Results 81 to 90 of about 6,576 (123)

Deep learning methods for 2D material electronic properties.

Digit Discov
Mishchenko A, Bhattacharya A, Wang X, Pentz HK, Wei Y, Yang Q.   +5 more
europepmc   +1 more source

Equivariant Neural Networks Reveal How Host-Guest Interactions Shape ¹²⁹Xe NMR in Porous Liquids. [PDF]

J Phys Chem Lett
Zakary O, Lantto P.
europepmc   +1 more source

Support field neural representation learner framework for learning stability landscapes in molecular geometry. [PDF]

NPJ Biomed Innov
Wang J.
europepmc   +1 more source

Deep generative model for the inverse design of Van der Waals heterostructures. [PDF]

Sci Rep
Gao S, Huang Q, Huang C, Li C, Liu K, Sa B, Yu Y, Xue D, Liu Z, Dai M.   +9 more
europepmc   +1 more source

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.

Digit Discov
Ben Mahmoud C, El-Machachi Z, Gierczak KA, Gardner JLA, Deringer VL.   +4 more
europepmc   +1 more source

chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations. [PDF]

J Chem Theory Comput
Fuchs P, Chen W, Thaler S, Zavadlav J.
europepmc   +1 more source

CrystalFlow: a flow-based generative model for crystalline materials. [PDF]

Nat Commun
Luo X, Wang Z, Wang Q, Shao X, Lv J, Wang L, Wang Y, Ma Y.   +7 more
europepmc   +1 more source

<i>P</i>-adic <i>L</i>-functions for GL ( 3 ). [PDF]

Math Ann
Loeffler D, Williams C.
europepmc   +1 more source

Long-range electrostatics for machine learning interatomic potentials is easier than we thought. [PDF]

J Chem Phys
Kim D, Cheng B.
europepmc   +1 more source

Learning non-local molecular interactions via equivariant local representations and charge equilibration. [PDF]

NPJ Comput Mater
Fuchs P, Sanocki M, Zavadlav J.
europepmc   +1 more source