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Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
v1v2v3 (latest)

Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential

31 October 2024
Kentaro Hino
Yuki Kurashige
ArXiv (abs)PDFHTML

Papers citing "Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential"

12 / 12 papers shown
Title
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast
  and Accurate Force Fields
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Ilyes Batatia
D. P. Kovács
G. Simm
Christoph Ortner
Gábor Csányi
97
507
0
15 Jun 2022
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
324
1,340
0
08 Jan 2021
Geoopt: Riemannian Optimization in PyTorch
Geoopt: Riemannian Optimization in PyTorch
Max Kochurov
R. Karimov
Sergei Kozlukov
113
126
0
06 May 2020
TeaNet: universal neural network interatomic potential inspired by
  iterative electronic relaxations
TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxations
So Takamoto
S. Izumi
Ju Li
GNN
65
80
0
02 Dec 2019
Riemannian Adaptive Optimization Methods
Riemannian Adaptive Optimization Methods
Gary Bécigneul
O. Ganea
ODL
135
259
0
01 Oct 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle
Michael Carbin
411
3,497
0
09 Mar 2018
DeePMD-kit: A deep learning package for many-body potential energy
  representation and molecular dynamics
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang
Linfeng Zhang
Jiequn Han
E. Weinan
AI4CE
92
1,274
0
11 Dec 2017
Deep Potential Molecular Dynamics: a scalable model with the accuracy of
  quantum mechanics
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
Linfeng Zhang
Jiequn Han
Han Wang
R. Car
E. Weinan
85
1,163
0
30 Jul 2017
SchNet: A continuous-filter convolutional neural network for modeling
  quantum interactions
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt
Pieter-Jan Kindermans
Huziel Enoc Sauceda Felix
Stefan Chmiela
A. Tkatchenko
K. Müller
170
1,088
0
26 Jun 2017
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.5K
150,700
0
22 Dec 2014
On the Complexity of Learning with Kernels
On the Complexity of Learning with Kernels
Nicolò Cesa-Bianchi
Yishay Mansour
Ohad Shamir
102
38
0
05 Nov 2014
Stochastic gradient descent on Riemannian manifolds
Stochastic gradient descent on Riemannian manifolds
Silvere Bonnabel
135
593
0
22 Nov 2011
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