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

Join this webinar to see how highly efficient software such as VASP, unprecedented compute power, and intuitive multiscale modeling have made computational materials science central to R&D. Learn how these tools can optimize electronic devices, engineer organic–inorganic interfaces, unravel reaction mechanisms, study hydrogen diffusion, tailor polymer properties, and predict stress in PVD-grown thin films.

Join this webinar and explore how MLPs extend DFT accuracy to large, complex systems and long time scales. Learn how MedeA’s MLP Generator streamlines training, enables advanced materials-property predictions, and supports real-world research in corrosion, phase stability, defects, and catalysis. Ideal for scientists and engineers in multiscale modeling.

Upcoming MedeA VASP Training Materials Design announces an online training session featuring MedeA, the industry-leading atomistic...

Materials Design presents an interview with a pioneer in computational materials design: Prof. Gregory B. Olson

MedeA VASP Training: Experience the ease with which atomistic models may be constructed and VASP simulations may be launched, monitored, and

University of Kazan Develops Highly Accurate Machine-Learned Potential (MLP) for Aluminum Using n2p2 Methodology in MedeA MLPG

Webinar: From the Femtoscale to the Mesoscale and Back: An Integrated Multiscale Approach #engineering #compchem

The partnership will connect MedeA Environment outputs to ICMD® models to provide common clients with the most accurate predictive and presc

Upcoming Webinar Polyvalent Machine-Learned Potential for Cobalt: from Bulk to Nanoparticles In this webinar we describe the development...

UGM Plenary Speaker Spotlight Tuesday, we host Dr. Carla Verdi; Faculty at University of Vienna, Austria and Dr. Georg Kresse; Professor...

UGM MedeA Training Generating and Applying Machine-Learned Potentials with MedeA Instructor: Dr. David Reith Density functional theory...

UGM Plenary Speaker Spotlight This week, we host Jörg Behler; Full Professor of Theoretical Chemistry University of Göttingen, Germany...

Jeffrey Grossman; Department Head of Materials Science and Engineering at the Massachusetts Institute of Technology

Radionuclide Sequestration in MOFs: DFT Method Exploration and a Conceptualization of Graph Neural Networks This webinar will be focused...