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Communication: Is directed percolation in colloid-polymer mixtures linked to dynamic arrest?
Communication: Contact values of pair distribution functions in colloidal hard disks by test-particle insertion
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy
Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Solid harmonic wavelet scattering for predictions of molecule properties
Predicting molecular properties with covariant compositional networks
Inclusion of nuclear quantum effects for simulations of nonlinear spectroscopy
An efficient water force field calibrated against intermolecular THz and Raman spectra
Issues
COMMUNICATIONS
Communication: Is directed percolation in colloid-polymer mixtures linked to dynamic arrest?
J. Chem. Phys. 148, 241101 (2018)
https://doi.org/10.1063/1.5037680
Communication: Contact values of pair distribution functions in colloidal hard disks by test-particle insertion
In Special Collection:
JCP Editors' Choice 2018
J. Chem. Phys. 148, 241102 (2018)
https://doi.org/10.1063/1.5038668
SPECIAL TOPIC: DATA-ENABLED THEORETICAL CHEMISTRY
Guest Editorial
Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241401 (2018)
https://doi.org/10.1063/1.5043213
Articles
Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation
Xiayue Li; Farren S. Curtis; Timothy Rose; Christoph Schober; Alvaro Vazquez-Mayagoitia; Karsten Reuter; Harald Oberhofer; Noa Marom
J. Chem. Phys. 148, 241701 (2018)
https://doi.org/10.1063/1.5014038
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241702 (2018)
https://doi.org/10.1063/1.5003074
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241703 (2018)
https://doi.org/10.1063/1.5011399
Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241704 (2018)
https://doi.org/10.1063/1.5009347
Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241705 (2018)
https://doi.org/10.1063/1.5007230
A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241708 (2018)
https://doi.org/10.1063/1.5017898
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241709 (2018)
https://doi.org/10.1063/1.5019667
Metadynamics for training neural network model chemistries: A competitive assessment
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241710 (2018)
https://doi.org/10.1063/1.5020067
Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241711 (2018)
https://doi.org/10.1063/1.5017661
Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241712 (2018)
https://doi.org/10.1063/1.5007873
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
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241713 (2018)
https://doi.org/10.1063/1.5017495
Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241714 (2018)
https://doi.org/10.1063/1.5016317
Hierarchical modeling of molecular energies using a deep neural network
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241715 (2018)
https://doi.org/10.1063/1.5011181
Structure prediction of boron-doped graphene by machine learning
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241716 (2018)
https://doi.org/10.1063/1.5018065
Alchemical and structural distribution based representation for universal quantum machine learning
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241717 (2018)
https://doi.org/10.1063/1.5020710
Constant size descriptors for accurate machine learning models of molecular properties
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241718 (2018)
https://doi.org/10.1063/1.5020441
Compositional descriptor-based recommender system for the materials discovery
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241719 (2018)
https://doi.org/10.1063/1.5016210
Extending the accuracy of the SNAP interatomic potential form
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241721 (2018)
https://doi.org/10.1063/1.5017641
SchNet – A deep learning architecture for molecules and materials
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241722 (2018)
https://doi.org/10.1063/1.5019779
Sparse learning of stochastic dynamical equations
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241723 (2018)
https://doi.org/10.1063/1.5018409
The accuracy of ab initio calculations without ab initio calculations for charged systems: Kriging predictions of atomistic properties for ions in aqueous solutions
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241724 (2018)
https://doi.org/10.1063/1.5022174
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
In Special Collection:
Data-Enabled Theoretical Chemistry
Thuong T. Nguyen; Eszter Székely; Giulio Imbalzano; Jörg Behler; Gábor Csányi; Michele Ceriotti; Andreas W. Götz; Francesco Paesani
J. Chem. Phys. 148, 241725 (2018)
https://doi.org/10.1063/1.5024577
Machine learning of molecular properties: Locality and active learning
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241727 (2018)
https://doi.org/10.1063/1.5005095
Predicting the stability of ternary intermetallics with density functional theory and machine learning
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241728 (2018)
https://doi.org/10.1063/1.5020223
Physics-informed machine learning for inorganic scintillator discovery
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241729 (2018)
https://doi.org/10.1063/1.5025819
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241730 (2018)
https://doi.org/10.1063/1.5024611
Solid harmonic wavelet scattering for predictions of molecule properties
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241732 (2018)
https://doi.org/10.1063/1.5023798
Less is more: Sampling chemical space with active learning
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241733 (2018)
https://doi.org/10.1063/1.5023802
Accelerating atomic structure search with cluster regularization
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241734 (2018)
https://doi.org/10.1063/1.5023671
Machine learning-based screening of complex molecules for polymer solar cells
In Special Collection:
Data-Enabled Theoretical Chemistry
Peter Bjørn Jørgensen; Murat Mesta; Suranjan Shil; Juan Maria García Lastra; Karsten Wedel Jacobsen; Kristian Sommer Thygesen; Mikkel N. Schmidt
J. Chem. Phys. 148, 241735 (2018)
https://doi.org/10.1063/1.5023563
Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241737 (2018)
https://doi.org/10.1063/1.5029279
Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241738 (2018)
https://doi.org/10.1063/1.5024442
Building machine learning force fields for nanoclusters
In Special Collection:
Data-Enabled Theoretical Chemistry
Claudio Zeni; Kevin Rossi; Aldo Glielmo; Ádám Fekete; Nicola Gaston; Francesca Baletto; Alessandro De Vita
J. Chem. Phys. 148, 241739 (2018)
https://doi.org/10.1063/1.5024558
The potential for machine learning in hybrid QM/MM calculations
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241740 (2018)
https://doi.org/10.1063/1.5029879
Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree search
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241741 (2018)
https://doi.org/10.1063/1.5023139
A local environment descriptor for machine-learned density functional theory at the generalized gradient approximation level
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241742 (2018)
https://doi.org/10.1063/1.5022839
Can exact conditions improve machine-learned density functionals?
In Special Collection:
Data-Enabled Theoretical Chemistry
J. Chem. Phys. 148, 241743 (2018)
https://doi.org/10.1063/1.5025668
Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions
In Special Collection:
Data-Enabled Theoretical Chemistry
Flaviu Cipcigan; Anna Paola Carrieri; Edward O. Pyzer-Knapp; Ritesh Krishna; Ya-Wen Hsiao; Martyn Winn; Maxim G. Ryadnov; Colin Edge; Glenn Martyna; Jason Crain
J. Chem. Phys. 148, 241744 (2018)
https://doi.org/10.1063/1.5027261
Predicting molecular properties with covariant compositional networks
J. Chem. Phys. 148, 241745 (2018)
https://doi.org/10.1063/1.5024797
ARTICLES
Theoretical Methods and Algorithms
A near-linear scaling equation of motion coupled cluster method for ionized states
J. Chem. Phys. 148, 244101 (2018)
https://doi.org/10.1063/1.5029470
Inclusion of nuclear quantum effects for simulations of nonlinear spectroscopy
J. Chem. Phys. 148, 244105 (2018)
https://doi.org/10.1063/1.5036768
Fermi resonance in OH-stretch vibrational spectroscopy of liquid water and the water hexamer
J. Chem. Phys. 148, 244107 (2018)
https://doi.org/10.1063/1.5037113
Exponential propagators for the Schrödinger equation with a time-dependent potential
J. Chem. Phys. 148, 244109 (2018)
https://doi.org/10.1063/1.5036838
Atoms, Molecules, and Clusters
Criegee intermediate inside fullerene cage: Evidence for size-dependent reactivity
J. Chem. Phys. 148, 244301 (2018)
https://doi.org/10.1063/1.5024786
Fourier-transform-spectroscopic photoabsorption cross sections and oscillator strengths for the S2 system
J. Chem. Phys. 148, 244302 (2018)
https://doi.org/10.1063/1.5029929
Predissociation of the state of S2: A coupled-channel model
J. Chem. Phys. 148, 244303 (2018)
https://doi.org/10.1063/1.5029930
Anion photoelectron spectroscopy and chemical bonding of and
J. Chem. Phys. 148, 244304 (2018)
https://doi.org/10.1063/1.5030142
Scattering of CO with H2O: Statistical and classical alternatives to close-coupling calculations
J. Chem. Phys. 148, 244308 (2018)
https://doi.org/10.1063/1.5036819
Liquids, Glasses, and Crystals
Atomic diffusion in liquid nickel: First-principles modeling
J. Chem. Phys. 148, 244503 (2018)
https://doi.org/10.1063/1.5026348
An efficient water force field calibrated against intermolecular THz and Raman spectra
J. Chem. Phys. 148, 244504 (2018)
https://doi.org/10.1063/1.5037062
Decoding entangled transitions: Polyamorphism and stressed rigidity
J. Chem. Phys. 148, 244505 (2018)
https://doi.org/10.1063/1.5034500
Rheology of the transition in liquid sulfur: Insights from arsenic sulfide liquids
J. Chem. Phys. 148, 244506 (2018)
https://doi.org/10.1063/1.5037719
Surfaces, Interfaces, and Materials
New nickel-based hybrid organic/inorganic metal halide for photovoltaic applications
J. Chem. Phys. 148, 244703 (2018)
https://doi.org/10.1063/1.5025077
Enhancement of reaction rate in small-sized droplets: A combined analytical and simulation study
J. Chem. Phys. 148, 244704 (2018)
https://doi.org/10.1063/1.5030114
Polymers and Soft Matter
Influence of weak reversible cross-linkers on entangled polymer melt dynamics
J. Chem. Phys. 148, 244901 (2018)
https://doi.org/10.1063/1.5019277
Conformation and dynamics of flexible polyelectrolytes in semidilute salt-free solutions
J. Chem. Phys. 148, 244902 (2018)
https://doi.org/10.1063/1.5024242
A simulation method for the phase diagram of complex fluid mixtures
J. Chem. Phys. 148, 244903 (2018)
https://doi.org/10.1063/1.5033958
Langevin dynamics simulation of crystallization of ring polymers
J. Chem. Phys. 148, 244904 (2018)
https://doi.org/10.1063/1.5023602
Biological Molecules and Networks
Effect of membrane tension on transbilayer movement of lipids
J. Chem. Phys. 148, 245101 (2018)
https://doi.org/10.1063/1.5035148
LETTERS TO THE EDITOR
Notes
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
CREST—A program for the exploration of low-energy molecular chemical space
Philipp Pracht, Stefan Grimme, et al.
Dielectric profile at the Pt(111)/water interface
Jia-Xin Zhu, Jun Cheng, et al.