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6 papers
FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque +24
Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains challenging and computationally intensiv…
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu +6
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…
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases
Martin Siron, Inel Djafar, Ali Ramlaoui +10
The rapid expansion of materials science databases has driven machine learning-based discovery while also posing challenges in data integration, duplication, and interoperability.…
Open Molecular Crystals 2025 (OMC25) Dataset and Models
Vahe Gharakhanyan, Luis Barroso-Luque, Yi Yang +16
The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly av…
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Xiang Fu, Brandon M. Wood, Luis Barroso-Luque +4
Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However…