Machine Learning for Materials (Lecture 8)

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 8. Accelerated Discovery Module MATE70026
2. 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
3. “A problem in artificial intelligence is one which is so complex that it cannot be solved using any normal algorithm” Hugh M. Cartwright, Applications of AI in Chemistry (1993)
4. Class Outline Accelerated Discovery A. Automated Experiments B. Bayesian Optimisation C. Reinforcement Learning
5. Accelerate Scientific Discovery Research can be broken down into a set of tasks that can each benefit from acceleration H. S. Stein and J. M. Gregoire, Chem. Sci. 10, 9640 (2019) Traditional research workflow
6. Potential for speedup Accelerate Scientific Discovery H. S. Stein and J. M. Gregoire, Chem. Sci. 10, 9640 (2019) Research can be broken down into a set of tasks that can each benefit from acceleration
7. Accelerate Scientific Discovery Workflow classification of published studies H. S. Stein and J. M. Gregoire, Chem. Sci. 10, 9640 (2019)
8. Automation and Robotics Execution of physical tasks to achieve a target using autonomous or collaborative robots Image: https://www.thefifthindustrialrevolution.co.uk
9. Automation and Robotics Robots can be tailored for a wide range of materials synthesis and characterisation tasks B. P. MacLeod et al, Science Advances 6, eaaz8867 (2020)
10. Automation and Robotics Self-driving labs (SDL) are now operating N. J. Szymanski et al, Nature 624, 86 (2023); Phys. Rev. Energy 3, 011002 (2024) A-Lab, Berkeley
11. Flexible Automation Systems Modular hardware with computer-controlled synthesis and characterisation DIGIBAT: In collaboration with Magda Titirici, Ifan Stephens, and others (ICL)
12. Flexible Automation Systems Automation platforms designed to deliver complex research workflows (fixed platform or mobile) DIGIBAT: In collaboration with Magda Titirici, Ifan Stephens, and others (ICL) Usually a mix of proprietary code, with GUI and Python API for user control
13. Automation and Robotics Robots can be equipped with sensors and artificial intelligence to interact with their environment S. Eppel et al, ACS Central Science 6, 1743 (2020) Adapting computer vision models for laboratory settings GT = ground truth Pred = predicted
14. Automation and Robotics Robots can be equipped with sensors and artificial intelligence to interact with their environment https://www.youtube.com/watch?v=K7I2QJcIyBQ
15. Optimisation Algorithms to efficiently achieve a desired research objective. Considerations: Objective function (O): Materials properties or device performance criteria, e.g. battery lifetime Parameter selection: Variables that can be controlled, e.g. temperature, pressure, composition Data acquisition: How the data is collected, e.g. instruments, measurements, automation
16. Optimisation Algorithms Local optimisation – find the best solution in a limited region of the parameter space (x) Gradient based: iterate in the direction of the steepest gradient (dO/dx), e.g. gradient descent Hessian based: use information from the second derivatives (d2O/dx2), e.g. quasi-Newton O x x1 xn Local minimum The same concepts are involved in ML model training
17. Optimisation Algorithms Global optimisation – find the best solution from across the entire parameter space Numerical: iterative techniques to explore parameter space, e.g. downhill simplex, simulated annealing Probabilistic: incorporate probability distributions, e.g. Markov chain Monte Carlo, Bayesian optimisation O x Global minimum xn x1 The same concepts are involved in ML model training
18. Class Outline Accelerated Discovery A. Automated Experiments B. Bayesian Optimisation C. Reinforcement Learning
19. Bayesian Optimisation (BO) Use prior (measured or simulated) data to decide which experiment to perform next J. Močkus, Optimisation Techniques 1, 400 (1974) Probabilistic (Surrogate) Model Approximation of the true objective function O(x) ~ f(x), e.g. Gaussian process, GP(x,x') Acquisition Function Selection of the next sample point, e.g. upper confidence bound (UCB), probability of improvement (PI), expected improvement (EI) known new (parameters to sample)
20. Bayesian Optimisation (BO) Use prior (measured or simulated) data to decide which experiment to perform next J. Močkus, Optimisation Techniques 1, 400 (1974) Probabilistic (Surrogate) Model (parameters to sample) Gaussian process: f(x) ~ GP(μ(x), k(x,x’)) mean function Gaussian kernel function k(x,x’) measures the similarity between points x and x’ • Kernel controls function smoothness and defines uncertainty • Unobserved point x influenced by similar prior data • Dissimilar points default to the mean with high uncertainty known new1
21. Bayesian Optimisation (BO) Bayesian optimisation for chemistry: Y. Wu et al, Digital Disc. 3, 1086 (2024) Use prior (measured or simulated) data to decide which experiment to perform next
22. Upper confidence bound selects points that maximise the predicted function value of the model Exploration–Exploitation Tradeoff xnext = max( μ(x) + βσ(x) ) Prediction based on prior knowledge Weighted Uncertainty What to do next N. Srinivas et al, IEEE Transactions on Information Theory, 58 (2012) x β < 1 focus on exploitation β ~ 1 balance risk and reward β > 1 focus on exploration A tunable hyperparameter of UCB
23. Applications of BO Application to maximise electrical conductivity of a composite (P3HT-CNT) thin-film D. Bash et al, Adv. Funct. Mater. 31, 2102606 (2021)
24. Applications of BO D. Bash et al, Adv. Funct. Mater. 31, 2102606 (2021) Application to maximise electrical conductivity of a composite (P3HT-CNT) thin-film
25. Active Learning (AL) BO: find inputs that maximise the objective function AL: find inputs that enhance model performance Epistemic uncertainty* Posterior samples Target unknown regions with the largest uncertainty The Gaussian process is updated with new observations to yield revised function values and uncertainty * Reducible uncertainty associated with lack of information
26. Integrated Research Workflows Feedback loop between optimisation model and automated experiments NIMS-OS: R. Tamura, K. Tsuda, S. Matsuda, arXiv:2304.13927 (2023)
27. Integrated Research Workflows Feedback loop between optimisation model and automated experiments NIMS-OS: R. Tamura, K. Tsuda, S. Matsuda, arXiv:2304.13927 (2023)
28. Class Outline Accelerated Discovery A. Automated Experiments B. Bayesian Optimisation C. Reinforcement Learning
29. Reinforcement Learning (RL) Boston robotics Early applications in video games (maximise score), finance (maximise profit), and robotics (perform a task) Nintendo An agent interacts with an environment to learn decision-making strategies that achieve a specific goal
30. Reinforcement Learning (RL) RL schematic: S. J. D. Prince, https://udlbook.github.io
31. Reinforcement Learning (RL) Digital lab RL schematic: S. J. D. Prince, https://udlbook.github.io/udlbook Virtual scientist New experiment Property change Composition, structure, processing Design strategy (explore/exploit)
32. RL Policy This familiar equation is a softmax (Boltzmann) policy Data-driven decision making that adapts over time Probability of action at given state st Expected reward for action at Effective temperature for exploration/exploitation balance Sum over all possible actions
33. RL of Metal-Organic Frameworks This familiar equation is a softmax (Boltzmann) policy Hyunsoo Park et al, Digital Discovery 3, 728 (2024) Selectivity: 𝑆𝐶𝑂2/𝐻2𝑂 = 𝐾𝐻,𝐶𝑂2 𝐾𝐻,𝐻2𝑂 Heat of adsorption : 𝑄𝑠𝑡
34. RL of Metal-Organic Frameworks Hyunsoo Park et al, Digital Discovery 3, 728 (2024)
35. Optimisation Strategies Advantages Disadvantages Combinatorial (Enumeration) - Exhaustive search ensures no possibilities are missed - Simple to implement and understand - Inefficient for high-dimensional spaces - Maximises number of experiments and dataset Bayesian Optimisation - Efficiently exploit data - Works with noisy and expensive evaluations - Can use prior knowledge - Performance depends on surrogate model & acquisition function - May struggle with high- dimensional spaces Reinforcement Learning - Learns optimal policies through interaction - Can handle dynamic and complex environments - Requires large amounts of data for training - High computational cost - May converge slowly
36. Class Outcomes 1. Assess the impact of AI optimisation tools on materials research and discovery 2. Selection of appropriate optimisation strategy for a given problem Activity: Closed-loop optimisation