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Predicted Antarctic Heat Flow and Uncertainties using Machine Learning
We predicted Antarctic Geothermal Heat Flow (GHF) using a machine learning approach. The adopted approach estimates GHF from multiple geophysical and geological data sets,... -
Predicting electronic screening for fast Koopmans spectral functional calcula...
Koopmans spectral functionals represent a powerful extension of Kohn-Sham density-functional theory (DFT), enabling accurate predictions of spectral properties with... -
Solar Wind Speed Prediction from Coronal Holes
The solar wind, a stream of charged particles originating from the Sun and transcending interplanetary space, poses risks to technology and astronauts. In particular geomagnetic... -
Replication Data for: Are nuclear masks all you need for improved out-of-doma...
This dataset is a processed version of the CAMELYON17 dataset used in the NeurIPS 2024 paper "Are nuclear masks all you need for improved out-of-domain generalization? A closer... -
University of Maryland classified LVIS georeferenced imagery of Arctic summer...
This data collection encompasses 1,387 classified LVIS georeferenced images, which include four classes: Ice, Melt Pond, Open Water, and Shadow. The original LVIS images were... -
Probing the effects of broken symmetries in machine learning
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to... -
The SWELL Knowledge Work Dataset for Stress and User Modeling Research
This is the multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed... -
A Global Dataset for Seasonal to Annual Forecasts of GRACE-like Gridded Terre...
This dataset is linked to the manuscript titled "GRACE-FCast: a global long-lead forecast of total water storage for 2010-2024," which focuses on seasonal to annual forecasting... -
Adaptive energy reference for machine-learning models of the electronic densi...
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and... -
Topology Bench: Systematic Graph Based Benchmarking for Optical Networks
TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical... -
A neural operator-based surrogate solver for free-form electromagnetic invers...
This dataset has no description
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A prediction rigidity formalism for low-cost uncertainties in trained neural ...
Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based... -
SPAᴴM(a,b): encoding the density information from guess Hamiltonian in quantu...
Recently, we introduced a class of molecular representations for kernel-based regression methods — the spectrum of approximated Hamiltonian matrices (SPAᴴM) — that takes... -
Benchmarking machine-readable vectors of chemical reactions on computed activ...
In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks.... -
Homogeneous nucleation of undercooled Al-Ni melts via a machine-learned inter...
Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately... -
Adaptive energy reference for machine-learning models of the electronic densi...
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and... -
Environmental data and image area measurements of different substrate types e...
This dataset contains the percentage coverage of different substrate types (live scleractinian corals, dead scleractinian framework, rubble, hard substrates and fine sediments)... -
Seagrass meadows derived from field to spaceborne earth observation at Midge ...
Seagrass meadow extent and meadow-scape was mapped using three alternative approaches at Midge Point, a coastal turbid water habitat, in the central section of the Great Barrier... -
Seagrass meadows derived from field to spaceborne earth observation at Green ...
Seagrass meadow extent and meadow-scape was mapped using two alternative approaches at Green Island, a reef clear water habitat, in the Cairns section of the Great Barrier Reef,... -
Seagrass meadows derived from field to spaceborne earth observation at Yule P...
Seagrass meadow extent and meadow-scape was mapped using four alternative approaches at Yule Point, a coastal clear water habitat, in the Cairns section of the Great Barrier...
