相关参考文献
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
O. Anatole von Lilienfeld
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2018)
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Tristan Bereau et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Machine learning approaches to evaluate correlation patterns in allosteric signaling: A case study of the PDZ2 domain
Mohsen Botlani et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation
Gunnar Schmitz et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Hierarchical modeling of molecular energies using a deep neural network
Nicholas Lubbers et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
Nongnuch Artrith et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Sparse learning of stochastic dynamical equations
Lorenzo Boninsegna et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
SchNet - A deep learning architecture for molecules and materials
K. T. Schuett et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Alchemical and structural distribution based representation for universal quantum machine learning
Felix A. Faber et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions
Flaviu Cipcigan et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Thuong T. Nguyen et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Machine learning of molecular properties: Locality and active learning
Konstantin Gubaev et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Structure prediction of boron-doped graphene by machine learning
Thaer M. Dieb et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
Ryo Nagai et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
Oliver T. Unke et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Survival of the most transferable at the top of Jacob's ladder: Defining and testing the ωB97M(2) double hybrid density functional
Narbe Mardirossian et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials
M. Gastegger et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree search
Shin Kiyohara et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
A local environment descriptor for machine-learned density functional theory at the generalized gradient approximation level
Hyunjun Ji et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Accelerating atomic structure search with cluster regularization
K. H. Sorensen et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Compositional descriptor-based recommender system for the materials discovery
Atsuto Seko et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
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
Chen Qu et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels
GuanYa Yang et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
Junji Seino et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Metadynamics for training neural network model chemistries: A competitive assessment
John E. Herr et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Predicting the stability of ternary intermetallics with density functional theory and machine learning
Jonathan Schmidt et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy
Aditya Kamath et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Predicting molecular properties with covariant compositional networks
Truong Son Hy et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(10(1)over-bar0) interface from a high-dimensional neural network potential
Vanessa Quaranta et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Less is more: Sampling chemical space with active learning
Justin S. Smith et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Building machine learning force fields for nanoclusters
Claudio Zeni et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Can exact conditions improve machine-learned density functionals?
Jacob Hollingsworth et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Extending the accuracy of the SNAP interatomic potential form
Mitchell A. Wood et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers
Mohammad Atif Faiz Afzal et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
The accuracy of ab initio calculations without ab initio calculations for charged systems: Kriging predictions of atomistic properties for ions in aqueous solutions
Nicodemo Di Pasquale et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
The potential for machine learning in hybrid QM/MM calculations
Yin-Jia Zhang et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Solid harmonic wavelet scattering for predictions of molecule properties
Michael Eickenberg et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Machine learning-based screening of complex molecules for polymer solar cells
Peter Bjorn Jorgensen et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors
Pascal Pernot et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
So Fujikake et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation
Xiayue Li et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
Giulio Imbalzano et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Constant size descriptors for accurate machine learning models of molecular properties
Christopher R. Collins et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Physics-informed machine learning for inorganic scintillator discovery
G. Pilania et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories
Y. Matsunaga et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
THE DRUG-MAKER'S GUIDE TO THE GALAXY
Asher Mullard
NATURE (2017)
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad et al.
NPJ COMPUTATIONAL MATERIALS (2017)
Machine learning for quantum mechanics in a nutshell
Matthias Rupp
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Probabilistic machine learning and artificial intelligence
Zoubin Ghahramani
NATURE (2015)
Machine learning: Trends, perspectives, and prospects
M. I. Jordan et al.
SCIENCE (2015)
Ground-State Energy as a Simple Sum of Orbital Energies in Kohn-Sham Theory: A Shift in Perspective through a Shift in Potential
Mel Levy et al.
PHYSICAL REVIEW LETTERS (2014)
Virtual screening: an endless staircase?
Gisbert Schneider
NATURE REVIEWS DRUG DISCOVERY (2010)
Kernel methods in machine learning
Thomas Hofmann et al.
ANNALS OF STATISTICS (2008)
Prescription for the design and selection of density functional approximations: More constraint satisfaction with fewer fits
JP Perdew et al.
JOURNAL OF CHEMICAL PHYSICS (2005)