- Home
- Publications
- Publication Search
- Publication Details
Title
FCHL revisited: Faster and more accurate quantum machine learning
Authors
Keywords
-
Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 152, Issue 4, Pages 044107
Publisher
AIP Publishing
Online
2020-01-27
DOI
10.1063/1.5126701
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Training Neural Nets to Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality
- (2019) Silvia Amabilino et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Operators in quantum machine learning: Response properties in chemical space
- (2019) Anders S. Christensen et al. JOURNAL OF CHEMICAL PHYSICS
- A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
- (2019) Lixue Cheng et al. JOURNAL OF CHEMICAL PHYSICS
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
- (2019) Oliver T. Unke et al. Journal of Chemical Theory and Computation
- Chemical Exploration with Virtual Reality in Organic Teaching Laboratories
- (2019) Jonathon B. Ferrell et al. JOURNAL OF CHEMICAL EDUCATION
- Teaching Enzyme Catalysis Using Interactive Molecular Dynamics in Virtual Reality
- (2019) Simon J. Bennie et al. JOURNAL OF CHEMICAL EDUCATION
- Quantum Machine Learning in Chemical Compound Space
- (2018) O. Anatole von Lilienfeld ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Alchemical and structural distribution based representation for universal quantum machine learning
- (2018) Felix A. Faber et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning of molecular properties: Locality and active learning
- (2018) Konstantin Gubaev et al. JOURNAL OF CHEMICAL PHYSICS
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- (2018) M. Gastegger et al. JOURNAL OF CHEMICAL PHYSICS
- Constant size descriptors for accurate machine learning models of molecular properties
- (2018) Christopher R. Collins et al. JOURNAL OF CHEMICAL PHYSICS
- Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules
- (2018) Wiktor Pronobis et al. Journal of Chemical Theory and Computation
- Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
- (2018) Andrea Grisafi et al. PHYSICAL REVIEW LETTERS
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
- (2018) Linfeng Zhang et al. PHYSICAL REVIEW LETTERS
- Sampling molecular conformations and dynamics in a multiuser virtual reality framework
- (2018) Michael O’Connor et al. Science Advances
- Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
- (2018) Matthew Welborn et al. Journal of Chemical Theory and Computation
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- SchNetPack: A Deep Learning Toolbox For Atomistic Systems
- (2018) K. T. Schütt et al. Journal of Chemical Theory and Computation
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
- (2017) Justin S. Smith et al. Scientific Data
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- A universal strategy for the creation of machine learning-based atomistic force fields
- (2017) Tran Doan Huan et al. npj Computational Materials
- 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
- Machine Learning Force Fields: Construction, Validation, and Outlook
- (2016) V. Botu et al. Journal of Physical Chemistry C
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Reproducibility in density functional theory calculations of solids
- (2016) K. Lejaeghere et al. SCIENCE
- Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
- (2015) O. Anatole von Lilienfeld et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Consistent structures and interactions by density functional theory with small atomic orbital basis sets
- (2015) Stefan Grimme et al. JOURNAL OF CHEMICAL PHYSICS
- Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
- (2015) A.P. Thompson et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
- (2015) Matthias Rupp 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
- Learning scheme to predict atomic forces and accelerate materials simulations
- (2015) V. Botu 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
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
- (2013) Katja Hansen et al. Journal of Chemical Theory and Computation
- Machine learning of molecular electronic properties in chemical compound space
- (2013) Grégoire Montavon et al. NEW JOURNAL OF PHYSICS
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
- (2012) Lars Ruddigkeit et al. Journal of Chemical Information and Modeling
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Extended-Connectivity Fingerprints
- (2010) David Rogers et al. Journal of Chemical Information and Modeling
- 970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13
- (2009) Lorenz C. Blum et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExplorePublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More