Webinar: Precision at Scale with Machine-Learned Potentials

Katherine Hollingsworth
12 minutes ago
2 min read

Webinar: Advancing Molecular-Scale Modeling: A Novel Approach for Semicrystalline Polymers

Machine-learned interatomic potentials (MLPs) have become an indispensable and central part of multiscale modeling by bridging the gap between ab-initio and phase-field approaches. While inheriting the accuracy of DFT from calculations for comprehensive sets of training structures these potentials offer unprecedented capabilities to investigate large and complex atomic structures at long time scales. Thereby they open the door to materials properties and phenomena, which reach beyond the limitations of DFT methods with respect to system sizes and time scales, and at the same time provide a basis for continuum approaches to materials. Here we demonstrate the full integration of MLPs in the MedeA software environment combining efficient ways for full-scale training-set calculations with the MLP Generator to provide potentials for direct use within MedeA. This opens a plethora of capabilities for materials property calculations extending beyond the calculation of energies and forces. The presentation will showcase new results obtained from the latest GRACE potentials for highly accurate electronic properties.

What You Will Learn:

1. How Machine-Learned Potentials can be generated using the high-throughput capabilities and the MLP Generator of #MedeA

2. How MLPs can be used within the MedeA software environment to access relevant materials properties at large length and time scales

3. How MLPs can be used to perform real-world materials research including corrosion, phase stability, defect properties, and catalysis

Who Should Attend:

This webinar is ideal for materials scientists, computational modelers, mechanical and chemical engineers, and researchers working in microstructure evolution, corrosion science, thin film growth, phase transformations, or multiscale modeling. Anyone interested in connecting atomistic and microstructure-scale behavior to better understand and predict material performance over realistic length and time scales will benefit from attending.