v1v2 (latest)

Understanding Machine-learned Density Functionals

4 April 2014

Papers citing "Understanding Machine-learned Density Functionals"

21 / 21 papers shown

Title
Predicting fermionic densities using a Projected Quantum Kernel method Francesco Perciavalle Francesco Plastina Michele Pisarra Nicola Lo Gullo 139 0 0 18 Apr 2025
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension Sergei Manzhos Manabu Ihara 99 8 0 17 Nov 2023
Machine learning for accuracy in density functional approximations Johannes Voss AI4CE 49 3 0 01 Nov 2023
Grad DFT: a software library for machine learning enhanced density functional theory Pablo Antonio Moreno Casares Jack S. Baker Matija Medvidović Roberto Dos Reis J. M. Arrazola 107 9 0 23 Sep 2023
Evolving symbolic density functionals He Ma Arunachalam Narayanaswamy Patrick F. Riley Li Li 142 32 0 03 Mar 2022
Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches R. Korbmacher A. Tordeux 95 93 0 11 Nov 2021
How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems? Bhupalee Kalita Ryan Pederson Jielun Chen Li Li K. Burke 122 9 0 28 Oct 2021
Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method Owen Ren Mohamed Ali Boussaidi Dmitry Voytsekhovsky Manabu Ihara Sergei Manzhos GP 52 28 0 24 Nov 2020
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
Machine Learning for Condensed Matter Physics Edwin Bedolla L. C. Padierna R. Castañeda-Priego AI4CE 80 68 0 28 May 2020
PFNN: A Penalty-Free Neural Network Method for Solving a Class of Second-Order Boundary-Value Problems on Complex Geometries H. Sheng Chao Yang 67 118 0 14 Apr 2020
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning Kevin Maik Jablonka D. Ongari S. M. Moosavi B. Smit AI4CE 85 365 0 18 Jan 2020
Analytical classical density functionals from an equation learning network Shang-Chun Lin Georg Martius M. Oettel 61 36 0 28 Oct 2019
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 32 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
Improving Malware Detection Accuracy by Extracting Icon Information Pedro Silva Sepehr Akhavan Masouleh Li Li 48 4 0 10 Dec 2017
Deep learning and the Schrödinger equation Kyle Mills M. Spanner Isaac Tamblyn 70 140 0 05 Feb 2017
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data Anuj Karpatne G. Atluri James H. Faghmous M. Steinbach A. Banerjee A. Ganguly Shashi Shekhar N. Samatova Vipin Kumar AI4CE 81 998 0 27 Dec 2016
By-passing the Kohn-Sham equations with machine learning Felix Brockherde Leslie Vogt Li Li M. Tuckerman K. Burke K. Müller AI4CE 121 607 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 77 100 0 16 Jan 2015