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A beginner’s guide to using
data science for physicists
Trevor David Rhone
Department of Physics, Applied Physics and Astronomy, Rensselaer Polytechnic Institute
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I Keep Six Honest Serving-Men
I keep six honest serving-men
(They taught me all I knew);
Their names are What and Why and When
And How and Where and Who.
- by Rudyard Kipling
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3.
What is Data Science?
Statistics
Computer
Science
Digital
Data
Knowledge
base
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What is Data Science?
Data Science
Machine
Learning
Statistics
Visualization
Databases
Data mining AI
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Why do we care about data science?
o Netflix movie recommendations
o Materials Discovery
• Experiments slow
• Calculations expensive
• No analytical solution
o Uncover physical insights
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o Targeted advertisements
o Self driving cars

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o Thales of Miletus, Ancient Greece
(624 BC – 546 BC)
The When and Where of data science
o Age of big data
• Data are accessible
• Data analytics tools are accessible
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o Observational astronomy
o Bioinformatics
o Social media and targeted advertising

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The essential guide
How to do data science?
1. Get data
o Kaggle
o Google dataset search
2. What are good descriptors?
o Mathematical representation of
the data
o Domain knowledge
3. Data visualization
4. Model selection
5. Model validation
6. Model exploitation
Photo: https://commons.wikimedia.org/7

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Data Science
Ecosystem
How to do data science?
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Data science ~ data visualization + machine learning
y = f(x1, x2, …, xN) + 𝜺
Inputs of machine learning modelTarget
property
How to do data science?
Goal: learn or quantify some relationship
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How to do data science?
o What are good descriptors?
o Data visualization
A. Baldominos et al., Appl. Sci. 2018, 8(11), 2321
Housing Prices
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How to do data science?
x1
x2 x
x
x
x
x
x
x
x
x
x
x
Supervised versus Unsupervised learning
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How to do data science?
x1
x2 x
x
x
x
x
x
x
x
x
x
x
Supervised versus Unsupervised learning
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How to do data science?
x1
x2
x1
x2x
x
x
x
x
x
x
x
x
x
x
Supervised versus Unsupervised learning
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How to do data science?
x1
x2
x1
x2x
x
x
x
x
x
x
x
x
x
x
Supervised versus Unsupervised learning
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age
Student? Check rating?yes
yesno yes no
young
middle-
aged
senior
no no yesyes
How to do data science?
Statistical models
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Machine learning models: Regression
x
y
Goal: Build predictive
model
Training data
How to do data science?
𝑓(𝑥) = 𝑚𝑥 + 𝑐
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ethen8181.github.io
How to do data science?
Model validation techniques

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x
y
Goal: Build predictive
model
Training data
Test data
How to do data science?
𝑓(𝑥) = 𝑚𝑥 + 𝑐
Machine learning models: Regression
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G.A. Landrum, H. Genin, Journal of Solid State Chemistry
176 (2003) 587–593
Ferromagnetism in
ordered binary
transition metal alloys
Machine learning models: Classification
How to do data science?
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G.A. Landrum, H. Genin, Journal of Solid State Chemistry
176 (2003) 587–593
Ferromagnetism in
ordered binary
transition metal alloys
Machine learning models: Classification
How to do data science?
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G.A. Landrum, H. Genin, Journal of Solid State Chemistry
176 (2003) 587–593
Ferromagnetism in
ordered binary
transition metal alloys
Machine learning models: Classification
How to do data science?
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Overfitting
How to do data science?
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Regularization
o y = f(x) + 𝛆
o Constraints on coefficients of a model
o LASSO
• Linear regression with constraints:
o Neural Networks
• Drop out
How to do data science?
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Hidden layer
Output layer
How to do data science?
Neural Networks
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Hidden layer Output layer
Andrew Ng, cs229.stanford.edu/notes/
Neural Networks
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Neural Networks
Architectures
Perceptron
Feed Forward
NN
Deep
NN
Autoencoder Recurrent
NN
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M. Mattheakis, P. Protopapas, D. Sondak, M. Di Giovanni, E. Kaxiras, arXiv:1904.08991
Neural Networks
Architectures that incorporate physical principles
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A2B2X6 crystal structure
Transition metal trichalcogenides are magnetic 2D crystals
1. Sivadas et al., PRB 91 235425 (2015) 2. C. Gong et al., Nature 546, 265 (2017)
o CrGeTe3 is a
ferromagnet
(FM)1,2
o CrSiTe3 is a zigzag
antiferromagnet
(zigzag-AFM)1
Machine Learning for Materials studies
A Case Study: Magnetic 2D crystals

29.
X = Te X = Se X = S
magneticmoment[𝜇B]
Magnetic moment of A2B2X6
T.D. Rhone et al.,
arxiv:1806.07989

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Magnetic moment, X=Te
DFT 𝝁
Predicted𝝁
Training data
Test data
var(#
spin↑
)
var(# valence e’)
m
axdif(#
valence
e’)
m
ean(polariz.)
chem
.spaceBoB
m
ean(#spin↑
)
Top 6 descriptors
Machine learning predictions
T.D. Rhone et al.,
arxiv:1806.07989

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Machine learning results
Magnetic moment Formation Energy

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Who can do data science?
o Programmers (python)
o Data wranglers
o Computer scientists
o Statisticians
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o Physicists!!!

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Resources
Data
o Kaggle
o Google’s dataset search
o Citrine datasets
o Materials project
Self-learning
o Coursera
• Andrew Ng (Machine
learning)
o Trevor Hastie (Intro to
Statistical Learning)
o Citrine newsletter
Workshops
o IPAM @ UCLA
o IACS computeFest @ Harvard
Data science tools
o Jupyter notebook
o Scikit-learn
o TensorFlow
o Keras
o PyTorch
materials-intelligence.com 33
Resources

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Outlook
o Growing interest in data science
o Google and Facebook seeking collaboration with physicists
o Create machine learning tools with physics principles
o AI for knowledge discovery
• Beyond ‘black box’ model predictions
• Use AI to understand physics
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Data science resources
https://materials-intelligence.com/
For additional resources please visit:
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