Orbital-free Bond Breaking via Machine Learning

Orbital-free Bond Breaking via Machine Learning

7 June 2013

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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 117 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 106 607 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