amkrajewski
Computational Materials Science Ph.D. specializing in artificial intelligence, complex data processing, and materials discovery
Phases Research LabUniversity Park, PA, USA
Pinned Repositories
MatSE580GuestLectures
Two guest lectures for MatSE580 at PSU to cover basics of (1) materials data manipulation, (2) storage, and (3) running ML methods on them.
mpdd-alignn
MPDD Calculator for Atomistic Line Graph Neural Network Deployment
MPDD-X
[playing around for now] One stop solution for getting material properties with ML models while contributing to a community database.
nimble-badge
Static hosting of automatically versioned badges for all Nim packages correctly released on Nimble. Relies on GitHub Actions and updates every 12 hours.
nimCSO
nim Composition Space Optimization is a high-performance tool leveraging metaprogramming to implement several methods for selecting components (data dimensions) in compositional datasets, as to optimize the data availability and density for applications such as machine learning.
nimplex
NIM simPLEX: A concise scientific Nim library (with CLI and Python binding) providing samplings, uniform grids, and traversal graphs in compositional (simplex) spaces.
PhD-Dissertation
My "Efficient Materials Informatics between Rockets and Electrons" PhD Dissertation in Materials Science and Engineering, defended on May 20th 2024, concisely spanning 352 pages and 109 figures.
pysmartdl2
Fork of the Smart Download Manager for Python
TDB-Highlighter
VS Code Language Extension provides syntax highlighting for the Thermodynamic DataBase (TDB) files used in the CALPHAD community to describe thermodynamic models of properties of materials.
pySIPFENN
Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique advantages through (1) effortless extensibility, (2) optimizations for ordered, dilute, and random atomic configurations, and (3) automated model tuning.
amkrajewski's Repositories
amkrajewski/nimCSO
nim Composition Space Optimization is a high-performance tool leveraging metaprogramming to implement several methods for selecting components (data dimensions) in compositional datasets, as to optimize the data availability and density for applications such as machine learning.
amkrajewski/nimplex
NIM simPLEX: A concise scientific Nim library (with CLI and Python binding) providing samplings, uniform grids, and traversal graphs in compositional (simplex) spaces.
amkrajewski/TDB-Highlighter
VS Code Language Extension provides syntax highlighting for the Thermodynamic DataBase (TDB) files used in the CALPHAD community to describe thermodynamic models of properties of materials.
amkrajewski/MatSE580GuestLectures
Two guest lectures for MatSE580 at PSU to cover basics of (1) materials data manipulation, (2) storage, and (3) running ML methods on them.
amkrajewski/mpdd-alignn
MPDD Calculator for Atomistic Line Graph Neural Network Deployment
amkrajewski/MPDD-X
[playing around for now] One stop solution for getting material properties with ML models while contributing to a community database.
amkrajewski/pysmartdl2
Fork of the Smart Download Manager for Python
amkrajewski/nimble-badge
Static hosting of automatically versioned badges for all Nim packages correctly released on Nimble. Relies on GitHub Actions and updates every 12 hours.
amkrajewski/PhD-Dissertation
My "Efficient Materials Informatics between Rockets and Electrons" PhD Dissertation in Materials Science and Engineering, defended on May 20th 2024, concisely spanning 352 pages and 109 figures.
amkrajewski/pqam-dparamhu2021
PyQAlloy-compatible Model for D Parameter Prediction Based on Yong-Jie Hu 2021 (10.1016/j.actamat.2021.116800)
amkrajewski/cGAN_demo
A demo of a conditional Generative Adversarial Network for High Entropy Alloy design
amkrajewski/pqam_RMSADTandoc2023
Lightweight fork of Tandoc's 2023 RMSAD model for HEAs for deployment compatible with ULTERA Database
amkrajewski/Al-Co-Cr-Mo-Ti-V-thermodynamic-database
amkrajewski/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
amkrajewski/dfttk
Density functional theory workflows for finite temperature thermodynamics based on atomate workflows.
amkrajewski/ESPEI
Fitting thermodynamic models with pycalphad - https://doi.org/10.1557/mrc.2019.59
amkrajewski/Hybrid-Cuckoo-Search
Hybrid CS for ultrafast global optimization in materials science and other diverse fields. And, Hybrid CS SCRAPs is a Multinary Solid-Solution Alloy Structure Design Tool
amkrajewski/mapp_api
Batch calculation for the Materials Properties Prediction project
amkrajewski/matten
MatTen: Equivariant Graph Neural Nets for Tensorial Properties of Materials
amkrajewski/nim-cocoa
macOS GUI Library for the Nim Programming Language
amkrajewski/nim-packages
List of packages for Nimble
amkrajewski/openjournals-draft-action
Experimental GitHub Action
amkrajewski/providers
This repository hosts the providers.json file for OPTIMADE that lists reserved database-specific prefixes and URLs to the index databases of all database providers that participate in the OPTIMADE network
amkrajewski/PSU-MatSE580-2020
amkrajewski/pycalphad
CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.
amkrajewski/pykan
Kolmogorov Arnold Networks
amkrajewski/pymatgen
Personal fork of Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.
amkrajewski/ULTERA-contribute-amkrajewski
ULTERA data extraction by AMK
amkrajewski/voro
Modified Voro++: a three-dimensional Voronoi cell library in C++
amkrajewski/vscode-vasp-support
(fork) Provides VS Code support for files of the Vienna Ab-initio Simulation Package (VASP)