MolSSI Education: Empowering the Next Generation of Computational Molecular Scientists

Nash, Jessica A.; Mostafanejad, Mohammad; Crawford, T. Daniel; McDonald, Ashley Ringer

doi:10.1109/mcse.2022.3165607

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…Junior faculty expressed a desire for more teaching mentorship and a shared repository of resources, while senior faculty added the need for professional development workshops focused on modernizing the physical chemistry curriculum. Several participants highlighted the work and progress made by current microgroups in physical chemistry (e.g., POGIL–PCL, ,, PIPER, the ESCIP project, − the MERCURY consortium, and MolSSI Education , ), yet it became clear that not everyone was aware of these resources, and all agreed that it would be helpful to create a centralized location to connect groups with each other and to the broader community of physical chemistry instructors.…”

Section: What We Have Learned So Farmentioning

confidence: 99%

Lowering Activation Barriers to Success in Physical Chemistry (LABSIP): A Community Project

Baiz,

Berger,

Donald

et al. 2023

J. Phys. Chem. A

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Section: What We Have Learned So Farmentioning

confidence: 99%

Lowering Activation Barriers to Success in Physical Chemistry (LABSIP): A Community Project

Baiz,

Berger,

Donald

et al. 2023

J. Phys. Chem. A

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Deep learning algorithms applied to computational chemistry

Guzman-Pando,

Ramirez-Alonso,

Arzate-Quintana

et al. 2023

Mol Divers

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Cookiecutter for Computational Molecular Sciences: A Best Practices Ready Python Project Generator

Naden,

Nash,

Crawford

et al. 2024

J. Chem. Educ.

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Scientific software development takes far more than good programming abilities and scientific reasoning. Concepts such as version control, continuous integration, packaging, deployment, automatic documentation compiling, licensing, and even file structure are not traditionally taught to scientific programmers. The skill gap leads to inconsistent code quality and difficulty deploying products to the broader audience. Most of the implementation of these skills however can be constructed at project inception. The Cookiecutter for Computational Molecular Sciences generates ready-to-go Python projects that incorporate all of the concepts above from a single command. The final product is then a software project which lets developers focus on the science and minimizes worries about nonscientific and nonprogramming concepts because the best practices, as established by the Molecular Sciences Software Institute, have already been incorporated for them. This is a community driven project with widespread adoption across the computational molecular sciences. The Molecular Sciences Software Institute and Computational Molecular Sciences community also continually contribute and update the Cookiecutter for Computational Molecular Science, ensuring that the project is responsive to community needs and tool updates. All are welcome to suggest changes and contribute to making this the best starting point for Python-based scientific code.

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MolSSI Education: Empowering the Next Generation of Computational Molecular Scientists

Cited by 6 publications

References 4 publications

Lowering Activation Barriers to Success in Physical Chemistry (LABSIP): A Community Project

Lowering Activation Barriers to Success in Physical Chemistry (LABSIP): A Community Project

Deep learning algorithms applied to computational chemistry

Cookiecutter for Computational Molecular Sciences: A Best Practices Ready Python Project Generator

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