Streamline Computational Workflows with Python in the Amsterdam Modeling Suite
Part 5 of our AMS Weekly Video Series
This video demonstrates how AMS seamlessly connects GUI-based materials simulations with the power and flexibility of Python scripting.
By exporting existing calculations to PLAMS scripts and working inside Jupyter Lab, researchers can automate repetitive tasks, run high-throughput studies, and integrate analysis directly into their workflow—all while retaining the user-friendly setup tools of the AMS graphical interface. Jobs can be submitted to local machines or HPC clusters with just a few clicks, enabling scalable and efficient compute.
As an example, the workflow focuses on lithium-ion battery cathode materials based on layered lithium cobalt oxide. The intercalation potential is a key property that impacts cell voltage and energy density. Starting from a geometry optimization using accurate yet efficient machine-learning potentials (M3GNet with D3 dispersion), the calculation is exported from the GUI and used as a template for a fully featured Jupyter notebook.
With simple Python functions for atomic substitution and delithiation, entire families of related cathode chemistries are generated and simulated automatically through concise loops. Running these jobs in parallel showcases the speed of machine-learning potentials and the power of scripted batch execution in AMS.
Results are instantly processed with the Python ecosystem—compiled into tables using Pandas and visualized through interactive plots to reveal materials trends at a glance. Finally, the notebook is submitted directly to an HPC cluster from the AMS Jobs interface and synchronized back once complete, connecting development, automation, and compute into a unified workflow.
This approach empowers researchers to accelerate screening and design of new materials, combining the flexibility of Python with the reliability and ease-of-use of the Amsterdam Modeling Suite.
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