标题
Quantum machine learning using atom-in-molecule-based fragments selected on the fly
作者
关键词
-
出版物
Nature Chemistry
Volume 12, Issue 10, Pages 945-951
出版商
Springer Science and Business Media LLC
发表日期
2020-09-15
DOI
10.1038/s41557-020-0527-z
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。- 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
- Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
- (2018) Giulio Imbalzano et al. JOURNAL OF CHEMICAL PHYSICS
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Chemical transferability of functional groups follows from the nearsightedness of electronic matter
- (2017) Stijn Fias et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Density functional theory is straying from the path toward the exact functional
- (2017) Michael G. Medvedev et al. SCIENCE
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
- (2016) Bing Huang et al. JOURNAL OF CHEMICAL PHYSICS
- 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
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
- (2015) Raghunathan Ramakrishnan et al. Journal of Chemical Theory and Computation
- 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
- Combinatorial screening for new materials in unconstrained composition space with machine learning
- (2014) B. Meredig et al. PHYSICAL REVIEW B
- 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
- 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
- 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
- Fragmentation Methods: A Route to Accurate Calculations on Large Systems
- (2011) Mark S. Gordon et al. CHEMICAL REVIEWS
- CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures
- (2011) Axel Drefahl Journal of Cheminformatics
- The ORCA program system
- (2011) Frank Neese Wiley Interdisciplinary Reviews-Computational Molecular Science
- On the applicability of fragmentation methods to conjugated π systems within density functional framework
- (2010) Sachin D. Yeole et al. JOURNAL OF CHEMICAL PHYSICS
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Consistent van der Waals Radii for the Whole Main Group
- (2009) Manjeera Mantina et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Alkaline polymer electrolyte fuel cells completely free from noble metal catalysts
- (2008) S. Lu et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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