Physics accuracy and machine learning towards the next-generation of molecular potentials

The UNIWERSYTET MIKOLAJA KOPERNIKA in Belgium from Europe region has released this tender for Physics accuracy and machine learning towards the next-generation of molecular potentials Weak intermolecular interactions are the driving force of relevant processes including protein folding charge transfer through solid state junctions in solar cells and collisions in interstellar gas clouds Understanding the nature of these forces is essential to achieve better control and optimization of these processes Nowadays modelling has become an essential tool for unravelling experiments and aiding materials design However the accurate description of weak interactions is plenty of challenges for many theoretical approaches Atomistic simulations can help decipher the mechanisms underlying complex processes nonetheless precise molecular potentials are required to achieve a quantitative and qualitative description of these physical-chemical processes Despite the importance of this field there is at present no systematic and comprehensive training in this subject particularly in the fundamentals of the theory method development techniques to rigorously test the theory using experimental data and the creation of new models using detailed physical understanding combined with machine-learning The PHYMOL network aims to fill in this fill this gap by bringing together a group of experts in the field of chemical physics intermolecular interactions simulation and machine-learning with several industrial and academic partners This program will exploit the synergies between an accurate description of intermolecular interactions and the use of machine learning techniques towards the generation of new molecular potentials Combining scientific investigation with an intensive training program PHYMOL will help shape a new generation of researchers with the capability of developing accurate molecular potentials impacting a broad range of applications with tender notice no. 101073474 published on 10 Aug 2026. The tender is due to expire on 31 Jan 2027. This tender is sourced from Cordis and is eligible for suppliers interested in E-learning services. Interested bidders can obtain further information including complete bid documents or ways to participate by registering on Tender Impulse and referencing via Tender Impulse ID 14279989.