Published July 14, 2025 | Version 1.0
Dataset Open

High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling

Authors/Creators

  • 1. ROR icon Massachusetts Institute of Technology
  • 2. ROR icon Forschungszentrum Jülich

Description

This dataset accompanies the codebase published here, and is part of the paper titled "High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling". 
The dataset contains the data used for Neural network surrogate model training, the trained model, the experimental data used in the paper, and an example set of results.

Files

Data.zip

Files (1.8 GB)

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Additional details

Funding

German Academic Exchange Service
United States Department of Energy
DE-EE0009096

Dates

Submitted
2025-07-13

Software

Repository URL
https://github.com/PV-Lab/bias.git
Programming language
Python
Development Status
Active