Tech Services on the Web: Token X; http://jetson.unl.edu:8080/cocoon/tokenx/index.html?file=../xml/base.xml

Heinrich, Helen

doi:10.1080/07317131.2013.788398

Cited by 1 publication

(1 citation statement)

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“…Automating these multistep workflows minimizes manual effort and human error and enables the complex protocols and their associated data and metadata to be fully captured and reused. [9][10][11][12][13] Automated workflows for QM calculations, including those that address the important challenge of transition state (TS) location and conformational analysis (e.g., Wheeler's QChASM 14 and Duarte's autodE 15 ), have emerged as powerful software tools, such as AARON, 16 ACE, 17 Aiida, 18 Auto-QChem, 19 CatVS, 20 Chemistream, 21 ChemShell, 22 FireWorks, 23 molSimplify, 24 PyADF, 25 and QMflows, 26 among others. In this work, we focus on the development of an automated end-to-end workflow software, AQME, to perform multistep computational tasks spanning multiple programs and theoretical methods.…”

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confidence: 99%

AQME: Automated quantum mechanical environments for researchers and educators

Alegre‐Requena

Pérez‐Soto

et al. 2023

WIREs Comput Mol Sci

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AQME, 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.This article is categorized under: Data Science > Chemoinformatics Data Science > Computer Algorithms and Programming Software > Quantum Chemistry

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