No Article Processing Charges (APCs) for submissions through December 31, 2025. Submit your research today! | No Article Processing Charges (APCs) for submissions through December 31, 2025 | | |  | Spanning Diverse Areas in Computational Physics Research | APL Computational Physics is a new, dynamic gold open access platform for the rapidly evolving landscape of physical sciences, where computational techniques drive groundbreaking research. The journal offers a dedicated venue for diverse computational physics models, methods, and simulations across a broad range of physical science disciplines. Emphasizing interdisciplinary approaches, it highlights the integration of computational innovations to tackle complex physical challenges.
Check out the diverse topics that will be covered and a sample of the types of articles by each category below. We look forward to receiving your submissions in any of the topical areas in computational physics research. | To learn more about the scope, and the acceptable sub-disciplines, click here. |
| | COMPUTATIONAL SIMULATIONS AND APPLICATIONS |
Atomistic Origin of Diverse Charge Density Wave States in CsV3Sb5 Binhua Zhang, Hengxin Tan, Binghai Yan, Changsong Xu, et al Phys. Rev. Lett. READ MORE > | |
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COMPUTATIONAL TECHNIQUES AND METHODOLOGIES |
Green/WeakCoupling: Implementation of fully self-consistent finite-temperature many-body perturbation theory for molecules and solids Sergei Iskakov, Chia-Nan Yeh, Pavel Pokhilko, Yang Yu, et al Computer Physics Communications READ MORE > | |
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MULTISCALE AND MULTIPHYSICS MODELING |
Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining Chun-I Wang, J. Charlie Maiera, and Nicholas E. Jackson Chem. Sci. READ MORE > | |
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ALGORITHMIC AND NUMERICAL ADVANCES |
Efficient evaluation of the Breit operator in the Pauli spinor basis Shichao Sun, Jordan Ehrman, Qiming Sun, Xiaosong Li J. Chem. Phys. READ MORE > | |
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BIG DATA AND PREDICTIVE COMPUTATION |
Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte using machine-learning interatomic potentials Yongliang Ou, Yuji Ikeda, Lena Scholz, Sergiy Divinski, et al. Phys. Rev. Materials READ MORE > | |
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VALIDATION, VERIFICATION, AND UNCERTAINTY QUANTIFICATION |
Overdestabilization vs Overstabilization in the Theoretical Analysis of f-Orbital Covalency Yongliang Ou, Yuji Ikeda, Lena Scholz, Sergiy Divinski, et al. J. Am. Chem. Soc. READ MORE > | |
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HIGH-PERFORMANCE COMPUTATION |
Small tensor product distributed active space (STP-DAS) framework for relativistic and non-relativistic multiconfiguration calculations: Scaling from 109 on a laptop to 1012 determinants on a supercomputer Hang Hu, Shiv Upadhyay, Lixin Lu, Andrew J. Jenkins, et al. Chem. Phys. Rev. READ MORE > | |
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EXPERIMENTAL DESIGN AND COMPUTATIONAL SUPPORT |
Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum Computer Yuval Baum, Mirko Amico, Sean Howell, Michael Hush, et al. PRX Quantum READ MORE > | |
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NEW SOFTWARE ANNOUNCEMENTS |
The ORCA quantum chemistry program package Frank Neese, Frank Wennmohs, Ute Becker, Christoph Riplinger J. Chem. Phys. READ MORE > | |
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