(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

Learning Basics 4. Materials Data and Representations 5. Classical Learning 6. Artificial Neural Networks 7. Building a Model from Scratch 8. Recent Advances in AI 9. and 10. Research Challenge Dense course with time to self-study to explore concepts further

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)

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

using standard techniques, e.g. combinational expansions Some 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

and properties, e.g. (1-x)ZnO + (x)ZnS à ZnO1-x Sx ML techniques can be used to sample and model this massive configurational space Mixed sites in a supercell model N = 16: 12,870 N = 32: 6×108 N = 64: 1.8×1018 ! ! ! 2 2 N N N æ öæ ö ç ÷ç ÷ è øè ø Number of configurations for ZnO0.5 S0.5 A wurtzite crystal with a partially occupied anion site

ML The term was 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”

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”

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

course with new concepts, advanced coding, and self-study • Attend all lectures • Attend all practical sessions • Attempt to solve problems yourself and ask course assistants if you need help

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

Github Copilot, CodeWhisperer, Codeium, GPT4 • Most helpful when you know the basics first • Assistants often lack domain expertise and give poor suggestions with buggy code based on old versions of Python libraries • Not a substitute for hands-on coding experience and knowledge of materials

sessions and coursework Computational exercises (40%) Submitted on MyDepartment (Due 1 hour after each computer lab) Research challenge (60%) Assignment to complete (details in Lecture 9) Registration of absence or mitigation goes via the student office