TEM-image-generation-using-Neural-Networks

Deep learning workflow for generating and predicting Transmission Electron Microscopy (TEM) images from Density Functional Theory (DFT) derived crystal structures.

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Author - Akshu Attri

Overview

This repository contains a machine learning pipeline for:

• processing DFT crystal structures,
• generating projected structural inputs,
• training neural networks,
• and predicting TEM-like image outputs.

The workflow integrates computational materials science with deep learning for microscopy image generation and structure-property analysis.

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Repository Structure

TEM-image-generation-using-Neural-Networks/
│
├── dft_structures/
│   └── Input DFT crystal structures
│
├── projected_inputs/
│   └── Generated projected structure representations
│
├── tem_targets/
│   └── Target TEM image datasets
│
├── dataset.py
│   └── Dataset loading and preprocessing
│
├── generate_inputs.py
│   └── Generates projected inputs from structures
│
├── generate_tem.py
│   └── TEM image generation utilities
│
├── model.py
│   └── Neural network architecture
│
├── train.py
│   └── Training workflow
│
└── README.md

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Workflow

Step 1 — Input Crystal Structures

DFT-generated structures are stored in:

dft_structures/

These structures serve as the starting point for projected feature generation.

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Step 2 — Generate Projected Inputs

Run:

python generate_inputs.py

This generates projected structural representations used for neural network training.

Generated outputs are stored in:

projected_inputs/

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Step 3 — TEM Dataset Preparation

Target TEM images are stored in:

tem_targets/

These images are used as supervised learning targets.

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Step 4 — Model Training

Train the neural network using:

python train.py

This performs:

• dataset loading,
• preprocessing,
• model training,
• optimization,
• and prediction learning.

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Neural Network Architecture

The deep learning architecture is implemented in:

model.py

The model is designed for:

• image generation,
• structure-to-image mapping,
• and TEM feature prediction.

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Dataset Handling

Dataset preprocessing and loading utilities are implemented in:

dataset.py

This includes:

• input normalization,
• batching,
• tensor conversion,
• and training dataset preparation.

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Requirements

Install dependencies using:

pip install numpy matplotlib torch torchvision

Additional dependencies may include:

pip install scikit-learn opencv-python

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Applications

This workflow can be extended for:

• TEM image prediction
• Structure-to-image deep learning
• Microscopy data analysis
• Computational materials informatics
• AI-assisted electron microscopy
• DFT + machine learning workflows

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Future Improvements

Potential future developments include:

• Generative AI models
• GAN-based TEM synthesis
• Diffusion models
• High-throughput structure prediction
• Experimental TEM comparison
• Automated feature extraction

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Research Areas

• Computational Materials Science
• Deep Learning
• Electron Microscopy
• DFT Workflows
• AI for Materials Science
• Scientific Machine Learning