Machine learning for the prediction of molecular dipole moments obtained by density functional theory (original) (raw)

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Florbela Pereira

View PDFchevron_right

A simple model for prediction of dipole moments of isolated molecules

Oleg Victor Prezhdo

Journal of Molecular Structure, 2013

View PDFchevron_right

Machine learning of molecular electronic properties in chemical compound space

O. Von Lilienfeld, Matthias Rupp

New Journal of Physics, 2013

View PDFchevron_right

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Olexandr Isayev

2019

View PDFchevron_right

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning

Wiktor Pronobis

The European Physical Journal B, 2018

View PDFchevron_right

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

Wiktor Pronobis

Journal of Physical Chemistry Letters, 2015

View PDFchevron_right

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules

Wiktor Pronobis

Journal of Chemical Theory and Computation, 2018

View PDFchevron_right

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Michael Bussmann

2021

View PDFchevron_right

Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset

Gabriel Pinheiro

The Journal of Physical Chemistry A, 2020

View PDFchevron_right

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning

Joao Damas

Journal of Chemical Information and Modeling, 2019

View PDFchevron_right

Using Machine Learning to Find New Density Functionals

bhupalee kalita

ArXiv, 2021

View PDFchevron_right

Dataset and scripts for A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Michael Bussmann

2021

View PDFchevron_right

The accuracy of molecular dipole moments in standard electronic structure calculations

Keld Bak

Chemical Physics Letters, 2000

View PDFchevron_right

Performance and basis set dependence of density functional theory dipole and quadrupole moments

Frederik Tielens

View PDFchevron_right

Prediction of conformationally dependent atomic multipole moments in carbohydrates

Paul Popelier

Journal of Computational Chemistry, 2015

View PDFchevron_right

Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms

Logan Ward

The Journal of Physical Chemistry A, 2020

View PDFchevron_right

Machine Learned Synthesizability Predictions Aided by Density Functional Theory

Suchismita sarker

2022

View PDFchevron_right

Description of Potential Energy Surfaces of Molecules Using FFLUX Machine Learning Models

Paul Popelier

Journal of Chemical Theory and Computation, 2018

View PDFchevron_right

How dependent are molecular and atomic properties on the electronic structure method? Comparison of Hartree-Fock, DFT, and MP2 on a biologically relevant set of molecules

Chérif F . Matta

Journal of Computational Chemistry, 2009

View PDFchevron_right

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Josh Horton

Cornell University - arXiv, 2022

View PDFchevron_right

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

Zikai Xie

View PDFchevron_right

Density functional theory-based electric field gradient database

Kamal Choudhary

Scientific Data

View PDFchevron_right

Machine learning of ab-initio energy landscapes for crystal structure predictions

joshua gabriel

Computational Materials Science, 2018

View PDFchevron_right

Machine Learning Predictor Models in the Electronic Properties of Alkanes based on Degree-Topology Indices

Suraya Masrom

International Journal of Emerging Technology and Advanced Engineering, 2021

View PDFchevron_right

Modeling and prediction of molecular properties. Theory of grid-weighted holistic invariant molecular (G-WHIM) descriptors

Laura Bonati, Demetrio Pitea, Giorgio Moro, Roberto Todeschini

Chemometrics and Intelligent Laboratory Systems, 1997

View PDFchevron_right

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

Olexandr Isayev

Scientific data, 2017

View PDFchevron_right

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

O. Von Lilienfeld, Matthias Rupp

Physical Review Letters, 2012

View PDFchevron_right

A machine learning approach to computer-aided molecular design

Filippo Fabrocini

Journal of Computer-Aided Molecular Design, 1991

View PDFchevron_right

Discrete Fourier Transform Improves the Prediction of the Electronic Properties of Molecules in Quantum Machine Learning

Alain Tchagang

2019

View PDFchevron_right

IMPRESSION - Prediction of NMR Parameters for 3-dimensional chemical structures using Machine Learning with near quantum chemical accuracy

Will Gerrard

Chemical Science, 2020

View PDFchevron_right

Choosing the right molecular machine learning potential

Mario Barbatti

Chemical Science

View PDFchevron_right

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

Yashaswi Pathak

2019

View PDFchevron_right

Mapping the Frontier Orbital Energies of Imidazolium-based Cations Using Machine Learning

Wyatt Gassaway

View PDFchevron_right

Improving the accuracy of density-functional theory calculation: The genetic algorithm and neural network approach

Xiujun Wang

Chemical Physics, 2007

View PDFchevron_right

Protein Binding Ligand Prediction Using Moments-Based Methods

daisuke kihara

Protein Function Prediction for Omics Era, 2011

View PDFchevron_right