OMat24 releases a new open dataset of 110M+ DFT calculations and EquiformerV2 models achieving SOTA on Matbench Discovery with F1>0.9 for stability and 20 meV/atom accuracy for formation energies.
A hitchhiker’s guide to geometric gnns for 3d atomic systems
10 Pith papers cite this work, alongside 34 external citations. Polarity classification is still indexing.
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XANE(3) uses a custom E(3)-equivariant GNN with absorber attention, derivative matching loss, and Gaussian spectral readout to predict XANES spectra from atomic structures at 10^{-3} MSE on iron oxide data.
A lightweight Pairformer architecture learns accurate biomolecular committors from atom-level pairs, revealing chignolin TSE bifurcation and substituent-tuned host–guest binding pathways.
A light-front spectator model yields the first calculation of Im(F^g_{1,4}) and the resulting sin(2φ) asymmetry in ep → epπ⁰ at EIC kinematics.
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
Atompack delivers 96x faster shuffled reads and 79% smaller artifacts than ASE LMDB baselines for complete-record atomistic ML training workloads.
Synthetic pre-training on ML-generated tensor data followed by fine-tuning on ground-truth calculations improves data efficiency for graph models of solid-state NMR parameters when the pre-training and fine-tuning domains match.
TriForces adds a model-agnostic three-stream architecture plus self-supervised objectives to atomistic GNNs, improving transfer performance on MatBench, QM9, and limited-data OMat24 without DFT labels.
GNN-based MD simulators achieve stable structure-only initialization and reliable OOD generalization through inference-time physics optimization and a GNN barostat on elastic network compression tasks.
A review of classical and AI-assisted methods for modeling chemical disorder in atomistic simulations of alloys and complex materials.
citing papers explorer
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Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
OMat24 releases a new open dataset of 110M+ DFT calculations and EquiformerV2 models achieving SOTA on Matbench Discovery with F1>0.9 for stability and 20 meV/atom accuracy for formation energies.
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XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures
XANE(3) uses a custom E(3)-equivariant GNN with absorber attention, derivative matching loss, and Gaussian spectral readout to predict XANES spectra from atomic structures at 10^{-3} MSE on iron oxide data.
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Navigating committor landscape of biomolecules with a general pairwise interaction model
A lightweight Pairformer architecture learns accurate biomolecular committors from atom-level pairs, revealing chignolin TSE bifurcation and substituent-tuned host–guest binding pathways.
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Accessing gluon GTMD $F^g_{1,4}$ via the $\langle\sin(2\phi)\rangle$ azimuthal asymmetry of exclusive $\pi^0$ production in $ep$ collisions
A light-front spectator model yields the first calculation of Im(F^g_{1,4}) and the resulting sin(2φ) asymmetry in ep → epπ⁰ at EIC kinematics.
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Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
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Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets
Atompack delivers 96x faster shuffled reads and 79% smaller artifacts than ASE LMDB baselines for complete-record atomistic ML training workloads.
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Synthetic pre-training of graph-network models for predicting solid-state NMR parameters
Synthetic pre-training on ML-generated tensor data followed by fine-tuning on ground-truth calculations improves data efficiency for graph models of solid-state NMR parameters when the pre-training and fine-tuning domains match.
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TriForces: Augmenting Atomistic GNNs for Transferable Representations
TriForces adds a model-agnostic three-stream architecture plus self-supervised objectives to atomistic GNNs, improving transfer performance on MatBench, QM9, and limited-data OMat24 without DFT labels.
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Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators
GNN-based MD simulators achieve stable structure-only initialization and reliable OOD generalization through inference-time physics optimization and a GNN barostat on elastic network compression tasks.
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Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches
A review of classical and AI-assisted methods for modeling chemical disorder in atomistic simulations of alloys and complex materials.