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The MAterials Simulation Toolkit (MAST) is a workflow manager and post-processing tool for ab initio defect and diffusion workflows. MAST codifies research knowledge and best practices for such workflows, and allows for the generation and management of easily modified and reproducible workflows, where data is stored along with workflow information for data provenance tracking. MAST is open-source and available for download (see PDF for links).
As data science and machine learning methods are taking on an increasingly important role in the materials research community, there is a need for the development of machine learning software tools that are easy to use (even for nonexperts with no pr
MAST-SEY is an open-source Monte Carlo code capable of calculating secondary electron emission using input data generated entirely from first principle (density functional theory) calculations. It utilizes the complex dielectric function and Penns th
Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Materials Science is that experimental datasets of material properties te
Driven by the unprecedented computational power available to scientific research, the use of computers in solid-state physics, chemistry and materials science has been on a continuous rise. This review focuses on the software used for the simulation
Graph neural networks (GNN) have been shown to provide substantial performance improvements for representing and modeling atomistic materials compared with descriptor-based machine-learning models. While most existing GNN models for atomistic predict