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.
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Updated
Jul 14, 2022 - R
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
A simple composition-based feature vectorization utility in Julia
Fitting module. Used for establishing functions/linkages between material descriptors and process or property parameters.
Data about chemical elements arranged in JSON format
A Deep Learning Model For Homogenization of Two-Phase High-Contrast Three-dimensional Materials
Reproducing classical machine learning models for predicting the heat capacity of solid inorganics based on Anthony Wang BestPractices repository.
SynCheck is a web interface for the Text Mining Pipeline that processes synthesis text, extracts information about targets, precursors and synthesis operations, and constructs synthesis graph.
Blogging using Pluto.jl notebooks.
A Python-Lammps implementation for Materials Optimization and Design
Workflow for generating formulas of chemically novel superconductors
Data and Codes for Experimentally Validated Inverse design of Multi-Property Fe-Co-Ni alloys
Periodic Table of Elemental Nobility / Reactivity
Data and code for the publication "Testing the Limits of the Global Instability Index"
Learning to Predict Crystal Plasticity at the Nanoscale:Deep Residual Networks and Size Effects in UniaxialCompression Discrete Dislocation Simulations
A WIP project to create a machine learning model for predicting the band gap of crystalline materials using compositional data.
A Deep Learning Model For Localization of Two-Phase High-Contrast Three-dimensional Materials
A deep generative modeling architecture for designing lattice constrained materials
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