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Orb: A Fast, Scalable Neural Network Potential

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arxiv 2410.22570 v1 pith:XPIHD4XT submitted 2024-10-29 cond-mat.mtrl-sci cs.LG

Mark Neumann , James Gin , Benjamin Rhodes , Steven Bennett , Zhiyi Li , Hitarth Choubisa , Arthur Hussey , Jonathan Godwin This is my paper · ORCID
classification cond-mat.mtrl-scics.LG
keywords materialsmodelpotentialsuniversalaspectsatomisticbenchmarkcarlo
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We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials. Orb models are 3-6 times faster than existing universal potentials, stable under simulation for a range of out of distribution materials and, upon release, represented a 31% reduction in error over other methods on the Matbench Discovery benchmark. We explore several aspects of foundation model development for materials, with a focus on diffusion pretraining. We evaluate Orb as a model for geometry optimization, Monte Carlo and molecular dynamics simulations.

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Cited by 20 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 43 citations worldwide. Full citation record

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    Unconstrained non-equivariant and direct-force neural interatomic potentials scale to 730M parameters and match or beat equivariant state-of-the-art models on several atomistic benchmarks.

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    cond-mat.mtrl-sci 2026-07 conditional novelty 6.5 of 10

    Dyna-Mat-v1.0 benchmarks 15 foundation MLIPs on finite-T MD observables, finding average force-error correlation with RDF/VDOS but systematic pressure failures and near-Pareto optimality of latest cross-trained models.

  4. Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations

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    Replacing explicit neural network stacks with self-consistent fixed-point iterations, and warm-starting the solver across timesteps, gives 2-5x cheaper molecular dynamics force evaluation at matched accuracy.

  5. From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions

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    An MLIP-trained CALPHAD workflow with analytic Hessians predicts spinodal and microstructure maps in Hf-Nb-Ti-V, but quantitative mismatches remain in partitioning, misfit, and modulation direction.

  6. Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models

    physics.chem-ph 2026-07 conditional novelty 6.0 of 10

    Rem3Di builds fixed-length, chirality-aware molecular descriptors by aggregating frozen atomistic foundation-model features with attention and a self-supervised denoising pretraining objective, matching or beating gra...

  7. Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW

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    Torched-TACAW plus ORB MD and z-partitioned supercells enables near-ab-initio-quality atomic-resolution vibrational STEM-EELS for thick TiO2 models with tractable memory and data flow.

  8. Performance of universal machine learning potentials in global optimization of inorganic crystal structures

    cond-mat.mtrl-sci 2026-02 conditional novelty 6.0 of 10

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    Distilled non-conservative force models in a multi-time-step integrator accelerate neural-network-potential molecular dynamics by up to 5.6x on tested systems without degrading sampling accuracy.

  10. Thermodynamic assessment of machine learning models for solid-state synthesis prediction

    cond-mat.mtrl-sci 2026-02 accept novelty 6.0 of 10

    Most machine-learning synthesizability models overpredict the likelihood of synthesizing hypothetical ternary oxides, relative to thermodynamic stability and reaction-selectivity bounds; only SynthNN tracks the thermo...

  11. Benchmarking Universal Interatomic Potentials on Zeolite Structures

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

    Universal machine-learned interatomic potentials, especially eSEN-30M-OAM, accurately reproduce DFT-level geometries and energies for zeolites, while classical universal force fields largely fail.

  12. Universal Machine Learning Potentials under Pressure

    cond-mat.mtrl-sci 2025-08 conditional novelty 6.0 of 10

    Universal machine learning interatomic potentials systematically lose accuracy under pressure up to 150 GPa, and fine-tuning on high-pressure DFT data recovers most of the lost performance.

  13. Universal Machine Learning Potential for Systems with Reduced Dimensionality

    cond-mat.mtrl-sci 2025-08 conditional novelty 6.0 of 10

    Benchmarking 11 universal machine learning interatomic potentials on a new 40,000-structure, 0D-3D dataset shows energy and geometry errors grow as dimensionality falls, with eSEN the most transferable.

  14. Distillation of atomistic foundation models across architectures and chemical domains

    physics.comp-ph 2025-06 conditional novelty 6.0 of 10

    Distillation of atomistic foundation models via synthetic data yields 10x-100x faster student potentials with near-teacher accuracy.

  15. Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data

    physics.comp-ph 2025-06 conditional novelty 6.0 of 10

    Several universal machine learning interatomic potentials, especially ORB v3, MatterSim, and MACE-OFF, reach near-DFT phonon accuracy and match many experimental neutron spectra, with important caveats about test-set ...

  16. Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

    cond-mat.mtrl-sci 2025-02 conditional novelty 6.0 of 10

    EquiformerV2 universal machine learning potentials predict energies and forces of metal and alloy defects with errors below 5 meV/atom and 100 meV/A on most benchmark datasets, approaching DFT accuracy.

  17. Quantum machine learning interatomic potential: Application of variational quantum algorithm

    quant-ph 2026-07 conditional novelty 5.0 of 10

    Replacing the final layer of a pretrained ANI interatomic potential with a small quantum circuit training only the circuit parameters gives slightly lower energy RMSE than a classical layer, only where the pretrained ...

  18. Leveraging neural network interatomic potentials for a foundation model of chemistry

    cond-mat.mtrl-sci 2025-06 conditional novelty 5.0 of 10

    Using embeddings from a pretrained neural network interatomic potential as features for small machine learning models gives competitive or better property predictions than end-to-end deep networks, especially with lim...

  19. Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors

    cond-mat.mtrl-sci 2025-02 conditional novelty 5.0 of 10

    MatterSim is the most accurate universal machine-learning potential for solid-state electrolytes across energy, force, mechanical, and lithium-diffusion benchmarks.

  20. Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials

    cond-mat.mtrl-sci 2025-02 conditional novelty 4.0 of 10

    A perspective arguing that current MLIP training on DFT data is insufficient, and proposing CCSD(T)-quality data, metrology, and efficient inference as research priorities.

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