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ABSTRACT Fragment‐based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many‐body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose‐built to handle the combinatorial complexity of fragment‐based calculations. To fill this void we introduce “Fragme∩t”, an open‐source software application that provides a tool for community validation of fragment‐based methods, a platform for developing new approximations, and a framework for analyzing many‐body interactions.Fragme∩tincludes algorithms for automatic fragment generation and structure modification, and for distance‐ and energy‐based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q‐Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large‐scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non‐experts.Fragme∩tconforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license. This article is categorized under:Electronic Structure Theory > Ab Initio Electronic Structure MethodsTheoretical and Physical Chemistry > ThermochemistrySoftware > Quantum Chemistry
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DFTpy : An efficient and object‐oriented platform for orbital‐free DFT simulations
Abstract In silico materials design is hampered by the computational complexity of Kohn–Sham DFT, which scales cubically with the system size. Owing to the development of new‐generation kinetic energy density functionals (KEDFs), orbital‐free DFT (OFDFT) can now be successfully applied to a large class of semiconductors and such finite systems as quantum dots and metal clusters. In this work, we present DFTpy, an open‐source software implementing OFDFT written entirely in Python 3 and outsourcing the computationally expensive operations to third‐party modules, such as NumPy and SciPy. When fast simulations are in order, DFTpy exploits the fast Fourier transforms from PyFFTW. New‐generation, nonlocal and density‐dependent‐kernel KEDFs are made computationally efficient by employing linear splines and other methods for fast kernel builds. We showcase DFTpy by solving for the electronic structure of a million‐atom system of aluminum metal which was computed on a single CPU. The Python 3 implementation is object‐oriented, opening the door to easy implementation of new features. As an example, we present a time‐dependent OFDFT implementation (hydrodynamic DFT) which we use to compute the spectra of small metal clusters recovering qualitatively the time‐dependent Kohn–Sham DFT result. The Python codebase allows for easy implementation of application programming interfaces. We showcase the combination of DFTpy and ASE for molecular dynamics simulations of liquid metals. DFTpy is released under the MIT license. This article is categorized under:Software > Quantum ChemistryElectronic Structure Theory > Density Functional TheoryData Science > Computer Algorithms and Programming
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- PAR ID:
- 10360571
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- WIREs Computational Molecular Science
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 1759-0876
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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