This lecture will address the impact of materials design in selected representative applications.

This training session will focus on an atomic scale approach to simulate a pressure-driven gas separation process involving a nanoporous polymer membrane.

In this talk, the evolution of MLPs will be illustrated using high-dimensional neural networks, an important and frequently used type of MLPs. Some examples concerning their applicability will be presented.

This session introduces MedeA's Machine-Learned Potential Generator (MLPG), which allows to generate MLPs in a highly efficient manner for a given set of training structures.

This talk will discuss the on-the-fly learning technique implemented in VASP, based on molecular dynamics and Bayesian
inference, and a Δ-machine learning approach.

This UGM training session will demonstrate the recent machine learning force field (MLFF) capability using MedeA VASP.

In this interview, Dr. Bicerano will tell us about his career, his experience in industry, and the growing impact of computer simulations.

We will demonstrate how to build both atomistic and coarse-grained models of polymers using the amorphous builder, and how to run simulations with the LAMMPS and Gibbs molecular dynamics and Monte Carlo codes.