Learning Interatomic Potentials

    cond-mat.mtrl-sci 2026-06 accept novelty 2.0 of 10

    SLUSCHI-UP deploys the SLUSCHI melting-temperature workflow as a web service backed by selectable universal ML interatomic potentials with reported validation errors on benchmark sets.

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