RXMD is a linear scalable parallel software for reactive molecular dynamics (RMD) based on the first principles-informed reactive force-fields (ReaxFF). RMD follows the time evolution of atomic trajectories, where ReaxFF describes chemical bond breakage and formation based on a reactive bond-order concept and charge transfer based on a charge-equilibration approach.
A scalable parallel software for quantum molecular dynamics (QMD) with various extensions (X), where X currently supported include adiabatic and non-adiabatic (NA). QMD follows the trajectories of all atoms, while interatomic forces are computed quantum mechanically based on density functional theory (DFT) with a plane-wave basis and pseudopotential formalism. NAQMD describes electronic excitations using the linear-response time-dependent DFT (LR-TDDFT). Transitions between excited electronic states are treated using surface hopping algorithm. Photo-excitation is described as a non-adiabatic process that involves electronic transitions and coupled nuclear motion.
QXMD documentation can be found here
Game-Engine-assisted Research platform for Scientific Computing is a hardware-agnostic workflow that leverages game engines to adapt scientific visualization and simulation techniques for Virtual Reality (VR). It also supports multiple game engines and programming languages, and incorporates existing libraries to allow researchers to directly port existing codebases to VR. Additionally, the platform integrates a number of software tools that take advantage of VR’s enhanced functionality for scientific computing.
Thermal conductivity tools is a set of plugins for thermal properties — velocity autocorrelation functions, phonon density of states, specific heat and thermal conductivity with isotopic mass distribution — using LAMMPS.
Our software models the ultrafast control of emergent magnetism by THz radiation in Re doped MoSe2 through a first-principles informed quantum Ising model. We have provided the full code and step-by-step tutorials for performing such simulations on each quantum processor in the form of python notebooks.
The python-based machine learning module identifies and classifies different point and extended defects in crystal structure data generated by atomistic simulations, by learning information about local atomic structure.
Researchers at the MAGICS Center, working on topics ranging from experimental and computational synthesis, to active learning for accelerated design of materials, to UED experiments on photo-induced lattice dynamics, have generated a large amount of computational and experimental data on layered materials.
1. Layered Heterostructures on Materials Project Database
2. Optimization of First Principles-Informed Reactive Force Fields for Computational Synthesis of Layered Materials
Published on September 7th, 2017
Last updated on August 3rd, 2020