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WIREs Comput Mol Sci

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The MolSSI QCArchive project: An open‐source platform to compute, organize, and share quantum chemistry data

Advanced Review

Daniel G. A. Smith, Doaa Altarawy, Lori A. Burns, Matthew Welborn, Levi N. Naden, Logan Ward, Sam Ellis, Benjamin P. Pritchard, T. Daniel Crawford

Published Online: Jul 31 2020

DOI: 10.1002/wcms.1491

Full article on Wiley Online Library: HTML PDF

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Abstract The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python‐based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI‐hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force‐field fitting, and more through a Python interface. Facile, user‐friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchive Infrastructure is open‐source (BSD‐3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming

Relationship between the QCArchive project, MolSSI, and the CMS community

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An example of the Reaction Datasets Viewer app showing the S22 dataset under several DFT method and basis combinations

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An example of the Quantum Chemistry Machine Learning Datasets Repository showing an expansion of the COMP6 DrugBank dataset

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Visualization of the butane carbon backbone torsion using the TorsionDrive Dataset object to compare several methods

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Setup and evaluation of a force field, machine learning potential, and DFT computations with a TorsionDrive Dataset object

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An example setup for a new TorsionDrive Dataset object with hydrogen peroxide and butane molecules

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An image showing QCPortal pulling the S22 dataset from the MQCAS, listing all current B3LYP interaction energies, and rendering an example molecular complex from the dataset

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The full QCArchive Infrastructure and how each layer and module communicates. Between different locations, all pieces communicate over TCP/IP protocols; within individual boxes, all pieces are connected through a single Python interpreter; and between a resource task queue and workers, communication can come in a variety of RPC sockets over either MPI or local Ethernet

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A Jupyter notebook demonstrating pulling a water geometry from the PubChem database⁵² and computing both Q‐Chem and Psi4 with the same input. Note the difference in absolute energies is primarily due to the fact that Q‐Chem uses direct SCF algorithms by default while Psi4 uses density‐fitting

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A Jupyter notebook showing a molecule object created from an XYZ string, the O‐H distance (Bohr) and HOH angle (degrees) measured, and the computation of a conversion factor for a gradient

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References

Jain, A, Ong, SP, Hautier, G, et al. The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 2013;1:011002.
Draxl, C, Scheffler, M. NOMAD: The FAIR concept for big data‐driven materials science. MRS Bull. 2018;43:676–682.
Toher, C, Oses, C, Hicks, D, et al. In: Andreoni, W, Yip, S, editors. Handbook of materials modeling: Methods: Theory and modeling. Cham: Springer International Publishing, 2018; p. 1–28.
https://www.iochem-bd.org/.
https://pqr.pitt.edu/.
Johnson, RD. NIST computational chemistry comparison and benchmark database.
Data mining uncovers a treasure trove of topological materials. Nature. 2019;566:425–425.
Thygesen, KS, Jacobsen, KW. Making the most of materials computations. Science. 2016;354:180–181.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and replicability in science. Washington, DC: The National Academies Press, 2019.
Widener, A. Scientists must take steps to improve reproducibility and reliability of research results. Chem Eng News. 2019;97:19.
Figshare: Store, discover, research for the current version, see https://figshare.com [accessed January 2020].
Zenodo: Research. Shared. For the current version, see https://zenodo.org [accessed January 2020].
Milham, MP. Data sharing and the future of science. Nat Commun. 2018;9:9–10.
Mobley,, D. L.; Bannan,, C. C.; Rizzi,, A.; Bayly,, C. I.; Chodera,, J. D.; Lim,, V. T.; Lim,, N. M.; Beauchamp,, K. A.; Shirts,, M. R.; Gilson,, M. K.; Eastman,, P. K. Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1. bioRxiv; 2018.
Jurečka, P, Šponer, J, Černý, J, Hobza, P. Benchmark database of accurate (MP2 and CCSD(T) complete basis set limit) interaction energies of Small model complexes, DNA base pairs, and amino acid pairs. Phys Chem Chem Phys. 2006;8:1985–1993.
Yuan, Y, Mills, MJL, Popelier, PLA, Jensen, F. Comprehensive analysis of energy minima of the 20 natural amino acids. Chem A Eur J. 2014;118:7876–7891. PMID: 25084473.
Smith, JS, Isayev, O, Roitberg, AE. ANI‐1: An extensible neural network potential with DFT accuracy at force field computational cost. Chem Sci. 2017;8:3192–3203.
Smith, JS, Isayev, O, Roitberg, AE. ANI‐1, A data set of 20 million calculated off‐equilibrium conformations for organic molecules. Sci Data. 2017;4:170193.
Ramakrishnan, R, Dral, PO, Rupp, M, Von Lilienfeld, OA. Quantum chemistry structures and properties of 134 kilo molecules. Sci Data. 2014;1:140022.
Wilkinson, MD, Dumontier, M, Aalbersberg, IJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3:160018.
Morgante, P, Peverati, R. ACCDB: A collection of chemistry databases for broad computational purposes. J Comput Chem. 2019;40:839–848.
Best Practices: MolSSI best practices provide a starting point to get into software development operations to ensure that your code is reliable and reproducible while decreasing long‐term maintenance requirements, increasing long‐term viability, and allowing others to work on your code base to assist your ow efforts. For the current version, see https://molssi.org/education/best-practices/ [accessed February 2020].
Cookiecutterfor Computational Molecular Sciences (CMS) Python Packages: A cookiecutter template for those interested in developing computational molecular packages in Python. Skeletal starting repositories can be created from this template to create the file structure semi‐autonomously so you can focus on what`s important: The science! For the current version, see https://github.com/MolSSI/cookiecutter-cms [accessed February 2020].
PyPI: Find, install and publish Python packages with the Python Package Index For the current version, see https://pypi.org [accessed February 2020].
Conda: Package, dependency and environment management for any language‐Python, R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN, and more. For the current version, see https://docs.conda.io/en/latest/ [accessed February 2020].
Docker: Securely build and share any application, anywhere For the current version, see https://www.docker.com [accessed February 2020].
Project Jupyter, Matthias Bussonnier, Jessica Forde, Jeremy Freeman, Brian Granger, Tim Head, Chris Holdgraf, Kyle Kelley, Gladys Nalvarte, Andrew Osheroff, Pacer, M. Yuvi Panda, Fernando Perez, Benjamin Ragan Kelley, Carol Willing, Binder 2.0 ‐ Reproducible, interactive, sharable environments for science at scale. Proceedings of the 17th Python in Science Conference. 2018; p. 113–120.
Google Colaboratory: Colaboratory, or “Colab” for short, allows you to write and execute Python in your browser. For the current version, see https://colab.research.google.com [accessed February 2020].
JSON SCHEMA: A vocabulary that allows you to annotate and validate JSON documents. For the current version, see https://json-schema.org/ [accessed January 2020].
Parrish, RM, Burns, LA, Smith, DGA, et al. Psi4 1.1: An open‐source electronic structure program emphasizing automation, advanced libraries, and interoperability. J Chem Theory Comput. 2017;13:3185–3197.
Smith, DGA, Burns, LA, Simmonett, AC, et al. Psi4 1.4: Open‐source software for high‐throughput quantum chemistry. J Chem Phys. 2020;152:184108.
Aprà, E, Bylaska, EJ, de Jong, WA, et al. NWChem: Past, present, and future. J Chem Phys. 2020;152:184102.
Wang, L‐P, Song, C. Geometry optimization made simple with translation and rotation coordinates. J Chem Phys. 2016;144:214108.
Wang, L‐P, Smith, DGA, Qiu, Y. Geometric: A geometry optimization code that includes the TRIC coordinate system. For the current version, see https://github.com/leeping/geomeTRIC [accessed January 2020].
Stanton, JF, Gauss, J, Cheng, L, Harding, ME, Matthews, DA, Szalay, PG. CFOUR, coupled‐cluster techniques for computational chemistry, a quantum‐chemical program package. With contributions from A.A. Auer, R.J. Bartlett, U. Benedikt, C. Berger, D.E. Bernholdt, Y.J. Bomble, O. Christiansen, F. Engel, R. Faber, M. Heckert, O. Heun, M. Hilgenberg, C. Huber, T.‐C. Jagau, D. Jonsson, J. Jusélius, T. Kirsch, K. Klein, W.J. Lauderdale, F. Lipparini, T. Metzroth, L.A. Mück, D.P. O`Neill, D.R. Price, E. Prochnow, C. Puzzarini, K. Ruud, F. Schiffmann, W. Schwalbach, C. Simmons, S. Stopkowicz, A. Tajti, J. Vázquez, F. Wang, J.D. Watts and the integral packages MOLECULE (J. Almlöf and P.R. Taylor), PROPS (P.R. Taylor), ABACUS (T. Helgaker, H.J. Aa. Jensen, P. Jørgensen, and J. Olsen), and ECP routines by A. V. Mitin and C. van Wüllen. For the current version, see http://www.cfour.de.
Manby, F, Miller, T, Bygrave, P, et al; entos: A Quantum Molecular Simulation Package. 2019. https://chemrxiv.org/articles/entos_A_Quantum_Molecular_Simulation_Package/7762646/2.
Barca, GMJ, Bertoni, C, Carrington, L, et al. Recent developments in the general atomic and molecular electronic structure system. J Chem Phys. 2020;152:154102.
Werner, H‐J, Knowles, PJ, Knizia, G, et al; MOLPRO, version 2019.2, a package of ab initio programs. 2019; see https://www.molpro.net.
Werner, H‐J, Knowles, PJ, Knizia, G, Manby, FR, Schütz, M. Molpro: A general‐purpose quantum chemistry program package. WIREs Comput Mol Sci. 2012;2:242–253.
Shao, Y, Gan, Z, et al. Advances in molecular quantum chemistry contained in the Q‐Chem 4 program package. Mol Phys. 2015;113:184–215.
Ufimtsev, IS, Martinez, TJ. Quantum chemistry on graphical processing units. 3. Analytical energy gradients, geometry optimization, and first principles molecular dynamics. J Chem Theory Comput. 2009;5:2619–2628.
Furche, F, Ahlrichs, R, Hättig, C, Klopper, W, Sierka, M, Weigend, F. Turbomole. WIREs Comput Mol Sci. 2014;4:91–100.
Stewart, JJP; MOPAC: Semiempirical quantum chemistry. For the current version, see http://OpenMOPAC.net/ [accessed January 2020] for Stewart computational chemistry, Colorado Springs, CO.
Gao, X; TorchANI: Accurate Neural Network Potential on PyTorch. For the current version, see https://github.com/aiqm/torchani [accessed January 2020].
Landrum,, G. RDKit: Cheminformatics and machine‐learning software in C++ and Python. For the current version, see https://doi.org/10.5281/zenodo.591637 [accessed January 2020].
Eastman, P, Swails, J, Chodera, JD, et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol. 2017;13:1–17.
Grimme, S, Antony, J, Ehrlich, S, Krieg, H; dftd3: Dispersion correction for DFT, Hartree–Fock, and semi‐empirical quantum chemical methods. For the current version, see https://github.com/loriab/dftd3 [accessed January 2020]. For the originating project, see https://www.chemie.uni-bonn.de/pctc/mulliken-center/software/dft-d3.
Grimme, S, Antony, J, Ehrlich, S, Krieg, H. A consistent and accurate Ab initio Parametrization of density functional dispersion correction (DFT‐D) for the 94 elements H‐Pu. J Chem Phys. 2010;132:154104.
Greenwell, C; MP2D: A program for calculating the MP2D dispersion energy. For the current version, see https://github.com/Chandemonium/MP2D [accessed January 2020].
Řezáč, J, Greenwell, C, Beran, GJO. Accurate noncovalent interactions via dispersion‐corrected second‐order Møller‐Plesset perturbation theory. J Chem Theory Comput. 2018;14:4711–4721.
Hermann, J; PyBerny: Molecular structure optimizer. For the current version, see https://github.com/jhrmnn/pyberny [accessed January 2020]. Also, for Version 0.6.2 https://doi.org/10.5281/zenodo.3695038.
Kim, S, Chen, J, Cheng, T, et al. Update: Improved access to chemical data. Nucleic Acids Res. 2019;2018(47):D1102–D1109.
PostgreSQL: The world`s most advanced open source relational database for the current version, see https://www.postgresql.org [accessed January 2020].
Rocklin, M. Dask: Parallel computation with blocked algorithms and task scheduling. Proceedings of the 14th Python in Science Conference. 2015; p. 130–136.
Babuji,, Y.; Woodard,, A.; Li,, Z.; Katz,, D. S.; Clifford,, B.; Kumar,, R.; Lacinski,, L.; Chard,, R.; Wozniak,, J.; Foster,, I.; Wilde,, M.; Chard,, K. Parsl: Pervasive parallel programming in Python. 28th ACM International Symposium on High‐Performance Parallel and Distributed Computing (HPDC). 2019.
Turilli,, M.; Merzky,, A.; Balasubramanian,, V.; Jha,, S. Building Blocks for Workflow System Middleware. Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2018; p. 348–349.
Jain, A, Ong, SP, Chen, W, et al. FireWorks: A dynamic workflow system designed for high‐throughput applications. Concurrency Comput Pract Exp. 2015;27:5037–5059. CPE‐14‐0307.R2.
Yoo, AB, Jette, MA, Grondona, M. SLURM: Simple linux utility for resource management. Job Scheduling Strategies for Parallel Processing. Berlin, Heidelberg: Springer, 2003; p. 44–60.
Qiu, Y, Smith DGA,, Stern, CD, Feng, M, Wang, L. Driving torsion scans with wavefront propagation, in preparation. https://doi.org/10.1063/5.0009232.
Torsiondrive: Dihedral scanner with wavefront propagation for the current version, see https://github.com/lpwgroup/torsiondrive [accessed January 2020].
McKinney, W. Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 2010; p. 56–61.http://doi.org/10.25080/Majora-92bf1922-00a.
Kluyver, T, Ragan‐Kelley, B, Pérez, F, et al. Jupyter Notebooks – A publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas. 2016; pp 87–90.
Nguyen, H, Case, DA, Rose, AS. NGLview‐interactive molecular graphics for Jupyter notebooks. Bioinformatics. 2017;34:1241–1242.
Halgren, TA. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem. 1996;17:490–519.
Smith, JS, Nebgen, B, Lubbers, N, Isayev, O, Roitberg, AE. Less is more: Sampling chemical space with active learning. J Chem Phys. 2018;148:241733.
Becke, AD. Density‐functional thermochemistry. III. The role of exact exchange. J Chem Phys. 1993;98:5648–5652.
Lee, C, Yang, W, Parr, RG. Development of the Colle‐Salvetti correlation‐energy formula into a functional of the electron density. Phys Rev B. 1988;37:785–789.
Weigend, F, Ahlrichs, R. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. Phys. Chem. Chem. Phys. 2005;7:3297–3305.
Developers, Q. QCFractal Eamples: QCFractal quickstart guide. For the current version, see https://docs.qcarchive.molssi.org/projects/QCFractal/en/stable/quickstart.html [accessed April 2020].
Flask: Web development, one drop at a time for the current version, see https://flask.palletsprojects.com/en/1.1.x/ [accessed January 2020].
Dash: Build beautiful, web‐based analytic apps. No JavaScript required. For the current version, see https://plot.ly/dash/ [accessed January 2020].
Wilkins‐Diehr, N, Zentner, M, Pierce, M, et al. The science gateways community institute at two years. New York, NY: Proceedings of the Practice and Experience on Advanced Research Computing, 2018.
Nakata, M, Shimazaki, T. PubChemQC project: A large‐scale first‐principles electronic structure database for data‐driven chemistry. J Chem Inf Model. 2017;57:1300–1308.
Glavatskikh, M, Leguy, J, Hunault, G, Cauchy, T, Da Mota, B. Dataset`s chemical diversity limits the generalizability of machine learning predictions. J Chem. 2019;11:69.
Schütt, KT, Arbabzadah, F, Chmiela, S, Müller, KR, Tkatchenko, A. Quantum‐chemical insights from deep tensor neural networks. Nat Commun. 2017;8:13890.
Schütt, K, Kindermans, P‐J, Sauceda Felix, HE, Chmiela, S, Tkatchenko, A, Müller, K‐R. In: Guyon, I, Luxburg, UV, Bengio, S, et al., editors. Advances in neural information processing systems 30. Red Hook, NY: Curran Associates, Inc., 2017; p. 991–1001. https://www.printingnews.com/home/company/10284215/curran-associates-inc.
Marshall, MS, Burns, LA, Sherrill, CD. Basis set convergence of the coupled‐cluster correction, $δ_{MP 2}^{CCSD (T)}$ : Best practices for benchmarking non‐covalent interactions and the attendant revision of the S22, NBC10, HBC6, and HSG databases. J Chem Phys. 2011;135:194102.
Faver, JC, Benson, ML, He, X, et al. Formal estimation of errors in computed absolute interaction energies of protein‐ligand complexes. J Chem Theory Comput. 2011;7:790–797.
Gruzman, D, Karton, A, Martin, JML. Performance of ab initio and density functional methods for conformational equilibria of C_nH_2n+2 alkane isomers (n = 4–8). J Phys Chem A. 2009;113:11974–11983.
Zhao, Y, González‐García, N, Truhlar, DG. Benchmark database of barrier heights for heavy atom transfer, nucleophilic substitution, association, and unimolecular reactions and its use to test theoretical methods. J Phys Chem A. 2005;109:2012–2018.
Burns, LA, Faver, JC, Zheng, Z, et al. The BioFragment database (BFDb): An open‐data platform for computational chemistry analysis of noncovalent interactions. J Chem Phys. 2017;147:161727.

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