thermoverse 0.1.0

ML models for materials property prediction

Research Find

Materials property prediction models and training scripts.

Overview

• Collection of training and evaluation scripts for materials datasets (181600, 50000, 5000).

Quick start

1. Create a Python virtual environment and activate it.
2. Install dependencies (add your project's requirements to requirements.txt).
3. Run training or evaluation scripts, e.g.:

python train_181600_models.py

Files of interest

• train_181600_models.py — training script you're editing.
• models/ — saved models (ignored by default).
• outputs/ — predictions and reports (ignored by default).

How to publish to GitHub

1. Create a new repo on GitHub (choose a name like research-find).
2. From this folder run:

git init
git add .
git commit -m "Initial commit"
git branch -M main
git remote add origin https://github.com/your-username/repo-name.git
git push -u origin main

Replace your-username/repo-name.git with your repository URL.

If you want me to run these commands and push, give me the repository URL or let me know and I'll guide you through creating a PAT for authentication.

Model description

• Purpose: Build accurate, generalizable machine-learning predictors for thermodynamic and mechanical materials properties to accelerate screening and discovery.
• Data: Trained on curated datasets (Materials_Dataset_181600.csv, Materials_Dataset_50000.csv, Materials_Dataset_FIXED.csv).
• Model types: Ensemble models (XGBoost / CatBoost / RandomForest-style) trained per-property with cross-validation and ensembling.
• Inputs: Composition-based features and engineered descriptors produced by preprocessing scripts.
• Outputs: Per-property CSV predictions under outputs/ and evaluation summaries (R², RMSE) stored in outputs/ and models/.
• Usage: create a Python environment, install dependencies, then run training and evaluation scripts such as python train_181600_models.py.
• Notes: large model files and outputs are excluded via .gitignore. If datasets or model artifacts exceed GitHub file size limits (>100MB) enable Git LFS for those paths.