标题
Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry
作者
关键词
-
出版物
JOURNAL OF CHEMICAL PHYSICS
Volume 148, Issue 24, Pages 241401
出版商
AIP Publishing
发表日期
2018-06-28
DOI
10.1063/1.5043213
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
- Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
- (2018) Tristan Bereau et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Mohsen Botlani et al. JOURNAL OF CHEMICAL PHYSICS
- Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation
- (2018) Gunnar Schmitz et al. JOURNAL OF CHEMICAL PHYSICS
- Hierarchical modeling of molecular energies using a deep neural network
- (2018) Nicholas Lubbers et al. JOURNAL OF CHEMICAL PHYSICS
- Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
- (2018) Nongnuch Artrith et al. JOURNAL OF CHEMICAL PHYSICS
- Sparse learning of stochastic dynamical equations
- (2018) Lorenzo Boninsegna et al. JOURNAL OF CHEMICAL PHYSICS
- 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
- Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions
- (2018) Flaviu Cipcigan et al. JOURNAL OF CHEMICAL PHYSICS
- Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
- (2018) Thuong T. Nguyen et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning of molecular properties: Locality and active learning
- (2018) Konstantin Gubaev et al. JOURNAL OF CHEMICAL PHYSICS
- Structure prediction of boron-doped graphene by machine learning
- (2018) Thaer M. Dieb et al. JOURNAL OF CHEMICAL PHYSICS
- Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
- (2018) Ryo Nagai et al. JOURNAL OF CHEMICAL PHYSICS
- A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
- (2018) Oliver T. Unke et al. JOURNAL OF CHEMICAL PHYSICS
- Survival of the most transferable at the top of Jacob’s ladder: Defining and testing the ωB97M(2) double hybrid density functional
- (2018) Narbe Mardirossian 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
- Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree search
- (2018) Shin Kiyohara et al. JOURNAL OF CHEMICAL PHYSICS
- A local environment descriptor for machine-learned density functional theory at the generalized gradient approximation level
- (2018) Hyunjun Ji et al. JOURNAL OF CHEMICAL PHYSICS
- Accelerating atomic structure search with cluster regularization
- (2018) K. H. Sørensen et al. JOURNAL OF CHEMICAL PHYSICS
- Compositional descriptor-based recommender system for the materials discovery
- (2018) Atsuto Seko et al. JOURNAL OF CHEMICAL PHYSICS
- High-dimensional fitting of sparse datasets of CCSD(T) electronic energies and MP2 dipole moments, illustrated for the formic acid dimer and its complex IR spectrum
- (2018) Chen Qu et al. JOURNAL OF CHEMICAL PHYSICS
- Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels
- (2018) GuanYa Yang et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Junji Seino et al. JOURNAL OF CHEMICAL PHYSICS
- Metadynamics for training neural network model chemistries: A competitive assessment
- (2018) John E. Herr et al. JOURNAL OF CHEMICAL PHYSICS
- Predicting the stability of ternary intermetallics with density functional theory and machine learning
- (2018) Jonathan Schmidt et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Aditya Kamath et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Truong Son Hy et al. JOURNAL OF CHEMICAL PHYSICS
- Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101¯0) interface from a high-dimensional neural network potential
- (2018) Vanessa Quaranta et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Christoph Wehmeyer et al. JOURNAL OF CHEMICAL PHYSICS
- Less is more: Sampling chemical space with active learning
- (2018) Justin S. Smith et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Claudio Zeni et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Jacob Hollingsworth et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Mitchell A. Wood et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Mohammad Atif Faiz Afzal et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Nicodemo Di Pasquale et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Yin-Jia Zhang et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Michael Eickenberg et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning-based screening of complex molecules for polymer solar cells
- (2018) Peter Bjørn Jørgensen et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Pascal Pernot et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) So Fujikake et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Xiayue Li et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Giulio Imbalzano et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Christopher R. Collins et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) G. Pilania et al. JOURNAL OF CHEMICAL PHYSICS
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- (2018) Y. Matsunaga et al. JOURNAL OF CHEMICAL PHYSICS
- The drug-maker's guide to the galaxy
- (2017) Asher Mullard NATURE
- Machine learning in materials informatics: recent applications and prospects
- (2017) Rampi Ramprasad et al. npj Computational Materials
- Machine learning for quantum mechanics in a nutshell
- (2015) Matthias Rupp INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Probabilistic machine learning and artificial intelligence
- (2015) Zoubin Ghahramani NATURE
- Machine learning: Trends, perspectives, and prospects
- (2015) M. I. Jordan et al. SCIENCE
- Ground-State Energy as a Simple Sum of Orbital Energies in Kohn-Sham Theory: A Shift in Perspective through a Shift in Potential
- (2014) Mel Levy et al. PHYSICAL REVIEW LETTERS
- Virtual screening: an endless staircase?
- (2010) Gisbert Schneider NATURE REVIEWS DRUG DISCOVERY
- Kernel methods in machine learning
- (2008) Thomas Hofmann et al. ANNALS OF STATISTICS
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