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By-passing the Kohn-Sham equations with machine learning

9 September 2016

Papers citing "By-passing the Kohn-Sham equations with machine learning"

50 / 88 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
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
E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction Ilan Mitnikov Joseph Jacobson 90 0 0 08 Oct 2024
Machine learning approach for vibronically renormalized electronic band structures Niraj Aryal Sheng Zhang Weiguo Yin Gia-Wei Chern 150 0 0 03 Sep 2024
Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators Karan Shah A. Cangi AI4CE 112 1 0 12 Jul 2024
NeuralSCF: Neural network self-consistent fields for density functional theory Feitong Song Ji Feng 70 1 0 22 Jun 2024
A Recipe for Charge Density Prediction Xiang Fu Andrew S. Rosen Kyle Bystrom Rui Wang Albert Musaelian Boris Kozinsky Tess E. Smidt Tommi Jaakkola 94 6 0 29 May 2024
3DReact: Geometric deep learning for chemical reactions Puck van Gerwen K. Briling Charlotte Bunne Vignesh Ram Somnath Rubén Laplaza Andreas Krause C. Corminboeuf 3DV 86 8 0 13 Dec 2023
Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials Teddy Koker Keegan Quigley Eric Taw Kevin Tibbetts Lin Li 68 15 0 08 Dec 2023
Alpha Zero for Physics: Application of Symbolic Regression with Alpha Zero to find the analytical methods in physics Yoshihiro Michishita AI4CE 85 2 0 21 Nov 2023
Equivariant Neural Operator Learning with Graphon Convolution Chaoran Cheng Jian-wei Peng 45 5 0 17 Nov 2023
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension Sergei Manzhos Manabu Ihara 90 8 0 17 Nov 2023
Electronic excited states from physically-constrained machine learning Edoardo Cignoni Divya Suman Jigyasa Nigam Lorenzo Cupellini B. Mennucci Michele Ceriotti 63 17 0 01 Nov 2023
Machine learning for accuracy in density functional approximations Johannes Voss AI4CE 44 3 0 01 Nov 2023
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach Phillip Pope David Jacobs 59 3 0 28 Oct 2023
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning He Zhang Siyuan Liu Jiacheng You Chang-Shu Liu Shuxin Zheng Ziheng Lu Tong Wang Nanning Zheng Jia Zhang 77 22 0 28 Sep 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 100 9 0 23 Sep 2023
Physics-inspired Equivariant Descriptors of Non-bonded Interactions Kevin K. Huguenin-Dumittan P. Loche Haoran Ni Michele Ceriotti 30 22 0 25 Aug 2023
Materials Informatics: An Algorithmic Design Rule B. Bishnoi 58 0 0 05 May 2023
Wigner kernels: body-ordered equivariant machine learning without a basis Filippo Bigi Sergey Pozdnyakov Michele Ceriotti 54 16 0 07 Mar 2023
SchNetPack 2.0: A neural network toolbox for atomistic machine learning Kristof T. Schütt Stefaan S. P. Hessmann Niklas W. A. Gebauer Jonas Lederer M. Gastegger 78 65 0 11 Dec 2022
The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces Sergei Manzhos Manabu Ihara GP 18 6 0 21 Nov 2022
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification Ruoxi Jiang Rebecca Willett 61 7 0 03 Nov 2022
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees) Paul Novello Gaël Poëtte D. Lugato S. Peluchon P. Congedo AI4CE 71 8 0 27 Sep 2022
A machine learning approach to predict the structural and magnetic properties of Heusler alloy families S. Mitra Aquil Ahmad Sajib Biswas A. Das 27 16 0 07 Aug 2022
Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression Lixue Cheng Jiace Sun J. E. Deustua Vignesh C. Bhethanabotla Thomas F. Miller 39 7 0 17 Jul 2022
Electronic-structure properties from atom-centered predictions of the electron density Andrea Grisafi Alan M Lewis M. Rossi Michele Ceriotti 90 20 0 28 Jun 2022
Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning Jiace Sun Lixue Cheng Thomas F. Miller 74 3 0 31 May 2022
Machine Learning Diffusion Monte Carlo Energies Kevin Ryczko J. Krogel Isaac Tamblyn DiffM 29 14 0 09 May 2022
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery Chenru Duan Fan Liu Aditya Nandy Heather J. Kulik AI4CE 39 34 0 06 May 2022
Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space Lixue Cheng Jiace Sun Thomas F. Miller 48 13 0 21 Apr 2022
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations Fang Wu Stan Z. Li DiffM 60 33 0 19 Apr 2022
Evolving symbolic density functionals He Ma Arunachalam Narayanaswamy Patrick F. Riley Li Li 142 32 0 03 Mar 2022
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN) Prathik R. Kaundinya K. Choudhary S. Kalidindi 41 29 0 20 Jan 2022
Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systems Puhan Zhang Sheng Zhang Gia-Wei Chern AI4CE 36 11 0 03 Jan 2022
Machine learning nonequilibrium electron forces for adiabatic spin dynamics Puhan Zhang Gia-Wei Chern 42 16 0 22 Dec 2021
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids Peter Bjørn Jørgensen Arghya Bhowmik 52 37 0 01 Dec 2021
Reducing the Long Tail Losses in Scientific Emulations with Active Learning Yi Heng Lim M. F. Kasim 39 0 0 15 Nov 2021
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery Aditya Nandy Chenru Duan Heather J. Kulik AI4CE 131 47 0 02 Nov 2021
How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems? Bhupalee Kalita Ryan Pederson Jielun Chen Li Li K. Burke 108 9 0 28 Oct 2021
Geometric Transformer for End-to-End Molecule Properties Prediction Yoni Choukroun Lior Wolf AI4CE ViT 78 16 0 26 Oct 2021
Deep learning for surrogate modelling of 2D mantle convection Siddhant Agarwal Nicola Tosi Pan Kessel D. Breuer G. Montavon AI4TS AI4CE 72 8 0 23 Aug 2021
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities Oliver T. Unke Mihail Bogojeski M. Gastegger Mario Geiger Tess E. Smidt Klaus-Robert Muller 86 94 0 04 Jun 2021
Learning the exchange-correlation functional from nature with fully differentiable density functional theory M. F. Kasim S. Vinko 174 69 0 08 Feb 2021
Bayesian neural networks for weak solution of PDEs with uncertainty quantification Xiaoxuan Zhang K. Garikipati AI4CE 86 12 0 13 Jan 2021
On the equivalence of molecular graph convolution and molecular wave function with poor basis set Masashi Tsubaki T. Mizoguchi 31 9 0 16 Nov 2020
Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning Masashi Tsubaki T. Mizoguchi 55 37 0 16 Nov 2020
Uncertainty estimation for molecular dynamics and sampling G. Imbalzano Yongbin Zhuang V. Kapil K. Rossi Edgar A. Engel Federico Grasselli Michele Ceriotti 38 5 0 10 Nov 2020
DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction Peter Bjørn Jørgensen Arghya Bhowmik 38 22 0 04 Nov 2020
Machine learning of solvent effects on molecular spectra and reactions M. Gastegger Kristof T. Schütt Klaus-Robert Muller AI4CE 61 62 0 28 Oct 2020