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MedeA Environment

The Most Comprehensive Atomistic Modeling and Simulation Software for Materials Science

MedeA is the leading environment for the atomistic simulation of materials. MedeA enables professional, day-to-day deployment of atomic-scale and nano-scale computations for materials engineering, materials optimization and materials discovery. In MedeA, world-class simulation engines are integrated with elaborate property prediction modules, experimental databases, structure builders and analysis tools, all in one user-friendly environment.

What's New
Trusted by thousands of users in over 700 commercial, government, and academic institutions.
Cheering Crowd

Computational material science tools have revolutionized the evaluation of neutron thermal scattering laws.  All of the new thermal scattering laws including in the new US national ENDF/B-VIII.0 nuclear data library were developed using DFT or MD simulations.  The vast majority were developed by MedeA users using VASP, PHONON, and LAMMPS.
-Michael L. Zerkle, Ph.D., Senior Advisor,
Reactor Physics Methods Development,
Naval Nuclear Laboratory
“I like MedeA, it gives me more time to think.”

-Ryoji Asahi 

Toyota Central Research and Development Laboratories, Nagoya, Japan

We are currently working with industrial partners to improve materials used in photodetectors. MedeA is ideal for what we need, as it allows me to study a wide range of material properties. The interface allows me to simulate what I want to, and the software comes with lots of built in materials which is really helpful. The MedeA support team is also excellent, in case of any problems. I highly recommend MedeA! 

-Dr Jamie Williams, Post Doctoral Research Associate, Department of Physics and Astronomy, 
University of Leicester, United Kingdom

What's New

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

This webinar will provide insight into how the properties of semicrystalline material structures relate to their molecular morphologies.


To start us off, Dr Belin will present a novel approach for generating semicrystalline material models using Materials Design's MedeA tool suite. Next, he will demonstrate the computation of mechanical properties of the structures generated to explore how the properties of these structures relate to their molecular morphologies.

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Machine-learned interatomic potentials: Recent developments and prospective applications

AI and machine learning are penetrating materials computer simulations at a staggering pace. In particular, high-throughput generation of large, consistent, and accurate ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting materials research: (i) while classical interatomic potentials have become indispensable in atomistic simulations, such potentials are typically restricted to certain classes of materials. Machine-learned potentials (MLPs) are applicable to all classes of materials individually and, importantly, to any combinations of them; (ii) MLPs are by design reactive force fields...

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A Report on the Advanced Simulation Engineering Tool

A recent report details the extraordinary work carried out at the Idaho National Laboratory (INL) High Performance Computing (HPC) facility. In excess of one billion core hours, delivered in less than 12 months, provided by this leading supercomputer facility have driven radically improved understanding in nuclear materials, energy storage, andrenewable energy. A highlight of the report is the contribution of Jonathan Wormald, Richard Sm...

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New Application Note: The structural Phase Transition in Ti Investigated Using a Machine-learned Interatomic Potential

This application note describes the generation of a machine-learned potential (MLP) using the highly automated MedeA Machine-Learned Potential Generator (MLPG). Our MLPG inputs a training set consisting of results from a set of MedeA VASP calculations selected by the user and generates a machine-learned potential for use with MedeA LAMMPS...

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