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  4. Cited By
Metadynamics for Training Neural Network Model Chemistries: a
  Competitive Assessment

Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment

19 December 2017
John E. Herr
Kun Yao
R. McIntyre
David W Toth
John A. Parkhill
ArXivPDFHTML

Papers citing "Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment"

15 / 15 papers shown
Title
Hierarchical modeling of molecular energies using a deep neural network
Hierarchical modeling of molecular energies using a deep neural network
Nicholas Lubbers
Justin S. Smith
K. Barros
AI4CE
BDL
53
270
0
29 Sep 2017
Learning Graph-Level Representation for Drug Discovery
Learning Graph-Level Representation for Drug Discovery
Junying Li
Deng Cai
Xiaofei He
GNN
AI4CE
54
116
0
12 Sep 2017
ANI-1: A data set of 20M off-equilibrium DFT calculations for organic
  molecules
ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
Justin S. Smith
Olexandr Isayev
A. Roitberg
40
5
0
16 Aug 2017
Machine Learning Molecular Dynamics for the Simulation of Infrared
  Spectra
Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
M. Gastegger
J. Behler
P. Marquetand
AI4CE
31
334
0
16 May 2017
Atomic Convolutional Networks for Predicting Protein-Ligand Binding
  Affinity
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Joseph Gomes
Bharath Ramsundar
Evan N. Feinberg
Vijay S. Pande
47
192
0
30 Mar 2017
The Many-Body Expansion Combined with Neural Networks
The Many-Body Expansion Combined with Neural Networks
Kun Yao
John E. Herr
John A. Parkhill
38
96
0
22 Sep 2016
By-passing the Kohn-Sham equations with machine learning
By-passing the Kohn-Sham equations with machine learning
Felix Brockherde
Leslie Vogt
Li Li
M. Tuckerman
K. Burke
K. Müller
AI4CE
58
606
0
09 Sep 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units
  (ELUs)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert
Thomas Unterthiner
Sepp Hochreiter
271
5,518
0
23 Nov 2015
Massively Multitask Networks for Drug Discovery
Massively Multitask Networks for Drug Discovery
Bharath Ramsundar
S. Kearnes
Patrick F. Riley
D. Webster
D. Konerding
Vijay S. Pande
95
471
0
06 Feb 2015
Understanding Kernel Ridge Regression: Common behaviors from simple
  functions to density functionals
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Kevin Vu
John C. Snyder
Li Li
M. Rupp
Brandon F. Chen
Tarek Khelif
K. Müller
K. Burke
45
100
0
16 Jan 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.4K
149,842
0
22 Dec 2014
Understanding Machine-learned Density Functionals
Understanding Machine-learned Density Functionals
Li Li
John C. Snyder
I. Pelaschier
Jessica Huang
U. Niranjan
Paul Duncan
M. Rupp
K. Müller
K. Burke
54
151
0
04 Apr 2014
Orbital-free Bond Breaking via Machine Learning
Orbital-free Bond Breaking via Machine Learning
John C. Snyder
M. Rupp
K. Hansen
Leo Blooston
K. Müller
K. Burke
66
115
0
07 Jun 2013
Finding Density Functionals with Machine Learning
Finding Density Functionals with Machine Learning
John C. Snyder
M. Rupp
K. Hansen
K. Müller
K. Burke
100
476
0
22 Dec 2011
Fast and Accurate Modeling of Molecular Atomization Energies with
  Machine Learning
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
M. Rupp
A. Tkatchenko
K. Müller
O. A. von Lilienfeld
AI4CE
148
1,587
0
12 Sep 2011
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