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Found 6 papers, 4 papers with code
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware.
Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials
Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces and myriad structural microstates.
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains.
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications.
Computational catalyst discovery: Active classification through myopic multiscale sampling
We address this issue by relaxing the catalyst discovery goal into a classification problem: "What is the set of catalysts that is worth testing experimentally?"
Chemical Physics
Methods for comparing uncertainty quantifications for material property predictions
These uncertainty estimates are instrumental for determining which materials to screen next, but there is not yet a standard procedure for judging the quality of such uncertainty estimates objectively.
Materials Science Computational Physics
