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TurboGenius: A python suite for implementing workflows with ab initio quantum Monte Carlo code "TurboRVB"

Authors: Kosuke Nakano;

TurboGenius: A python suite for implementing workflows with ab initio quantum Monte Carlo code "TurboRVB"

Abstract

The poster was presented at the Psi-k 2022 conference, August 22-25. Kosuke Nakano, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Japan Advanced Institute of Science and Technology (JAIST) Ab initio quantum Monte Carlo study involves many complicated operations such as generating trial wave functions, optimizing variational parameters, and time-step (lattice-size) extrapolations. Automation of such tasks can decrease the required time for our work and reduce human errors as much as possible. We have recently developed a python suite named "TurboGenius" that allows us to implement workflows with ab initio quantum Monte Carlo code, "TurboRVB" [K. Nakano et al., J. Chem. Phys. 152, 204121 (2020)]. TurboGenius is implemented by Python 3 in an object-oriented fashion. Users can utilize the provided modules as workflow templates or use the modules in their python scripts. TurboGenius also provides useful command-line interfaces by which users can quickly generate input files, run jobs, and analyze output results. In the presentation, I also show several scientifically new results, including high-throughput computations of binding energy calculations.

Keywords

Quantum Monte Carlo, TREX, TurboRVB

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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