Dynamic effects of catalysts play a crucial role in catalytic reactions, necessitating the incorporation of statistical sampling and understanding of the impact of dynamic structures in free energy calculations. However, the complexity of catalytic systems poses challenges in effectively exploring the vast configurational space effectively. In this work, we propose CatFlow, an automated workflow for training machine learning potentials (MLPs) to compute free energies of catalytic reactions. CatFlow combines constrained molecular dynamics (MD) simulation with concurrent training of MLPs and sequential calculation of free energies with well trained MLPs. By rapidly generating reliable MLPs, CatFlow facilitates rigorous free energy calculations, enabling the determination of the reaction profiles in an end-to-end manner. We showcased the capabilities of CatFlow by investigating the activation of O2 catalyzed by Pt clusters and demonstrated the effects of phase transition on the activities of the catalytic reaction. CatFlow offers an efficient and automated solution for studying the catalytic elementary reaction processes. It reduces the need for human intervention and provides researchers with a powerful tool to investigate free energies of dynamic catalysis.

This study introduces a2c, a computational method that leverages machine learning and atomistic simulations to predict the most likely crystallization products upon annealing of amorphous precursors. ...

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.