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000125780 0247_ $$2doi$$a10.1002/wcms.1663
000125780 0248_ $$2sideral$$a133167
000125780 037__ $$aART-2023-133167
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000125780 100__ $$0(orcid)0000-0002-0769-7168$$aAlegre-Requena, Juan V.
000125780 245__ $$aAQME: Automated quantum mechanical environments for researchers and educators
000125780 260__ $$c2023
000125780 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125780 5203_ $$aAQME, automated quantum mechanical environments, is a free and open-source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi-empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble-averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre-configured workflows are available in various formats, and hands-on video tutorials illustrate their use.
000125780 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/E07-20R$$9info:eu-repo/grantAgreement/ES/MICINN/IJC-2020-044217-I
000125780 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125780 590__ $$a16.8$$b2023
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000125780 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b1 / 65 = 0.015$$c2023$$dQ1$$eT1
000125780 593__ $$aBiochemistry$$c2023$$dQ1
000125780 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b11 / 230 = 0.048$$c2023$$dQ1$$eT1
000125780 593__ $$aComputational Mathematics$$c2023$$dQ1
000125780 593__ $$aPhysical and Theoretical Chemistry$$c2023$$dQ1
000125780 593__ $$aMaterials Chemistry$$c2023$$dQ1
000125780 593__ $$aComputer Science Applications$$c2023$$dQ1
000125780 594__ $$a28.9$$b2023
000125780 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125780 700__ $$aS. V., Shree Sowndarya
000125780 700__ $$aPérez-Soto, Raúl
000125780 700__ $$aAlturaifi, Turki M.
000125780 700__ $$aPaton, Robert S.
000125780 773__ $$g13, 5 (2023), e1663 [11 pp.]$$pWILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE$$tWILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE$$x1759-0876
000125780 8564_ $$s2966173$$uhttps://zaguan.unizar.es/record/125780/files/texto_completo.pdf$$yVersión publicada
000125780 8564_ $$s2464145$$uhttps://zaguan.unizar.es/record/125780/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000125780 951__ $$a2024-07-31-09:46:02
000125780 980__ $$aARTICLE