| Literature DB >> 29771539 |
Daniel G A Smith1, Lori A Burns1, Dominic A Sirianni1, Daniel R Nascimento2, Ashutosh Kumar3, Andrew M James3, Jeffrey B Schriber4, Tianyuan Zhang4, Boyi Zhang5, Adam S Abbott5, Eric J Berquist6, Marvin H Lechner7, Leonardo A Cunha8, Alexander G Heide9, Jonathan M Waldrop10, Tyler Y Takeshita11, Asem Alenaizan1, Daniel Neuhauser12, Rollin A King9, Andrew C Simmonett13, Justin M Turney5, Henry F Schaefer5, Francesco A Evangelista4, A Eugene DePrince2, T Daniel Crawford3, Konrad Patkowski10, C David Sherrill1.
Abstract
Psi4NumPy demonstrates the use of efficient computational kernels from the open-source Psi4 program through the popular NumPy library for linear algebra in Python to facilitate the rapid development of clear, understandable Python computer code for new quantum chemical methods, while maintaining a relatively low execution time. Using these tools, reference implementations have been created for a number of methods, including self-consistent field (SCF), SCF response, many-body perturbation theory, coupled-cluster theory, configuration interaction, and symmetry-adapted perturbation theory. Furthermore, several reference codes have been integrated into Jupyter notebooks, allowing background, underlying theory, and formula information to be associated with the implementation. Psi4NumPy tools and associated reference implementations can lower the barrier for future development of quantum chemistry methods. These implementations also demonstrate the power of the hybrid C++/Python programming approach employed by the Psi4 program.Year: 2018 PMID: 29771539 DOI: 10.1021/acs.jctc.8b00286
Source DB: PubMed Journal: J Chem Theory Comput ISSN: 1549-9618 Impact factor: 6.006