Building a curated dataset from NREL’s High Throughput Experimental Materials Database (HTEM DB) and training a neural network to predict thin-film properties (starting with thickness). The project is structured in three stages:

• Stage 1 — Dataset pipeline (complete):
  Notebook-driven workflow to search HTEM libraries, download filtered libraries locally to reduce API calls, and construct a flattened ML-ready dataset (deposition parameters + composition + measurement outputs). Includes preliminary cleaning, EDA, and outlier handling.

• Stage 2 — Neural network modeling:
  Training and refining a PyTorch regression model. Current focus:

  • Simplify architecture while preserving performance (parameter efficiency vs. accuracy)
  • Tune batch size / learning rate tradeoffs; improve training stability
  • Add regularization (dropout, weight decay) and compare impact on overfitting
  • Analyze and compare model performance to determine best choice

• Stage 3 — Web interface (planned):
  A lightweight web app for model access and inference once the training pipeline is finalized.

Repo structure

• notebooks/ — HTEM querying, filtering, dataset creation, EDA
• neuralnet/ — PyTorch training code and experiments
• neuralnet/analysis notebooks — Analysis and comparison of model performance
• config/ — local config.yaml (ignored by git) for system-specific paths

HTEM API: https://htem.nrel.gov/api-docs

• Pebble (https://github.com/Vision84/Pebble)
• Parky (https://github.com/Plate1/Parky)