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.
Trusted by thousands of users in over 700 commercial, government, and academic institutions.
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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
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What's New
Polyvalent Machine-Learned Potential for Cobalt: from Bulk to Nanoparticles
In this webinar we describe the development of a highly accurate machine-learned potential (MLP) for Co, enabling simulations of large models of bulk material, surfaces, and nanoclusters over extended time scales across a wide range of temperatures and pressures. While non-magnetic itself, the MLP is trained on several thousand spin-polarized ab initio computations performed using MedeA VASP. The resulting MLP closely reproduces the phonon dispersions of hexagonal close-packed (hcp) and face-centered cubic (fcc) Co, Co surface energies, and the relative stabilities of Co nanoparticles of various shapes. The thermal expansion coefficient and the melting temperature of Co computed with this MLP are close to experimental values. Furthermore, this MLP captures nuanced material properties such as vacancy formation energies on nanoparticle vertices. This accuracy and versatility make the potential suitable for a wide array of applications, including modeling the geometry of Co catalytic surfaces.
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...
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...
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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