Neural network, Plinko, Das Model, The Vectors, Input/output, Reinforcement learning
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RT @simonbatzner: We're excited to introduce NequIP, an equivariant Machine Learning Interatomic Potential that not only obtains SOTA on MD-17, but also outperforms existing potentials with up to 1000x fewer data! w/ @tesssmidt @Materials_Intel @bkoz37 #compchem👇🧵 1/N
https://t.co/5njHPLCcyD https://t.co/mnUbxqYgCc
OpenSE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon Batzner,1 Tess E. Smidt,2 Lixin Sun,1 Jonathan P. Mailoa,3 Mordechai Kornbluth,3 Nicola Molinari,1 and Boris Kozinsky1, 3 1Harvard University 2Lawrence Berkeley National Laboratory 3Robert ...
RT @simonbatzner: We're excited to introduce NequIP, an equivariant Machine Learning Interatomic Potential that not only obtains SOTA on MD-17, but also outperforms existing potentials with up to 1000x fewer data! w/ @tesssmidt @Materials_Intel @bkoz37 #compchem👇🧵 1/N
https://t.co/5njHPLCcyD https://t.co/mnUbxqYgCc
OpenClick here to read the article
SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon Batzner,1 Tess E. Smidt,2 Lixin Sun,1 Jonathan P. Mailoa,3 Mordechai Kornbluth,3 Nicola ...
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In this work, we additionally include the SAAO den- sity matrix, P, the orbital centroid distance matrix, D, the core Hamiltonian matrix, H, and the overlap matrix, S. B. Approximated ...
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