XCR-Research

Project

The Self-Driving Microscope: Predicting Stochastic Failure in Solid-State Batteries using Physics-Informed AI

Overview

This repository provides an organised workspace for research into solid-state batteries, X-ray computed tomography (XCT), scientific image processing, and physics-informed artificial intelligence.

Repository Structure

• data/ — datasets and input data

• notebooks/ — exploratory analysis and Jupyter notebooks

• src/ — reusable Python source code

• results/ — model outputs and evaluation results

• figures/ — plots and research visualisations

• tests/ — tests for research code

Environment

• Python 3.13

• NumPy

• SciPy

• scikit-image

• SimpleITK

• h5py

• JupyterLab

• Matplotlib

• pandas

Getting Started

Create and activate a virtual environment, then install the packages listed in requirements.txt.

Data and Reproducibility

Follow the research group's data-management, confidentiality, and version-control policies. Do not commit restricted datasets, credentials, or confidential research material.