From
12
Nov
Seminars and Conferences
Atomistic simulations for energy related materials with machine learning-based interatomic potentials
The growing demand for clean energy sources and efficient energy storage systems - such as hydrogen - highlights the crucial importance of characterizing materials at the atomistic scale to enhance performance, particularly under operando conditions.
While ab initio atomistic simulations provide a powerful theoretical framework for studying atomic-scale dynamics, their computational cost often limits their application to realistic systems.
Recently, Machine Learning (ML)-based interatomic potentials have emerged as an innovative solution, bridging the gap between ab initio accuracy and classical force field efficiency.
Trained on extensive quantum mechanical datasets, these potentials enable simulations of larger systems over longer timescales, while maintaining high precision.
The short course “Atomistic Simulations for Energy-Related Materials with Machine Learning-Based Interatomic Potentials” will be a PhD-level intensive course held by Umberto Raucci and Francesco Mambretti (Parrinello Group).
The course provides a comprehensive overview of the most advanced methodologies in the field, even for participants with limited prior experience in molecular simulations.
Topics will include
The lectures will take place at Politecnico di Torino as follows:
Those wishing to attend are invited to contact professor Matteo Fasano at matteo.fasano@polito.it.
While ab initio atomistic simulations provide a powerful theoretical framework for studying atomic-scale dynamics, their computational cost often limits their application to realistic systems.
Recently, Machine Learning (ML)-based interatomic potentials have emerged as an innovative solution, bridging the gap between ab initio accuracy and classical force field efficiency.
Trained on extensive quantum mechanical datasets, these potentials enable simulations of larger systems over longer timescales, while maintaining high precision.
The short course “Atomistic Simulations for Energy-Related Materials with Machine Learning-Based Interatomic Potentials” will be a PhD-level intensive course held by Umberto Raucci and Francesco Mambretti (Parrinello Group).
The course provides a comprehensive overview of the most advanced methodologies in the field, even for participants with limited prior experience in molecular simulations.
Topics will include
- Training ML interatomic potentials and data collection
- Comparison of different ML approaches
- Case studies on structural dynamics, chemical kinetics, and transport phenomena in energy systems
- Practical examples such as temperature effects on cathode materials and catalyst-driven chemical reactions
The lectures will take place at Politecnico di Torino as follows:
- Wednesday, November 12, 14:30 – 17:30 | DENERG, MULTIMEDIA meeting room
- Thursday, November 13, 09:30 – 12:30 | DENERG, MULTIMEDIA meeting room
- Friday, November 14, 09:30 – 12:30 | DENERG, MORELLI meeting room
Those wishing to attend are invited to contact professor Matteo Fasano at matteo.fasano@polito.it.