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Abbott, Adam S.; Turney, Justin M.; Zhang, Boyi; Smith, Daniel G.; Altarawy, Doaa; Schaefer, Henry F. (, Journal of Chemical Theory and Computation)
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Krylov, Anna; Windus, Theresa L; Barnes, Taylor; Marin-Rimoldi, Eliseo; Nash, Jessica A; Pritchard, Benjamin; Smith, Daniel_G A; Altarawy, Doaa; Saxe, Paul; Clementi, Cecilia; et al (, The Journal of Chemical Physics)The field of computational molecular sciences (CMSs) has made innumerable contributions to the understanding of the molecular phenomena that underlie and control chemical processes, which is manifested in a large number of community software projects and codes. The CMS community is now poised to take the next transformative steps of better training in modern software design and engineering methods and tools, increasing interoperability through more systematic adoption of agreed upon standards and accepted best-practices, overcoming unnecessary redundancy in software effort along with greater reproducibility, and increasing the deployment of new software onto hardware platforms from in-house clusters to mid-range computing systems through to modern supercomputers. This in turn will have future impact on the software that will be created to address grand challenge science that we illustrate here: the formulation of diverse catalysts, descriptions of long-range charge and excitation transfer, and development of structural ensembles for intrinsically disordered proteins.more » « less
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Haghighatlari, Mojtaba; Vishwakarma, Gaurav; Altarawy, Doaa; Subramanian, Ramachandran; Kota, Bhargava U.; Sonpal, Aditya; Setlur, Srirangaraj; Hachmann, Johannes (, WIREs Computational Molecular Science)Abstract ChemMLis an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain.ChemMLallows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community.ChemMLis also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under:Software > Simulation MethodsComputer and Information Science > ChemoinformaticsStructure and Mechanism > Computational Materials ScienceSoftware > Molecular Modelingmore » « less
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