Curated list of known efforts in materials informatics, i.e. in modern materials science

Reproduction of CGCNN for predicting material properties

mechanoChemML library

Utilities for ab initio modeling suite CRYSTAL, developed in Turin University

A "sky-scanner" Optimade browser-only GUI

Code implementation of PolyCL

A suite of scripts and modules that perform menial, tedious and repetitive tasks and operations on VASP files that were hurriedly and haphazardly put together by some intern.

Tools for handling Wannier models with a spin operator, calculating the Wannier model quality and spin-texture plotting.

The optical properties of 2D materials with the VASP code

Basin-Hopping Monte Carlo (BHMC) algorithm coupled with density functional theory (Quantum espresso) package for unbiased global optimization of clusters and nanoparticles

Layer Toolkit is a reusable Python package for building layered VASP inputs and analyzing electronic localization function (ELF) and bonding characteristics.

End-to-end Machine Learning pipeline for developing Moment Tensor Potentials (MTP) for Tungsten and GaAs using mlip-3, Quantum Espresso and LAMMPS

Discovery pipeline for magnetic materials: unified dataset, multitask crystal GNN screening, GNoME candidate ranking, and DFT validation of rare-earth-free permanent-magnet candidates.

SAD-based formation-energy-per-atom prediction for fast screening without structure relaxation.

Generative inverse design of Li–P–S solid-electrolyte candidates: MatterGen → self-consistent MLIP stability screen (S.U.N.) → conductivity ranking. Concept validation.

calypso setup script to automate file setup for searches (lots of config options for 2d searches)

Dataset of sodium-ion battery cathode candidate materials generated from Materials Project structures with estimated electrochemical properties (capacity, voltage, and energy density) for machine learning–based inverse design and screening.

Thermo-metallurgical FEM (Abaqus) predicting phase transformation in a quenched S5140 steel gear - JMAK + Koistinen-Marburger kinetics, parametric cooling-rate study. IIT Madras.

A comprehensive Python toolkit for fiber network generation, simulation, and analysis | 面向材料科学研究的完整纤维网络结构生成、模拟与分析 Python 工具包

Open machine-learning force field (MLFF) training datasets for pristine, defect-engineered, doped, and interfacial HOPG systems generated from first-principles Density Functional Theory (DFT) calculations.