FLOATBench is a tabular benchmark dataset with 582,120 fatigue labels from 19,404 OpenFAST simulations of three 22 MW FOWT towers, featuring alpha-shape regime partitioning and three evaluation protocols for surrogate models.
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13 Pith papers cite this work, alongside 600 external citations. Polarity classification is still indexing.
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2026 13representative citing papers
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
SOAP and SOAP-Muon optimizers deliver faster convergence and higher final accuracy than Adam for NequIP and Allegro MLIPs, with the largest gains under partial force supervision.
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.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
A facet-resolved adsorption energy distribution method with ML force fields identifies active and methanol-selective alloy nanocatalyst surfaces for CO2 hydrogenation.
DenSNet learns the Hohenberg-Kohn map to electron density with equivariant networks and delta-learning, then maps density to energy, producing stable MD trajectories whose infrared spectra match experiment and DFT on ethanol, ethanethiol, resorcinol, and polythiophene oligomers.
GFlowState introduces interactive visualizations such as trajectory node-link diagrams and transition heatmaps to make GFlowNet training dynamics observable for debugging and quality assessment.
EquiformerV3 advances prior equivariant transformers via efficiency tweaks and new activations, achieving SOTA on OC20, OMat24, and Matbench Discovery with DeNS auxiliary training.
Atompack delivers 96x faster shuffled reads and 79% smaller artifacts than ASE LMDB baselines for complete-record atomistic ML training workloads.
A pretrained universal MLIP without spin or Hubbard-U corrections predicts the same chemisorbed S removal and O uptake under 15 eV O+/O2+ bombardment of WS2 as after three rounds of PBE+D3+U+spin fine-tuning, which still reduces energy/force MAE to 4.5 meV/atom and 0.076 eV/A.
A benchmark dataset of 60,000 DFT calculations on 2D MXenes is created and used to train MLIPs achieving ~1000-4000x CPU speedup with ~10 meV/A force and ~1 meV/atom energy accuracy.
Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including out-of-distribution cases.
citing papers explorer
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FLOATBench: A Dataset and Benchmark for Floating Offshore Wind Turbine Tower Fatigue
FLOATBench is a tabular benchmark dataset with 582,120 fatigue labels from 19,404 OpenFAST simulations of three 22 MW FOWT towers, featuring alpha-shape regime partitioning and three evaluation protocols for surrogate models.
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Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
-
Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
SOAP and SOAP-Muon optimizers deliver faster convergence and higher final accuracy than Adam for NequIP and Allegro MLIPs, with the largest gains under partial force supervision.
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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.
-
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
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Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
A facet-resolved adsorption energy distribution method with ML force fields identifies active and methanol-selective alloy nanocatalyst surfaces for CO2 hydrogenation.
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Enhancing molecular dynamics with equivariant machine-learned densities
DenSNet learns the Hohenberg-Kohn map to electron density with equivariant networks and delta-learning, then maps density to energy, producing stable MD trajectories whose infrared spectra match experiment and DFT on ethanol, ethanethiol, resorcinol, and polythiophene oligomers.
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GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward
GFlowState introduces interactive visualizations such as trajectory node-link diagrams and transition heatmaps to make GFlowNet training dynamics observable for debugging and quality assessment.
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EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
EquiformerV3 advances prior equivariant transformers via efficiency tweaks and new activations, achieving SOTA on OC20, OMat24, and Matbench Discovery with DeNS auxiliary training.
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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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Fine-Tuning a Universal Machine-Learned Interatomic Potential for Oxygen Plasma Interactions with WS$_2$
A pretrained universal MLIP without spin or Hubbard-U corrections predicts the same chemisorbed S removal and O uptake under 15 eV O+/O2+ bombardment of WS2 as after three rounds of PBE+D3+U+spin fine-tuning, which still reduces energy/force MAE to 4.5 meV/atom and 0.076 eV/A.
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Benchmark Dataset for Catalysis on 2D MXenes
A benchmark dataset of 60,000 DFT calculations on 2D MXenes is created and used to train MLIPs achieving ~1000-4000x CPU speedup with ~10 meV/A force and ~1 meV/atom energy accuracy.
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Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including out-of-distribution cases.