An O(L^3) algorithm computes contracted Clebsch-Gordan tensor products for equivariant ML potentials using a structured angular grid and spherical Poisson bracket to handle parity-odd terms at fixed CP rank.
Schütt, Oliver T
8 Pith papers cite this work, alongside 268 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
HotRelax is a high-order tensor message-passing neural network trained on paired unrelaxed-relaxed structures to predict relaxed crystal geometries in a single forward pass without force labels or iteration.
A dual-stream crystal graph network with angle-aware metal–ligand–metal exchange features lowers magnetic-moment prediction MAE from 2.54 to 2.02 μB and, after pretraining, improves FM/AFM classification accuracy from 73.6% to 75.7%.
Drift-React produces full minimum energy pathways for reactions in a single step via SE(3) drifting fields, matching TS accuracy of iterative models with orders-of-magnitude speedup on Transition1x and Halo8 datasets.
TSAgent automates transition state searches at DFT accuracy via an agentic loop, reaching 83% success on 100 OC20NEB examples and 70% on 10 held-out cases versus 73% for human experts.
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
Equivariant GNNs outperform prior models on optical spectra and static permittivity prediction using RPA datasets for materials screening.
A multimodal AI approach is introduced to predict properties arising from stacking dissimilar 2D material layers.
citing papers explorer
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Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks
An O(L^3) algorithm computes contracted Clebsch-Gordan tensor products for equivariant ML potentials using a structured angular grid and spherical Poisson bracket to handle parity-odd terms at fixed CP rank.
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High-order tensor neural network for iteration-free structure relaxation
HotRelax is a high-order tensor message-passing neural network trained on paired unrelaxed-relaxed structures to predict relaxed crystal geometries in a single forward pass without force labels or iteration.
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mCGCNN: A Dual-Stream Crystal Graph Convolutional Neural Network for the Efficient Prediction of Magnetic Properties of Crystalline Materials
A dual-stream crystal graph network with angle-aware metal–ligand–metal exchange features lowers magnetic-moment prediction MAE from 2.54 to 2.02 μB and, after pretraining, improves FM/AFM classification accuracy from 73.6% to 75.7%.
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Drift-React: One-step Generation of Reaction Pathways via SE(3) Drifting Fields
Drift-React produces full minimum energy pathways for reactions in a single step via SE(3) drifting fields, matching TS accuracy of iterative models with orders-of-magnitude speedup on Transition1x and Halo8 datasets.
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TSAgent: An Agentic Workflow for Autonomous Transition State Search
TSAgent automates transition state searches at DFT accuracy via an agentic loop, reaching 83% success on 100 OC20NEB examples and 70% on 10 held-out cases versus 73% for human experts.
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Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
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Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Equivariant GNNs outperform prior models on optical spectra and static permittivity prediction using RPA datasets for materials screening.
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Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
A multimodal AI approach is introduced to predict properties arising from stacking dissimilar 2D material layers.