Discover how automated high-throughput Molecular Dynamics (MD) and Monte Carlo (MC) workflows enable accurate prediction of gas sorption, isotherms, and polymer swelling in industrial polymer systems. This webinar demonstrates how molecular simulations accelerate material selection, membrane and packaging design, and process optimization while reducing experimental screening effort and time-to-results.

Recent advances in computational materials science are increasingly driven by multiscale modeling, machine learning, and major improvements in theoretical frameworks and computational performance. Artificial intelligence plays an ever more central role in materials discovery and optimization by enabling systematic screening, accelerated exploration of configurational space, and quantitative property prediction. However, machine learning approaches rely on high‑quality, materi

ACEworks  and Materials Design are rolling forward their successful collaboration with the full integration of the GRACEmaker code of ACEworks in the MedeA  computational environment. The GRACEmaker code is based on the Graph Atomic Cluster Expansion (GRACE), one of the most advanced methods for the generation of Machine-Learned Potentials (MLPs). MedeA 3.12  provides comprehensive support of the leading GRACE 1L and 2L potentials in MedeA  LAMMPS . This includes access to t

The shift from physical laboratories to in-silico research has transformed materials science, with the Vienna Ab initio Simulation Package (VASP) at its core. As the world’s most widely used density functional theory (DFT) code, VASP drives digital twins, machine learning, and GPU-accelerated simulations on leading supercomputers. MedeA uniquely integrates VASP with workflow automation, job management, and analysis, enabling productive, reproducible, and scalable materials di