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We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials.
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“Comprehensive study of carbon dioxide adsorption in the metal–organic frameworks M 2 (dobdc)(M= Mg, Mn, Fe, Co, Ni, Cu, Zn)”
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“The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies”
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“Adam: A Method for Stochastic Optimization”, 2017
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“PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation”, 2017
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“Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning”, 2018
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“Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials”
Jonathan Schmidt et al · 2023
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“Attention Is All You Need”, 2023
Ashish Vaswani et al · 2023
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“Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials”
Yuyang Wang, Changwen Xu, Zijie Li and Amir Barati · 2023
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Yang Song and Stefano Ermon · 2019
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Ilyes Batatia et al · 2022
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Simon Batzner et al · 2022
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Xiang Fu et al · 2022
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Viktor Zaverkin et al · 2023
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