Datasets

Mesoscopic simulations of full supercapacitors using pystencils in EESSI

(Céline Merlet, University of Toulouse)

Final questions and answers

Supporting waLBerla applications in EESSI

(Alan O`Cais, University of Barcelona)

Usage of waLBerla for simulation of turbulent flows

(Matteo Zanfrognini, LEONARDO)

Performance Portability and Scalability of Codes: Kokkos and ALL

(Rodrigo Bartolomeu, Jülich Supercomputing Centre)

Digital twin for ultrasound through OBMD plugin for LAMMPS

(Tilen PotiskNational Institute of Chemistry)

Questions and answers

EESSI CI/CD services for ESPResSo and pyMBE

(Alan O`Cais, University of Barcelona)

Improving the Scalability of Energy Materials Simulations in ESPResSo

(Jean-Noël Grad, University of Stuttgart)

Welcome message and introduction to EESSI

(Kenneth Hoste, Ghent University)

Role of computational methods in the era of precision medicine

Computational drug discovery has become an increasingly important tool in the search for new drugs to treat a wide range of diseases. One of the critical challenges in this field is simulating the complex behavior of biomolecules at the atomic level, which can be computationally very expensive and time-consuming. The thermodynamics and kinetics of drug-target binding play a crucial role in understanding and optimizing the interactions between drugs and their target molecules. To address this challenge, molecular dynamics (MD) and related approaches combined with machine learning and artificial intelligence play a crucial role in accelerating the exploration of the conformational space of molecules. These methods also enable researchers to compute the free energy differences between different states of a molecule and estimate thermodynamic and kinetics properties such as binding free energies and residence time. This talk overviews various enhanced sampling methods combined with machine learning in computational drug discovery, as well as their advantages and limitations. The talk focuses on using these methods for free energy and kinetics estimations, reporting on the significant limitations toward accurate estimations for large datasets of compounds. The talk then describes recent applications in drug discovery and discusses the challenges and limitations of these methodologies when applied to biological systems of increasing complexity. In conclusion, the talk illustrates how MD, enhanced sampling methods, and machine learning can improve the efficiency and effectiveness of computational drug discovery and accelerate the development of new therapeutics to treat significant unmet medical needs in the era of precision medicine.

Data generation and uncertainty quantification for universal graph deep learning interatomic potentials

One of the most exciting developments in recent years is the development of graph deep learning interatomic potentials such as the Materials 3-Body Graph Network (M3GNet), that have near-universal coverage across the entire periodic table. Such universal interatomic potentials (UIPs) have broad applications in the dynamic simulations and discovery of materials. However, current UIPs are still hampered by the lack of accurate potential energy surface (PES) data. In this talk, I will discuss these limitations and how advanced sampling approaches based M3GNet latent structural features can be used to generate high-quality PES data in an efficient manner. In addition, I will show that these approaches can also be used to quantify the uncertainty of a UIP for a structure. I will conclude with some observations on the relative performance of invariant versus equivariant UIP architectures, and a perspective on the challenges involved in UIPs with additional physics such as charges and magnetism.