Materials Design announces MedeA 3.12, materials simulation environment, delivering a revolutionary integration of machine learning capabilities that transform materials modeling workflows. The MedeA 3.12 release establishes a comprehensive machine-learned potential ecosystem spanning training, refinement, deployment, and analysis, while introducing powerful new builders for complex microstructures and enhanced tools for materials discovery.

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.

Materials Design announces MedeA 3.11, delivering significant performance improvements and expanded capabilities to accelerate materials modeling and simulation workflows. The MedeA 3.11 release enhances visualization performance, upgrades key computational engines, and introduces new analysis tools to meet the evolving needs of materials scientists and engineers.

The study explores the use of tantalum (Ta) doping to improve the performance and stability of LiNi0.80Mn0.1Co0.1O2 (NMC811) cathode mate...

MedeA VASP makes it easy to analyze and understand chemical bonding. By leveraging the integrated MedeA Environment you can quantify the...

At-a-Glance: MedeA Flowcharts make it easy to design and conduct systematic computational materials science studies with a visual...

MedeA 3.10 offers an integrated suite of multiscale modeling capabilities with advanced features that empower researchers to explore and des

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

Polypropylene straws survive in the environment for decades, endanger wildlife, and eventually release harmful chemicals as they decompose.

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

Understanding and Predicting Hydrogen Embrittlement in Metals Demonstrates the Predictive Power of MLPs for Complex Metallurgical Phenomena

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

Accelerating Materials Innovation: Connecting Quantum Simulations, Machine Learning, and Mesoscale Modeling to Speed Up Materials Research

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...

Upcoming Webinar Sorption and Diffusion of Small Gas Molecules in Semicrystalline Models: A Molecular-Scale Investigation In a recent...

Webinar: Advancing Molecular-Scale Modeling: A Novel Approach for Semicrystalline Polymers

AI and machine learning are penetrating materials computer simulations at a staggering pace. In particular, high-throughput generation of...