Predicting electronic structure properties of transition metal complexes with neural networks
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Predicting electronic structure properties of transition metal complexes with neural networks
Authors
Keywords
-
Journal
Chemical Science
Volume 8, Issue 7, Pages 5137-5152
Publisher
Royal Society of Chemistry (RSC)
Online
2017-05-17
DOI
10.1039/c7sc01247k
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- The many-body expansion combined with neural networks
- (2017) Kun Yao et al. JOURNAL OF CHEMICAL PHYSICS
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes
- (2016) Michael Gastegger et al. JOURNAL OF CHEMICAL PHYSICS
- Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
- (2016) Bing Huang et al. JOURNAL OF CHEMICAL PHYSICS
- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- How Much Can Density Functional Approximations (DFA) Fail? The Extreme Case of the FeO4 Species
- (2016) Wei Huang et al. Journal of Chemical Theory and Computation
- Where Does the Density Localize? Convergent Behavior for Global Hybrids, Range Separation, and DFT+U
- (2016) Terry Z. H. Gani et al. Journal of Chemical Theory and Computation
- Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks
- (2016) Lin Shen et al. Journal of Chemical Theory and Computation
- Evaluation of the Performance of the B3LYP, PBE0, and M06 DFT Functionals, and DBLOC-Corrected Versions, in the Calculation of Redox Potentials and Spin Splittings for Transition Metal Containing Systems
- (2016) Dilek Coskun et al. Journal of Chemical Theory and Computation
- Systematic Error Estimation for Chemical Reaction Energies
- (2016) Gregor N. Simm et al. Journal of Chemical Theory and Computation
- Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression
- (2016) Letif Mones et al. Journal of Chemical Theory and Computation
- Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks
- (2016) Kun Yao et al. Journal of Chemical Theory and Computation
- molSimplify: A toolkit for automating discovery in inorganic chemistry
- (2016) Efthymios I. Ioannidis et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Uncertainty Quantification Framework Applied to the Water–Gas Shift Reaction over Pt-Based Catalysts
- (2016) Eric Walker et al. Journal of Physical Chemistry C
- Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling
- (2016) Jonathan E. Sutton et al. Nature Chemistry
- Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
- (2016) Rafael Gómez-Bombarelli et al. NATURE MATERIALS
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Machine learning exciton dynamics
- (2016) Florian Häse et al. Chemical Science
- The Cambridge Structural Database
- (2016) Colin R. Groom et al. Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials
- Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
- (2016) Arun Mannodi-Kanakkithodi et al. Scientific Reports
- Machine learning bandgaps of double perovskites
- (2016) G. Pilania et al. Scientific Reports
- Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
- (2015) Edward O. Pyzer-Knapp et al. ADVANCED FUNCTIONAL MATERIALS
- Understanding machine-learned density functionals
- (2015) Li Li et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Machine learning for quantum mechanics in a nutshell
- (2015) Matthias Rupp INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Perspective: Treating electron over-delocalization with the DFT+U method
- (2015) Heather J. Kulik JOURNAL OF CHEMICAL PHYSICS
- Towards quantifying the role of exact exchange in predictions of transition metal complex properties
- (2015) Efthymios I. Ioannidis et al. JOURNAL OF CHEMICAL PHYSICS
- High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm
- (2015) Michael Gastegger et al. Journal of Chemical Theory and Computation
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
- (2015) Raghunathan Ramakrishnan et al. Journal of Chemical Theory and Computation
- Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening
- (2015) Xianfeng Ma et al. Journal of Physical Chemistry Letters
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- (2015) Katja Hansen et al. Journal of Physical Chemistry Letters
- Molecular fingerprint similarity search in virtual screening
- (2015) Adrià Cereto-Massagué et al. METHODS
- Accelerated materials property predictions and design using motif-based fingerprints
- (2015) Tran Doan Huan et al. PHYSICAL REVIEW B
- Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
- (2015) Zhenwei Li et al. PHYSICAL REVIEW LETTERS
- Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
- (2015) Dávid Bajusz et al. Journal of Cheminformatics
- Regularization Paths for Generalized Linear Models via Coordinate Descent
- (2015) Jerome Friedman et al. Journal of Statistical Software
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Combinatorial screening for new materials in unconstrained composition space with machine learning
- (2014) B. Meredig et al. PHYSICAL REVIEW B
- Orbital-free bond breaking via machine learning
- (2013) John C. Snyder et al. JOURNAL OF CHEMICAL PHYSICS
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
- (2013) Katja Hansen et al. Journal of Chemical Theory and Computation
- Molecular Similarity in Medicinal Chemistry
- (2013) Gerald Maggiora et al. JOURNAL OF MEDICINAL CHEMISTRY
- A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections
- (2013) Tobias Morawietz et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds
- (2013) Aaron M. Virshup et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Accelerating materials property predictions using machine learning
- (2013) Ghanshyam Pilania et al. Scientific Reports
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- The Catalyst Genome
- (2012) Jens K. Nørskov et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- The role of transition metal complexes in dye sensitized solar devices
- (2012) C.A. Bignozzi et al. COORDINATION CHEMISTRY REVIEWS
- Low-Spin versus High-Spin Ground State in Pseudo-Octahedral Iron Complexes
- (2012) David N. Bowman et al. INORGANIC CHEMISTRY
- Assessment of density functional theory for iron(II) molecules across the spin-crossover transition
- (2012) A. Droghetti et al. JOURNAL OF CHEMICAL PHYSICS
- Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters
- (2012) James J. P. Stewart JOURNAL OF MOLECULAR MODELING
- A B3LYP-DBLOC empirical correction scheme for ligand removal enthalpies of transition metal complexes: parameterization against experimental and CCSD(T)-F12 heats of formation
- (2012) Thomas F. Hughes et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Finding Density Functionals with Machine Learning
- (2012) John C. Snyder et al. PHYSICAL REVIEW LETTERS
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Structure:function relationships in molecular spin-crossover complexes
- (2011) Malcolm A. Halcrow CHEMICAL SOCIETY REVIEWS
- Toward Accurate Theoretical Thermochemistry of First Row Transition Metal Complexes
- (2011) Wanyi Jiang et al. JOURNAL OF PHYSICAL CHEMISTRY A
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
- (2011) Nongnuch Artrith et al. PHYSICAL REVIEW B
- Open Babel: An open chemical toolbox
- (2011) Noel M O'Boyle et al. Journal of Cheminformatics
- Correcting Systematic Errors in DFT Spin-Splitting Energetics for Transition Metal Complexes
- (2010) Thomas F. Hughes et al. Journal of Chemical Theory and Computation
- Quantum Chemistry on Graphical Processing Units. 3. Analytical Energy Gradients, Geometry Optimization, and First Principles Molecular Dynamics
- (2009) Ivan S. Ufimtsev et al. Journal of Chemical Theory and Computation
- DL-FIND: An Open-Source Geometry Optimizer for Atomistic Simulations†
- (2009) Johannes Kästner et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Quantifying uncertainties in the estimation of safety parameters by using bootstrapped artificial neural networks
- (2008) Piercesare Secchi et al. ANNALS OF NUCLEAR ENERGY
- Insights into Current Limitations of Density Functional Theory
- (2008) A. J. Cohen et al. SCIENCE
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started