A package that organizes compositions written as strings into pandas.DataFrames, or conversely, generates compositions from mixing ratios by using only numpy and pandas.
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Updated
Apr 15, 2022 - Python
A package that organizes compositions written as strings into pandas.DataFrames, or conversely, generates compositions from mixing ratios by using only numpy and pandas.
A Machine Learning Python package for materials informatics.
Basic info and tutorials for the GUI of the Materials Platform for Data Science (MPDS)
Capstone project for my Master's degree. In it, I developed some machine learning models to predict the heat of formation for materials containing 1–3 components.
Inversion Symmetry-aware Directional PaiNN
Workflow for generating formulas of chemically novel superconductors
A simple composition-based feature vectorization utility in Julia
Probabilistic machine learning for reconstruction and parametrization of electronic band sturcture from photoemission spectroscopy data
Repository for my senior design project/thesis code
Material simulation tutorial on DFT for high school students to find alternative, green energy source to fossil fuels.
Generative model variational autoencoder (VAE) implementation for the predictions of phase separation in binary alloys
An interactive guide to using hidden layer embeddings as descriptors of materials
Periodic Table of Elemental Nobility / Reactivity
Data and code for the publication "Testing the Limits of the Global Instability Index"
Phonons from ML force fields
Computational experiments using SMACT for materials design
Reproducing classical machine learning models for predicting the heat capacity of solid inorganics based on Anthony Wang BestPractices repository.
A Python-Lammps implementation for Materials Optimization and Design
DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials
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