标题
Quantum Machine Learning in Chemical Compound Space
作者
关键词
-
出版物
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
Volume 57, Issue 16, Pages 4164-4169
出版商
Wiley
发表日期
2017-12-08
DOI
10.1002/anie.201709686
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- The many-body expansion combined with neural networks
- (2017) Kun Yao et al. JOURNAL OF CHEMICAL PHYSICS
- Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
- (2017) Pavlo O. Dral et al. JOURNAL OF CHEMICAL PHYSICS
- Improving the accuracy of Møller-Plesset perturbation theory with neural networks
- (2017) Robert T. McGibbon et al. JOURNAL OF CHEMICAL PHYSICS
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
- (2017) Kun Yao et al. Journal of Physical Chemistry Letters
- Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties
- (2017) Nicholas J. Browning et al. Journal of Physical Chemistry Letters
- Mastering the game of Go without human knowledge
- (2017) David Silver et al. NATURE
- DeepStack: Expert-level artificial intelligence in heads-up no-limit poker
- (2017) Matej Moravčík et al. SCIENCE
- Solving the quantum many-body problem with artificial neural networks
- (2017) Giuseppe Carleo et al. SCIENCE
- Density functional theory is straying from the path toward the exact functional
- (2017) Michael G. Medvedev et al. SCIENCE
- Predicting electronic structure properties of transition metal complexes with neural networks
- (2017) Jon Paul Janet et al. Chemical Science
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- Bypassing the Kohn-Sham equations with machine learning
- (2017) Felix Brockherde et al. Nature Communications
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- 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
- Google AI algorithm masters ancient game of Go
- (2016) Elizabeth Gibney NATURE
- Mastering the game of Go with deep neural networks and tree search
- (2016) David Silver et al. NATURE
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals
- (2016) Felix A. Faber et al. PHYSICAL REVIEW LETTERS
- 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
- Many Molecular Properties from One Kernel in Chemical Space
- (2015) Raghunathan Ramakrishnan et al. CHIMIA
- 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
- Constructing high-dimensional neural network potentials: A tutorial review
- (2015) Jörg Behler INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Electronic spectra from TDDFT and machine learning in chemical space
- (2015) Raghunathan Ramakrishnan et al. JOURNAL OF CHEMICAL PHYSICS
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
- (2015) Raghunathan Ramakrishnan et al. Journal of Chemical Theory and Computation
- Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules
- (2015) Tristan Bereau et al. Journal of Chemical Theory and Computation
- 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
- Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
- (2015) S. Alireza Ghasemi 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
- Big Data of Materials Science: Critical Role of the Descriptor
- (2015) Luca M. Ghiringhelli et al. PHYSICAL REVIEW LETTERS
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces
- (2014) Sergei Manzhos et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Alternative Approach to Chemical Accuracy: A Neural Networks-Based First-Principles Method for Heat of Formation of Molecules Made of H, C, N, O, F, S, and Cl
- (2014) Jian Sun et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Modeling electronic quantum transport with machine learning
- (2014) Alejandro Lopez-Bezanilla et al. PHYSICAL REVIEW B
- Machine learning for many-body physics: The case of the Anderson impurity model
- (2014) Louis-François Arsenault et al. PHYSICAL REVIEW B
- 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
- Clustering by fast search and find of density peaks
- (2014) A. Rodriguez et al. SCIENCE
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties
- (2013) O. Anatole von Lilienfeld INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- 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
- Accelerating materials property predictions using machine learning
- (2013) Ghanshyam Pilania et al. Scientific Reports
- Watson: Beyond Jeopardy!
- (2012) David Ferrucci et al. ARTIFICIAL INTELLIGENCE
- ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning
- (2012) Matthew A. Kayala et al. Journal of Chemical Information and Modeling
- 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
- Optimizing transition states via kernel-based machine learning
- (2012) Zachary D. Pozun et al. JOURNAL OF 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
- Theoretical Chemistry-Quo Vadis?
- (2011) Walter Thiel ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Toward Quantitative Structure–Property Relationships for Charge Transfer Rates of Polycyclic Aromatic Hydrocarbons
- (2011) Milind Misra et al. Journal of Chemical Theory and Computation
- Editorial: Charting Chemical Space: Challenges and Opportunities for Artificial Intelligence and Machine Learning
- (2011) Pierre Baldi et al. Molecular Informatics
- Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?
- (2011) Roman M. Balabin et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning
- (2009) Chris M. Handley et al. Journal of Chemical Theory and Computation
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