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How to train a neural network potential
COMMUNICATIONS
To guess or not to guess excited state amplitudes during optimization and dynamics
J. Chem. Phys. 159, 121101 (2023)
https://doi.org/10.1063/5.0163571
TUTORIALS
How to train a neural network potential
In Special Collection:
JCP Editors’ Choice 2023
J. Chem. Phys. 159, 121501 (2023)
https://doi.org/10.1063/5.0160326
ARTICLES
Theoretical Methods and Algorithms
Parameter estimation in ultrafast spectroscopy using probability theory
J. Chem. Phys. 159, 124101 (2023)
https://doi.org/10.1063/5.0160631
Learned mappings for targeted free energy perturbation between peptide conformations
J. Chem. Phys. 159, 124104 (2023)
https://doi.org/10.1063/5.0164662
A new generation of non-diagonal, renormalized self-energies for calculation of electron removal energies
J. Chem. Phys. 159, 124109 (2023)
https://doi.org/10.1063/5.0168779
Investigating the accuracy of density functional methods for molecules in electric fields
J. Chem. Phys. 159, 124111 (2023)
https://doi.org/10.1063/5.0164372
Connection between nuclear and electronic Fukui functions beyond frontier molecular orbitals
J. Chem. Phys. 159, 124112 (2023)
https://doi.org/10.1063/5.0169403
Neural network learned Pauli potential for the advancement of orbital-free density functional theory
J. Chem. Phys. 159, 124114 (2023)
https://doi.org/10.1063/5.0165524
Eliminating finite-size effects on the calculation of x-ray scattering from molecular dynamics simulations
J. Chem. Phys. 159, 124115 (2023)
https://doi.org/10.1063/5.0164365
Classification of the HCN isomerization reaction dynamics in Ar buffer gas via machine learning
J. Chem. Phys. 159, 124116 (2023)
https://doi.org/10.1063/5.0156313
Orbital-free QM/MM simulation combined with a theory of solutions
J. Chem. Phys. 159, 124118 (2023)
https://doi.org/10.1063/5.0160465
Advanced Experimental Techniques
Nuclear induction lineshape modeling via hybrid SDE and MD approach
J. Chem. Phys. 159, 124201 (2023)
https://doi.org/10.1063/5.0163782
Atoms, Molecules, and Clusters
Ab initio study of electronic states and radiative properties of the AcF molecule
J. Chem. Phys. 159, 124301 (2023)
https://doi.org/10.1063/5.0159888
Properties of water and argon clusters developed in supersonic expansions
J. Chem. Phys. 159, 124302 (2023)
https://doi.org/10.1063/5.0166912
Excited-state van der Waals potential energy surfaces for the NO A2Σ+ + CO2 collision complex
J. Chem. Phys. 159, 124303 (2023)
https://doi.org/10.1063/5.0165769
Synthesis mechanism of four metallic Cyclo-N5− energetic materials: A theoretical Perspective
J. Chem. Phys. 159, 124305 (2023)
https://doi.org/10.1063/5.0167200
Rotational energy transfer in the collision of N2O with He atom
J. Chem. Phys. 159, 124306 (2023)
https://doi.org/10.1063/5.0160880
Liquids, Glasses, and Crystals
First-principles molten salt phase diagrams through thermodynamic integration
J. Chem. Phys. 159, 124502 (2023)
https://doi.org/10.1063/5.0164824
Materials, Surfaces, and Interfaces
Bandgaps of long-period polytypes of IV, IV-IV, and III-V semiconductors estimated with an Ising-type additivity model
In Special Collection:
John Perdew Festschrift
J. Chem. Phys. 159, 124702 (2023)
https://doi.org/10.1063/5.0166149
Chemical Physics Software
wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows
In Special Collection:
Software for Atomistic Machine Learning
J. Chem. Phys. 159, 124801 (2023)
https://doi.org/10.1063/5.0156845
Polymers and Soft Matter
Horizontal to perpendicular transition of lamellar and cylinder phases in block copolymer films induced by interface segregation of single-chain nanoparticles during solvent evaporation
In Special Collection:
Polymer Nanoconfinement
J. Chem. Phys. 159, 124901 (2023)
https://doi.org/10.1063/5.0166202
Anisotropic particle multiphase equilibria in nonuniform fields
J. Chem. Phys. 159, 124902 (2023)
https://doi.org/10.1063/5.0169659
