Machine Learning for Materials (Lecture 1)

Slides linked to https://github.com/aronwalsh/MLforMaterials. Updated for 2025.

Transcript

1. Aron Walsh Department of Materials Centre for Processable Electronics Machine Learning for Materials Module MATE70026 (2025)
2. New Era of Materials Research A. Agrawal and A. Choudhary, APL Materials 4, 053208 (2016) The research toolkit for materials science now includes powerful data-driven statistical models
3. Computer Revolution Chris Hendon (now: University of Oregon) Keith Butler (now: STFC/SciML) Analytical Engine Automated calculations Charles Babbage (1837) “The science of operations has its own truth and value” Ada Lovelace (1840) Multiple two 20 digit numbers in ~3 minutes
4. Computer Revolution Chris Hendon (now: University of Oregon) Keith Butler (now: STFC/SciML) “System on a chip” microprocessor from https://www.apple.com
5. Computer Revolution Chris Hendon (now: University of Oregon) Keith Butler (now: STFC/SciML) “System on a chip” microprocessor from https://www.apple.com
6. Exascale Supercomputing Exascale computing refers to 1018 floating point operations per second; https://top500.org
7. Powerful Statistical Techniques Chris Hendon (now: University of Oregon) Keith Butler (now: STFC/SciML) Using GPT-3 via https://github.com/hwchase17/langchain Answers provided included transition metal oxides (V2 O5 ), Chevrel phases (Mo6 S8 ), Prussian blues (Fe4 [Fe(CN)6 ]3 )
8. Efficient Research Workflows J. P. Correa-Baena et al., Joule 2, 1410 (2018) Integration of computational techniques to accelerate discovery & development cycles
9. Module Contents 1. Introduction 2. Machine Learning Basics 3. Materials Data 4. Crystal Representations 5. Classical Learning 6. Artificial Neural Networks 7. Building a Model from Scratch 8. Accelerated Discovery 9. Generative Artificial Intelligence 10. Recent Advances Dense module with time to self-study to explore concepts further
10. Class Outline Course Introduction A. Overview B. Expectations C. Assessments
11. What is Machine Learning (ML)? Statistical algorithms that learn from training data and build a model to make predictions Data types Materials features can be binary (e.g. stability), categorical (e.g. symmetry), integer (e.g. stoichiometry), continuous (e.g. rate) Learning types Unsupervised (identify patterns), supervised (use patterns), reinforcement (maximise reward)
12. What is Machine Learning (ML)? Statistical algorithms that identify and use patterns in multi-dimensional datasets Image from “How Machines Learn” by Helen Edwards
13. What is Machine Learning (ML)? Statistical algorithms that identify and use patterns in multi-dimensional datasets Images from https://vas3k.com/blog/machine_learning Predict a category, e.g. decision trees to predict reaction outcome Predict a value, e.g. regression to extract a reaction rate Group by similarity, e.g. high-throughput crystallography Maximise reward, e.g. reaction conditions to optimise yield
14. What is Machine Learning (ML)? Statistical algorithms that operate on multi-dimensional arrays of numerical data Image from http://karlstratos.com; note the physical definitions are more nuanced 7 8 3 1 7 2 3 4 8 6 7 8 9 [1 7] ⋯ [6 4] ⋮ ⋱ ⋮ [5 6] ⋯ [2 8] 𝑥 𝒙𝒊 𝒙𝒊𝒋 𝒙𝒊𝒋𝒌
15. What is Machine Learning (ML)? Statistical algorithms that operate on multi-dimensional arrays of numerical data Image from “How Machines Learn” by Helen Edwards 𝑦1 𝑦2 𝑦3 𝑥11 𝑥12 𝑥13 𝑥14 𝑥15 𝑥21 𝑥22 𝑥23 𝑥24 𝑥25 𝑥31 𝑥32 𝑥33 𝑥34 𝑥35 𝑔1 𝑔2 𝑔3 𝑔4 𝑔5 = 3 1 matrix 3 5 matrix 5 1 matrix
16. ML ~ Function Approximation Image from https://github.com/jermwatt/machine_learning_refined Model selection, training, and testing tunes a “complexity dial” for your problem of interest Linear model Highly non-linear model Underfit regime Overfit regime
17. Why Machine Learning (ML)? Many problems are difficult to solve using standard techniques, e.g. combinational expansions Non-deterministic polynomial hard (NP-hard) Challenging class of computational problems, where finding an efficient solution remains an open and difficult task Fast Marching Method: J. Andrews and J. A. Sethian, PNAS 104, 1118 (2007) Travelling salesman: find the shortest route that visits each city once and returns home
18. Why Machine Learning (ML)? Many problems are difficult to solve using standard techniques, e.g. combinational expansions Relevant challenges in materials science Reaction engineering Navigate configurational space of reactants & products Crystal structure prediction Find the optimal 3D structure(s) for a given composition Materials design Achieve target functionality within chemical constraints
19. Why Machine Learning (ML)? Solid-solutions are used to control structure and properties, e.g. (1-x)ZnO + (x)ZnS → ZnO1-x Sx ML techniques can be used to sample and model massive configurational spaces Mixed sites in a supercell model N = 16: 12,870 N = 32: 6×108 N = 64: 1.8×1018 ! ! ! 2 2 N N N          Number of configurations for ZnO0.5 S0.5 A wurtzite crystal with a partially occupied anion site
20. Image from https://vas3k.com/blog/machine_learning ML Model Map
21. A. L. Samuel, IBM Journal, 211 (1959) Brief History of ML Term coined by Arthur Samuel in 1959 “It is now possible to devise learning schemes which will greatly outperform an average person and that such learning schemes may eventually be economically feasible”
22. W. S. McCulloch and W. Pitts, Bull. Math. Biophys. 5, 115 (1943) Brief History of ML An artificial neuron had been proposed in 1943 “Every net, if furnished with a tape, scanners connected to afferents to perform the necessary motor-operations, can compute only such numbers as can a Turing machine”
23. A. M. Turing, Mind 236, 433 (1950) Brief History of ML In 1950, Alan Turing proposed a “Learning Machine” that could become intelligent “I PROPOSE to consider the question, Can machines think?”
24. ML in Materials R&D Growing field combining traditional industry, large technology companies, and start-ups
25. Special thanks to Anthony Onwuli and Zhenzhu Li for assistance Source Material for Course ML content available from many sources, including blogs, research papers, repositories, and textbooks These slides are a skeleton, fleshed out with lectures, activities, and reading General Specialist
26. Class Outline Course Introduction A. Overview B. Expectations C. Assessments
27. Active Participation Your engagement is essential. This is a dense course with new concepts, Python coding, and self-study • Attend all lectures to hear the core content • Attend all practical sessions for hands-on coding • Attempt to solve problems yourself and ask course assistants if you need help
28. Creative Solutions There is great flexibility in programming with no unique solution for a given problem You may be interested in speed or clarity, but ultimately want a robust code • Check package manuals, e.g. https://matplotlib.org & https://scikit-learn.org • Search https://stackexchange.com & https://github.com for ideas
29. Creative Solutions Many AI assistants for coding exist such as Github Copilot, CodeWhisperer, Codeium, GPT4 • Most helpful when you know the basics first • Assistants often lack domain expertise and may give poor suggestions with buggy code based on out-of-date libraries • Not a substitute for hands-on coding experience and knowledge of materials
30. Dr Zhenzhu Li Irea Mosquera-Lois Xia Liang Fintan Hardy Yifan Wu Pan D. Kinga Mastej 2025 Module Assistants Research Fellow
31. Class Outline Course Introduction A. Overview B. Expectations C. Assessments
32. Module Assessment Aim for working knowledge of ML with practical sessions and coursework Computer labs (8⨉3%) Notebook submitted on Blackboard (Due by the end of each session – 16:00) Research challenge (76%) Assignment to complete (details after Lecture 9) Registration of absence or mitigation goes via the student office
33. Introductory Quiz http://menti.com Open on your phone, tablet or laptop