Locating Peaks in Neutron Diffraction Patterns Using Machine 

Learning

Dylan Zhang

Mentor - Dr. William Ratcliff

Wootton High 

School

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Objective

● Build and train a U-net 

machine learning model 

to be able to identify 

peaks on a neutron 

diffraction graph

The neutron diffraction profiles for sequence 1: (a) crosses show... | Download 2

Scientific Diagram (researchgate.net)
Crystals

● a type of solid material composed of 

atoms or groups of atoms that are 

arranged in a three-dimensional pattern 

that is very ordered

● Lattice determines the different 

diffraction patterns

Crystal lattice of NaCl - Crystal lattice models - Structure of crystals - 3

Solid-state physics - Physics Equipment - Physics (leybold-shop.com)
Neutron Diffraction

● Neutron Diffraction is the application of elastic neutron scattering to

determine the atomic and magnetic structure of a material

● A sample is placed in a beam of neutrons to obtain a diffraction pattern that

provides information about the structure of the sample

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PPT - Neutron Diffraction and Scattering in Biology PowerPoint Presentation -

ID:2313326 (slideserve.com)
The Problem

● The Neutron diffraction process is largely manual and is extremely time 

consuming 

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Why/How

● automate some of the 

crystallography process 

● significant because the position of 

peaks gives us valuable 

information on the sample

1. Build the U-net model

2. Train the model with graph data and their labels

3. Evaluate/test the model

4. Tune hyperparameters if needed

5. Evaluate again

Why How

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Supervised Learning

● a subcategory of machine learning

● takes both training data and its associated 

output

● possible for you to be very specific about the 

definition of the classes

● supervised learning is a simple process for the 

supervisor to understand

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SUPERVISED MODELS | Data Vedas
Neural Networks

● a computer system modeled on the 

human brain and nervous system

● comprised of a node layers, containing 

an input layer, one or more hidden 

layers, and an output layer

● Each node has an associated weight 

and threshold

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Convolutional Neural Network

● Distinguished from other neural networks by their superior 

performance with image, speech, or audio signal inputs

● Convolutional layers

○ Main building block

○ Layer where the majority of computation occurs

● pooling layers

○ conducts dimensionality reduction

● fully-connected layers

○ connect every input neuron to every output neuron

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Semantic Segmentation

● an image segmentation method that assigns every single pixel in an image belonging to an object

10 Example of 2D semantic segmentation: (Top) input image (Bottom) prediction. |

Download Scientific Diagram (researchgate.net)
U-net

● Architecture for semantic segmentation

● Computationally efficient

● Trainable with a small data-set

● Trained end-to-end

● great for biomedical image segmentation

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Conclusion

● U-net consistently works well 

with both simulated data and 

real data

● Predicts simulated data more 

accurately

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Whats Next ● Use this model to determine 

which peaks have changed with 

temperature changes

● Output used to determine 

where an existing AI program 

will focus on for temperature 

measurements

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Acknowledgements

● Dr. Ratcliff

● CHRNS

● Dr. Dura

● Dr. Borchers

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