Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction | Paper Citation Record

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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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 26

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Crystal structure prediction with host-guided inpainting generation and foundation potentials cites this paper.

Crystal structure prediction with host-guided inpainting generation and foundation potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 18

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

LAMBench: A Benchmark for Large Atomistic Models Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 44

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A High-Quality Thermoelectric Material Database with Self-Consistent ZT Filtering cites this paper.

A High-Quality Thermoelectric Material Database with Self-Consistent ZT Filtering Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 10

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Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems cites this paper.

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 51

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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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 11

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations cites this paper.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 42

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Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture cites this paper.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 49

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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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control cites this paper.

Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 61

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An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials cites this paper.

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 21

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Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction cites this paper.

Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials cites this paper.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 38

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Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models cites this paper.

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 53

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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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 25

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Universal Machine Learning Potentials under Pressure cites this paper.

Universal Machine Learning Potentials under Pressure Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 48

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Quantum Advantage in Computational Chemistry? cites this paper.

Quantum Advantage in Computational Chemistry? Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 60

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Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials cites this paper.

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 35

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PLaID++: A Preference Aligned Language Model for Targeted Inorganic Materials Design cites this paper.

PLaID++: A Preference Aligned Language Model for Targeted Inorganic Materials Design Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 11

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Facet: highly efficient E(3)-equivariant networks for interatomic potentials cites this paper.

Facet: highly efficient E(3)-equivariant networks for interatomic potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 23

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Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations cites this paper.

Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 18

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Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials cites this paper.

Benchmarking foundation potentials against quantum chemistry methods for predicting molecular redox potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 26

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AI-Driven Expansion and Application of the Alexandria Database cites this paper.

AI-Driven Expansion and Application of the Alexandria Database Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 19

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OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers cites this paper.

OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 24

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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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 31

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

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 16

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UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems cites this paper.

UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 23

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Observation 589db86a-b090-4247-99f8-e910e6c0a6f6 · inbound

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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 53

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VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials cites this paper.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 14

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Observation c3439d7d-d1ae-409e-907d-385e43588b8d · inbound

Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields cites this paper.

Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 13

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Observation 74028b69-ca36-4b8e-ae1d-1cddbfe6ed0b · inbound

Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement cites this paper.

Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 14

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Observation db539c4f-7aff-4286-853d-bff56bec9cee · inbound

Categorification of Chemical Reactions: a bottom-up tower from stoichiometry to quantum structure cites this paper.

Categorification of Chemical Reactions: a bottom-up tower from stoichiometry to quantum structure Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 299

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Observation 89b809b6-2cf8-482d-8f92-7c4974a44826 · inbound

Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks cites this paper.

Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 23

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Observation 7307c407-f290-4d5a-a976-0f91b4a74cd5 · 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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 47

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Observation 222504fb-7cc4-40ae-ad7e-7cd81296d37f · inbound

Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction cites this paper.

Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 8

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Observation d1382f3d-0ae6-4145-a63d-289fe9380c16 · inbound

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation cites this paper.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 10

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Observation ded4a848-d529-4a9e-b78b-601ab40c7735 · inbound

Unravelling the Role of Stacking Disorder on the Optoelectronic Properties of Zn3P2 cites this paper.

Unravelling the Role of Stacking Disorder on the Optoelectronic Properties of Zn3P2 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 57

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Observation 191ec742-92c9-49a9-bf76-5b3eb30e566c · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 79

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Observation abfe3044-cce9-4227-88fe-fa9e2fe77af2 · inbound

Approaching the Limit of Intrinsic Crystalline Thermal Insulation cites this paper.

Approaching the Limit of Intrinsic Crystalline Thermal Insulation Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 49

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Observation cb68efd0-cedb-4aed-a4d4-2dbc5aaa628b · 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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 20

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Observation a175c471-bd63-42dc-a66f-9c648c8e5da6 · inbound

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 Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 44

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Observation f2e9997e-1136-433b-8727-e71e3a7a97eb · inbound

EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation cites this paper.

EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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Observation 0ac88a12-7d76-477a-bb69-ca93167d2b8c · inbound

Vilya-1: An all-atom foundation model for macrocycle structure prediction and design cites this paper.

Vilya-1: An all-atom foundation model for macrocycle structure prediction and design Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 41

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Observation 529cee33-7431-4f44-ad5b-44b216b959ed · inbound

Transformer Atomic Cluster Expansion: TRACE cites this paper.

Transformer Atomic Cluster Expansion: TRACE Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 14

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Observation 4919b939-95b4-490a-b4c0-c17c05cf57e2 · inbound

Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials cites this paper.

Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 77

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Observation 079ad000-f0e3-455c-b880-104510a62fa6 · inbound

Machine Learning Compatible CALPHAD-type Optimization from Phase Equilibria by Auto-differentiation cites this paper.

Machine Learning Compatible CALPHAD-type Optimization from Phase Equilibria by Auto-differentiation Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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Observation 8236dd72-c187-4494-8e28-729063a74c05 · inbound

ED-CSP: Crystal Structure Prediction from Electron Diffraction cites this paper.

ED-CSP: Crystal Structure Prediction from Electron Diffraction Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 4

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