Materials+ML Workshop Day 5¶

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Day 5 Agenda:¶

  • Questions about Day 4 Material
  • Review of Day 4

Content for today:

  • The Atomic Simulation Environment
    • Building atomic structures
    • Visualizing atomic structures
  • Python Materials Genomics (pymatgen with ase)
    • Visualizing properties (band structure)
  • Using the Materials Project Database
    • Querying material properties
    • Getting crystal structure data

Background Survey¶

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https://forms.gle/ArUHPp2C6TdLF5dQ7¶

The Workshop Online Book:¶

https://cburdine.github.io/materials-ml-workshop/¶

Tentative Week 1 Schedule:¶

Session Date Content
Day 1 06/09/2025 (2:00-4:00 PM) Introduction, Python Data Types
Day 2 06/10/2025 (2:00-4:00 PM) Python Functions and Classes
Day 3 06/11/2025 (2:00-4:00 PM) Scientific Computing with Numpy and Scipy
Day 4 06/12/2025 (2:00-4:00 PM) Data Manipulation and Visualization
Day 5 06/13/2025 (2:00-4:00 PM) Materials Science Packages, Introduction to ML

Questions¶

Material covered yesterday:

  • Pandas
  • Matplotlib

Review: Day 4¶

Pandas¶

  • Pandas is an open-source Python package for data manipulation and analysis.
  • It can be used for reading writing data to several different formats including:
    • CSV (comma-separated values)
    • Excel spreadsheets
    • SQL databases
  • We can import pandas as follows:
In [1]:
import pandas as pd

DataFrames¶

  • We can create Dataframes from Python dictionaries as follows:
In [2]:
# Data on the first four elements of the periodic table:
elements_data = {
    'Element' : ['H', 'He', 'Li', 'Be'],
    'Atomic Number' : [ 1, 2, 3, 4 ],
    'Mass' : [ 1.008, 4.002, 6.940, 9.012],
    'Electronegativity' : [ 2.20, 0.0, 0.98, 1.57 ]
}

# construct dataframe from data dictionary:
df = pd.DataFrame(elements_data)
In [6]:
import numpy as np

# get the 'Mass' column and convert it to a numpy array:
mass_series = df['Mass']
mass_array = np.array(mass_series)

print(mass_array)

[1.008 4.002 6.94  9.012]
In [28]:
# function for estimating mass:
def amu_mass(n):
    return 2*n

# add an "Estimated Mass" column to the dataframe:
df['Estimated Mass'] = \
    df['Atomic Number'].apply(amu_mass)
display(df)
Element Atomic Number Mass Electronegativity Estimated Mass
0 H 1 1.008 2.20 2
1 He 2 4.002 0.00 4
2 Li 3 6.940 0.98 6
3 Be 4 9.012 1.57 8

Matplotlib¶

  • Matplotlib is a MATLAB-like plotting utility for creating publication-quality plots
  • In matplotlib, we typically import the pyplot subpackage with the alias plt:
In [8]:
import matplotlib.pyplot as plt
In [10]:
# generate some data:
data_x = np.linspace(0,8,10)
data_y = np.sin(data_x)

# create a new figure and plot data:
plt.figure(figsize=(7,2))
plt.grid()
plt.plot(data_x, data_y, 'ro--')

# add a title and show plot in notebook:
plt.title('Example of a Line plot')
plt.show()
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New Content:¶

  • Materials Science Python Packages:
    • ASE (Atomic Simulation Environment)
    • Pymatgen (Python Materials Genomics)
    • Materials Project API

Installing packages:¶

  • Install ASE:
pip install ase
  • Install ASE:
pip install pymatgen
  • Install Materials Project API
pip install mp-api
In [25]:
pip install ase pymatgen mp-api

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: mp-api in /home/colin/.local/lib/python3.10/site-packages (0.33.3)
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Note: you may need to restart the kernel to use updated packages.

The Atomic Simulation Environment¶

  • ASE is a Python package for building, manipulating, and performing calculations on atomic structures
  • ASE provides interfaces to several different simulation platforms, such as:
    • VASP
    • Quantum ESPRESSO
    • Q-Chem
    • Gaussian

ASE Basics:¶

  • The fundamental data type in ASE is the Atoms object:

    • Atoms represents a collection of Atoms in a molecular or crystalline structure.
    • Material properties (such as the results of calculations) can be attached to Atoms instances
  • ASE has functionality for loading, exporting and viewing Atoms objects in different formats.
In [25]:
from ase.build import molecule
from ase.visualize import view

# build a common molecule:
acetic_acid = molecule('CH3COOH')
view(acetic_acid, viewer='x3d')
Out[25]:
ASE atomic visualization

Tutorial: ASE - The Atomic Simulation Environment¶

  • Building simple molecules
  • Building inorganic structures
    • MXenes
    • Carbon Nanotubes

Exercise: ASE - The Atomic Simulation Environment¶

  • Nitrogen-Vacancy Centers in Diamond

The Materials Project¶

  • Register for an account at https://next-gen.materialsproject.org/
  • Once you have registered, find your API key under My Dashboard:

mp api key

  • Once you have copied your API key, paste it into your notebook as a string:
In [30]:
# create a variable in your notebook with your API key
MP_API_KEY = '< Paste your API key here. >'

Always avoid sharing your API key with others.

Tutorial: Working with the Materials Project¶

  • We will examine the material

YBa$_1$Cu$_2$O$_7$ (YBCO)

  • YBCO is a high-temperature superconductor
  • We will:
    • Visualize the crystal structure
    • Plot the Band Structure

Exercise: Working with the Materials Project¶

  • Visualize the DOS of YBa$_2$Cu$_3$O$_7$

Questions¶

  • Any questions about Week 1 or Week 2 Content?

Additional Review: Statistics¶

  • Survey Results: Familiarity with statistics (1-10 Scale)
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Additional Review: Linear Algebra¶

  • Survey Results: Familiarity with Linear Algebra (1-10 Scale)
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Recommended Reading:¶

  • Introduction to Machine Learning
    • Statistics Review (as needed)
    • Mathematics Review (as needed)
  • Supervised Learning

If possible, try to do the exercises. Bring your questions to our next meeting (next Monday).