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1306.1812
Cited By
Orbital-free Bond Breaking via Machine Learning
7 June 2013
John C. Snyder
M. Rupp
K. Hansen
Leo Blooston
K. Müller
K. Burke
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Papers citing
"Orbital-free Bond Breaking via Machine Learning"
15 / 15 papers shown
Title
Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
Can Polat
Hasan Kurban
Erchin Serpedin
Mustafa Kurban
AI4CE
54
1
0
17 May 2025
Predicting fermionic densities using a Projected Quantum Kernel method
Francesco Perciavalle
Francesco Plastina
Michele Pisarra
Nicola Lo Gullo
139
0
0
18 Apr 2025
Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
Sergei Manzhos
Johann Luder
Manabu Ihara
Manabu Ihara
116
0
0
08 Feb 2025
Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional
Pablo Del Mazo-Sevillano
J. Hermann
112
13
0
30 Jun 2023
KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory
Roman Remme
Tobias Kaczun
Maximilian Scheurer
A. Dreuw
Fred Hamprecht
70
11
0
08 May 2023
Learning the exchange-correlation functional from nature with fully differentiable density functional theory
M. F. Kasim
S. Vinko
174
69
0
08 Feb 2021
Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
J. Ellis
Lenz Fiedler
G. Popoola
N. Modine
J. A. Stephens
A. Thompson
A. Cangi
S. Rajamanickam
AI4CE
58
40
0
10 Oct 2020
Compressing physical properties of atomic species for improving predictive chemistry
John E. Herr
Kevin J Koh
Kun Yao
John A. Parkhill
AI4CE
55
20
0
31 Oct 2018
Machine learning electron correlation in a disordered medium
Jianhua Ma
Puhan Zhang
Yaohua Tan
Avik W. Ghosh
Gia-Wei Chern
AI4CE
23
15
0
04 Oct 2018
Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
John E. Herr
Kun Yao
R. McIntyre
David W Toth
John A. Parkhill
61
63
0
19 Dec 2017
Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development
Fang Liu
Likai Du
Dongju Zhang
Jun Gao
50
19
0
28 May 2017
The Many-Body Expansion Combined with Neural Networks
Kun Yao
John E. Herr
John A. Parkhill
78
97
0
22 Sep 2016
By-passing the Kohn-Sham equations with machine learning
Felix Brockherde
Leslie Vogt
Li Li
M. Tuckerman
K. Burke
K. Müller
AI4CE
99
607
0
09 Sep 2016
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
67
100
0
16 Jan 2015
Understanding Machine-learned Density Functionals
Li Li
John C. Snyder
I. Pelaschier
Jessica Huang
U. Niranjan
Paul Duncan
M. Rupp
K. Müller
K. Burke
109
152
0
04 Apr 2014
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