Orb: A Fast, Scalable Neural Network Potential | Paper Citation Record

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A foundation model for atomistic materials chemistry cites this paper.

A foundation model for atomistic materials chemistry Orb: A Fast, Scalable Neural Network Potential

Reference 114

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A potassium ion channel simulated with a universal neural network potential cites this paper.

A potassium ion channel simulated with a universal neural network potential Orb: A Fast, Scalable Neural Network Potential

Reference 35

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A Quantum Computing Approach to Simulating Corrosion Inhibition cites this paper.

A Quantum Computing Approach to Simulating Corrosion Inhibition Orb: A Fast, Scalable Neural Network Potential

Reference 53

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CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties cites this paper.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Orb: A Fast, Scalable Neural Network Potential

Reference 54

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Universal Machine Learning Interatomic Potentials are Ready for Phonons cites this paper.

Universal Machine Learning Interatomic Potentials are Ready for Phonons Orb: A Fast, Scalable Neural Network Potential

Reference 21

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The OpenLAM Challenges cites this paper.

The OpenLAM Challenges Orb: A Fast, Scalable Neural Network Potential

Reference 4

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Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms cites this paper.

Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms Orb: A Fast, Scalable Neural Network Potential

Reference 27

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

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Orb: A Fast, Scalable Neural Network Potential

Reference 24

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

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 93

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Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning cites this paper.

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning Orb: A Fast, Scalable Neural Network Potential

Reference 9

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Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors cites this paper.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Orb: A Fast, Scalable Neural Network Potential

Reference 29

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High-performance training and inference for deep equivariant interatomic potentials cites this paper.

High-performance training and inference for deep equivariant interatomic potentials Orb: A Fast, Scalable Neural Network Potential

Reference 24

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LAMBench: A Benchmark for Large Atomistic Models cites this paper.

LAMBench: A Benchmark for Large Atomistic Models Orb: A Fast, Scalable Neural Network Potential

Reference 28

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Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation cites this paper.

Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation Orb: A Fast, Scalable Neural Network Potential

Reference 64

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Accelerating point defect photo-emission calculations with machine learning interatomic potentials cites this paper.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Orb: A Fast, Scalable Neural Network Potential

Reference 20

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Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties cites this paper.

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Orb: A Fast, Scalable Neural Network Potential

Reference 185

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HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials cites this paper.

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 25

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

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Orb: A Fast, Scalable Neural Network Potential

Reference 21

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Distillation of atomistic foundation models across architectures and chemical domains cites this paper.

Distillation of atomistic foundation models across architectures and chemical domains Orb: A Fast, Scalable Neural Network Potential

Reference 12

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Leveraging neural network interatomic potentials for a foundation model of chemistry cites this paper.

Leveraging neural network interatomic potentials for a foundation model of chemistry Orb: A Fast, Scalable Neural Network Potential

Reference 13

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Universal Machine Learning Potential for Systems with Reduced Dimensionality cites this paper.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Orb: A Fast, Scalable Neural Network Potential

Reference 9

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Universal Machine Learning Potentials under Pressure cites this paper.

Universal Machine Learning Potentials under Pressure Orb: A Fast, Scalable Neural Network Potential

Reference 32

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Benchmarking Universal Interatomic Potentials on Zeolite Structures cites this paper.

Benchmarking Universal Interatomic Potentials on Zeolite Structures Orb: A Fast, Scalable Neural Network Potential

Reference 22

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Pushing the limits of unconstrained machine-learned interatomic potentials cites this paper.

Pushing the limits of unconstrained machine-learned interatomic potentials Orb: A Fast, Scalable Neural Network Potential

Reference 29

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Thermodynamic assessment of machine learning models for solid-state synthesis prediction cites this paper.

Thermodynamic assessment of machine learning models for solid-state synthesis prediction Orb: A Fast, Scalable Neural Network Potential

Reference 2024

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Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces cites this paper.

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces Orb: A Fast, Scalable Neural Network Potential

Reference 57

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Performance of universal machine learning potentials in global optimization of inorganic crystal structures cites this paper.

Performance of universal machine learning potentials in global optimization of inorganic crystal structures Orb: A Fast, Scalable Neural Network Potential

Reference 55

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Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys cites this paper.

Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys Orb: A Fast, Scalable Neural Network Potential

Reference 18

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OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent cites this paper.

OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent Orb: A Fast, Scalable Neural Network Potential

Reference 22

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Observation 29cddac4-7994-47a9-a76f-e24488d32d7d · inbound

MatterSim-MT: A multi-task foundation model for in silico materials characterization cites this paper.

MatterSim-MT: A multi-task foundation model for in silico materials characterization Orb: A Fast, Scalable Neural Network Potential

Reference 25

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Observation 296f54ea-6446-4d4b-a06d-a60b96be4bd8 · inbound

MatterSim-MT: A multi-task foundation model for in silico materials characterization cites this paper.

MatterSim-MT: A multi-task foundation model for in silico materials characterization Orb: A Fast, Scalable Neural Network Potential

Reference 25

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Observation ccd13a70-2647-488f-892c-16d0e8529da5 · inbound

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models cites this paper.

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models Orb: A Fast, Scalable Neural Network Potential

Reference 41

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Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials cites this paper.

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 30

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Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates cites this paper.

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates Orb: A Fast, Scalable Neural Network Potential

Reference 38

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Observation a24884c9-6563-4f99-86a3-ceffd6a7de6d · inbound

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead cites this paper.

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead Orb: A Fast, Scalable Neural Network Potential

Reference 46

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Observation 4c3298d1-62bc-48d0-990a-9ab1ee9d0b2e · inbound

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations cites this paper.

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations Orb: A Fast, Scalable Neural Network Potential

Reference 20

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Observation 988974d6-8a5c-4005-b9a1-d9feda53857b · inbound

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations cites this paper.

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations Orb: A Fast, Scalable Neural Network Potential

Reference 5

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Additive binding energies in asphalt on a quantum processor via quantum-selected configuration interaction (QSCI) cites this paper.

Additive binding energies in asphalt on a quantum processor via quantum-selected configuration interaction (QSCI) Orb: A Fast, Scalable Neural Network Potential

Reference 21

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Observation 0f02222d-55fd-4643-9e76-c5e64bf8f30b · inbound

Geometry-based Discovery of Calcium Battery Cathodes Accelerated by Foundational Machine-Learned Models cites this paper.

Geometry-based Discovery of Calcium Battery Cathodes Accelerated by Foundational Machine-Learned Models Orb: A Fast, Scalable Neural Network Potential

Reference 69

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Observation 4c005fb8-a53c-41d5-ba37-8046abc32cd6 · inbound

Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials cites this paper.

Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 16

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Observation ab903c60-8524-4137-9ed2-675d40b66448 · inbound

Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization cites this paper.

Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization Orb: A Fast, Scalable Neural Network Potential

Reference 24

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Synthetic pre-training of graph-network models for predicting solid-state NMR parameters cites this paper.

Synthetic pre-training of graph-network models for predicting solid-state NMR parameters Orb: A Fast, Scalable Neural Network Potential

Reference 27

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Fine-tuning MLIP foundation models: strategies for accuracy and transferability cites this paper.

Fine-tuning MLIP foundation models: strategies for accuracy and transferability Orb: A Fast, Scalable Neural Network Potential

Reference 5

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Observation 4562a227-8a7c-43aa-98a5-24fbdda440df · inbound

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning cites this paper.

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning Orb: A Fast, Scalable Neural Network Potential

Reference 30

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Observation 816c0e26-f09d-41bb-aecf-ea0b154236ec · inbound

SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery cites this paper.

SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery Orb: A Fast, Scalable Neural Network Potential

Reference 8

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Observation ddd96b36-f1ea-4a0b-889f-763432a761e5 · inbound

Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models cites this paper.

Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models Orb: A Fast, Scalable Neural Network Potential

Reference 98

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Observation e5cdbe53-5bc9-4613-b047-e3467ac15228 · inbound

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design cites this paper.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Orb: A Fast, Scalable Neural Network Potential

Reference 19

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Observation 539fc044-441e-43b5-bfa1-02759c521c60 · inbound

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW cites this paper.

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW Orb: A Fast, Scalable Neural Network Potential

Reference 39

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Observation 093d2cbd-9dcf-4798-909d-89342ea47030 · inbound

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW cites this paper.

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW Orb: A Fast, Scalable Neural Network Potential

Reference 39

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles cites this paper.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Orb: A Fast, Scalable Neural Network Potential

Reference 41

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Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models cites this paper.

Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models Orb: A Fast, Scalable Neural Network Potential

Reference 28

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Observation 634002fe-e05e-4f2e-a282-80f60e76d136 · inbound

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 cites this paper.

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 Orb: A Fast, Scalable Neural Network Potential

Reference 27

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Observation 8822d2fa-4666-4858-a0cc-d099440cecb7 · inbound

Quantum machine learning interatomic potential: Application of variational quantum algorithm cites this paper.

Quantum machine learning interatomic potential: Application of variational quantum algorithm Orb: A Fast, Scalable Neural Network Potential

Reference 12

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Observation 1c5d5b2f-4375-4fde-9662-3c9933bc5374 · inbound

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations cites this paper.

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations Orb: A Fast, Scalable Neural Network Potential

Reference 44

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Observation 9d7fd2b5-583b-4337-90e3-8a0bdd7d272d · inbound

Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations cites this paper.

Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations Orb: A Fast, Scalable Neural Network Potential

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