Interactive data visualization for materials and molecular databases. Correlate atomic structures and their properties, either online, in a jupyter notebook, or with a portable app.

Universal interatomic potentials for advanced materials modeling - lab-cosmo/upet

In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit w

In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit w

Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce

Researchers at EPFL’s Laboratory of Computational Science and Modeling (COSMO) have reached a significant milestone in material science, reaching the top position on Matbench Discovery, the leading be...

The Pioneer Center CAPeX at DTU has announced the winners of the first phase (Stage 1) an international competition in partnership with the Novo Nordisk Foundation, and the Danish Centre for AI Innovation (DCAI), on using machine learning models to predict synthesis recipes for novel nanoparticles.

The Swiss Confederation launches six new National Centres of Competence in Research (NCCRs). The NCCR “Separations”, which aims to accelerate research in separation sciences - the quest for chemical a...

Universal interatomic potentials for advanced materials modeling - lab-cosmo/upet