EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations | Paper Citation Record

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Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 20

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Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 20

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Music102: An $D_{12}$-equivariant transformer for chord progression accompaniment cites this paper.

Music102: An $D_{12}$-equivariant transformer for chord progression accompaniment EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 17

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Learning the Electronic Hamiltonian of Large Atomic Structures cites this paper.

Learning the Electronic Hamiltonian of Large Atomic Structures EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 23

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Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity cites this paper.

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 7

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Transformers trained on proteins can learn to attend to Euclidean distance cites this paper.

Transformers trained on proteins can learn to attend to Euclidean distance EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 28

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Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity cites this paper.

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 38

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The Augmented Potential Method: Multiscale Modeling Toward a Spectral Defect Genome cites this paper.

The Augmented Potential Method: Multiscale Modeling Toward a Spectral Defect Genome EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework cites this paper.

Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning cites this paper.

Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 19

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A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention cites this paper.

A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 42

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems cites this paper.

OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow cites this paper.

Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 30

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MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling cites this paper.

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Equivariant Volumetric Grasping cites this paper.

Equivariant Volumetric Grasping EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 32

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing cites this paper.

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 41

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Platonic Transformers: A Solid Choice For Equivariance cites this paper.

Platonic Transformers: A Solid Choice For Equivariance EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory cites this paper.

E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 2023

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Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink cites this paper.

Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 2020

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 42

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Suiren-1.0 Technical Report: A Family of Molecular Foundation Models cites this paper.

Suiren-1.0 Technical Report: A Family of Molecular Foundation Models EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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A Priori Sampling of Transition States with Guided Diffusion cites this paper.

A Priori Sampling of Transition States with Guided Diffusion EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 88

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Knowing when to trust machine-learned interatomic potentials cites this paper.

Knowing when to trust machine-learned interatomic potentials EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 92

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

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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TSAgent: An Agentic Workflow for Autonomous Transition State Search cites this paper.

TSAgent: An Agentic Workflow for Autonomous Transition State Search EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning cites this paper.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials cites this paper.

CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 14

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CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials cites this paper.

CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 14

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CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials cites this paper.

CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 14

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TriSearch: Learning to Optimize Triangulations via Bistellar Flips cites this paper.

TriSearch: Learning to Optimize Triangulations via Bistellar Flips EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 34

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DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution cites this paper.

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 199

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EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning cites this paper.

EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 218

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

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 144

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Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining cites this paper.

Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 10

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REViT: Roto-reflection Equivariant Convolutional Vision Transformer cites this paper.

REViT: Roto-reflection Equivariant Convolutional Vision Transformer EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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High-order tensor neural network for iteration-free structure relaxation cites this paper.

High-order tensor neural network for iteration-free structure relaxation EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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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 EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 46

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E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding cites this paper.

E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 24

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