Machine Learning for Materials (Lecture 2)

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 2. Machine Learning Basics 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. Artificial Intelligence Computational techniques that mimic human intelligence ARTIFICIAL INTELLIGENCE (AI) (entire knowledge field) MACHINE LEARNING (ML) (data-driven statistical models) Supervised Unsupervised Reinforcement DEEP LEARNING (multi-layered neural networks) Nobel Prizes in Chemistry and Physics (2024)
4. Focus on Machine Learning (ML) https://vas3k.com/blog/machine_learning Statistical techniques that improve with experience
5. Focus on Machine Learning (ML) https://vas3k.com/blog/machine_learning
6. Quiz What type of learning is this? Input Output {Cubic, Tetragonal, Orthorhombic, Hexagonal, Trigonal, Monoclinic, Triclinic} ML Model Crystal structure Crystal system Black box of trained parameters “Black box” is a common criticism of ML – with the right tools you can open it up!
7. Class Outline Materials Learning Basics A. Terminology B. Evaluation metrics C. Learning by example
8. Function Approximation y = f(x) Output Input Property Features 1. Composition 2. Structure Linear Regression y = β 0 + β 1 x1 + β 2 x2 Property Composition Structure Learned weights Constant
9. Function Approximation Generalised Linear Models y = β 0 + f1 (x1 ) + f2 (x2 ) Property Composition Structure Constant y = β 0 + β 1 x1 + β 2 x2 + β 3 x1 x2 Non-Linear Interactions Property Composition Structure Constant Coupling
10. Function Approximation You should recognise the underlying function your undergraduate classes
11. Function Approximation My reference function to generate data for model training and testing
12. Function Approximation Underfitting Overfitting Fitting Default parameters with the scikit-learn Python package
13. Function Approximation Standard expansions work in low dimensions (D). Real problems face the “curse of dimensionality” An exponential increase in the data requirements needed to cover the parameter space effectively, O(eD) M. M. Bronstein et al, arXiv:2104.13478 (2021)
14. Three Components of ML Models 1. Representation Type of data and model architecture A Few Useful Things to Know about Machine Learning, P. Domingos 2. Evaluation Objective (or scoring) function to distinguish good from bad models 3. Optimisation Update of model parameters to improve performance
15. ML Vocabulary • Classification model – input a tensor of feature values and output a single discrete value (the class) • Regression model – input a tensor of feature values and output a continuous (predicted) value • Feature – an input variable • Labelled example – a feature with its corresponding label (the “answer” or “result”) • Ground truth – reliable reference value(s) • Hyperparameter – model variables that can be tuned to optimise performance, e.g. learning rate See https://developers.google.com/machine-learning/glossary
16. ML Vocabulary • Bias – systematic error in the average prediction • Variance – variability around the average prediction Low variance High variance Low bias High bias underfitting overfitting Predicted values (purple circles) compared to the ground truth (red centre)
17. ML Vocabulary • Underfitting – model too simple to describe patterns • Overfitting – model too complex and fits noise Image from https://github.com/jermwatt/machine_learning_refined Regression Classification
18. ML Vocabulary Image from https://github.com/jermwatt/machine_learning_refined Dataset split High training error & high bias High validation error & high variance • Underfitting – model too simple to describe patterns • Overfitting – model too complex and fits noise
19. Model assessment Typical Supervised ML Workflow Initial dataset x, y Data cleaning and feature engineering The exact workflow depends on the type of problem and available data Model training and validation Final model xnew ypredict Test (20%) xtest , ytest Train (80%) xtrain , ytrain Human time intensive Computer time intensive Production
20. Class Outline Materials Learning Basics A. Terminology B. Evaluation metrics C. Learning by example
21. Model Assessment Consider a linear model with optimal weights w 𝒚𝒑𝒓𝒆𝒅𝒊𝒄𝒕𝒆𝒅 = model(𝒙, 𝒘) = 𝑤0 + 𝑤1 𝑥 i = 1 𝑛 σ𝑖=1 𝑛 (𝑦i − model 𝑥𝑖 , 𝒘 )2 = 1 𝑛 σ𝑖=1 𝑛 (𝑒i )2 Mean squared error (MSE) Squaring the error ensures non-negativity and penalises larger deviations
22. • Residual – a measure of prediction error • MAE – Mean Absolute Error = • RMSE – Root Mean Square Error = • Standard Deviation – a measure of the amount of dispersion in a set of values. Small = close to the mean. Expressed in the same units as the data, e.g. lattice parameters a = 4 Å, 5 Å, 6 Å mean = (4+5+6)/3 = 5 Å deviation = -1, 0, 1; deviation squared = 1, 0, 1 sample variance σ2 = (1+0+1)/2 = 1 standard deviation σ = 1 Å Model Assessment σ𝑖=1 𝑛 𝑒𝑖 𝑛 σ 𝑖=1 𝑛 (𝑒𝑖 )2 𝑛 𝑒𝑖 = 𝑦𝑖 − 𝑦 𝑖 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
23. Model Training → Minimising Error Model weights w are adjusted until a cost function (e.g. RMSE) is minimised Gradient descent is a popular choice: 𝑤𝑖 → 𝑤𝑖 - α 𝑑 Error 𝑑𝑤𝑖 Learning rate Warning: local optimisation algorithms often miss global minima Parameter set w Error Step 0 3 1 2
24. Model Training → Minimising Error Model weights w are adjusted until a cost function (e.g. RMSE) is minimised Gradient descent is a popular choice: 𝑤𝑖 → 𝑤𝑖 - α 𝑑 Error 𝑑𝑤𝑖 Learning rate Animation from https://github.com/jermwatt/machine_learning_refined w RMSE
25. Model Training → Minimising Error Optimisation algorithms have their own parameters, e.g. step size and no. of iterations Animation from https://github.com/jermwatt/machine_learning_refined w Step number RMSE Learning rate (step size)
27. Correlation Coefficient (r) Describes the strength of the relationship between two variables (e.g. “ground truth” vs predicted values) r ∊ [-1,1] Positive: variables change in the same direction Zero: no relationship between the variables Negative: variables change in opposite directions *Outlined by Auguste Bravais (1844); https://bit.ly/3Kv75GJ Reminder: correlation does not imply causation Pearson correlation* 𝑟𝑥𝑦 = σ𝑖=1 𝑛 (𝑥𝑖 − ҧ 𝑥)(𝑦𝑖 − ത 𝑦) σ 𝑖=1 𝑛 (𝑥𝑖 − ҧ 𝑥)2 σ 𝑖=1 𝑛 (𝑦𝑖 − ҧ 𝑥)2
28. Coefficient of Determination (r2) Measure of the goodness of fit for a model. Describes how well that known data is approximated r2 ∊ [0,1] Zero: baseline model with no variability that predicts 0.5: 50% of the variability in y is accounted for One: model matches observed values of y exactly S. Wright “Correlation and Causation”, J. Agri. Res. 20, 557 (1921) Note: a unitless metric. Alternative definitions are sometimes used 𝑟2 = 1 − 𝑆𝑆𝑟𝑒𝑠 𝑆𝑆𝑡𝑜𝑡 ഥ 𝒚 𝑟2 = 1 − σ𝑖=1 𝑛 𝑦𝑖−𝑦 𝑖 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 2 σ 𝑖=1 𝑛 (𝑦𝑖− ത 𝑦)2 𝑟2 = 1 − σ𝑖=1 𝑛 𝑒𝑖 2 σ 𝑖=1 𝑛 (𝑦𝑖− ത 𝑦)2 Three equivalent definitions
29. Correlation, Causation… F. Messereli, New England Journal of Medicine 367, 1562 (2012) Chocolate Nobel prizes Correlation Confounder Causation Causation Causal Inference
30. Classification Metrics Confusion (or error) matrix provides a summary of classification model performance K. Pearson “Mathematical Contributions to the Theory of Evolution” (1904) True positive (𝐓𝐏) False negative (𝐅𝐍) False positive (𝐅𝐏) True negative (𝐓𝐍) Actual class Predicted class + + - - 70 0 0 30 Perfect model to classify metals and insulators (N = 100) 66 4 8 22 My best model Accuracy = Correct/Total (70+30)/100 = 100 % (66+22)/100 = 88 %
31. Classification Metrics Confusion (or error) matrix provides a summary of classification model performance K. Pearson “Mathematical Contributions to the Theory of Evolution” (1904) True positive (𝐓𝐏) False negative (𝐅𝐍) False positive (𝐅𝐏) True negative (𝐓𝐍) Actual class Predicted class + + - - 70 0 0 30 Perfect model to classify metals and insulators (N = 100) 66 4 8 22 My best model Sensitivity = TP/(TP+FN) 70/(70+0) = 100 % 66/(66+4) = 94 %
32. Quiz Fill in “?” for this confusion matrix Metal Insulator Predicted class Actual class Insulator Metal Insulator 10 2 Metal ? 4 There are 20 data points in total
33. Class Outline Materials Learning Basics A. Terminology B. Evaluation metrics C. Learning by example
34. Supervised Regression Model that maps an input to an output based on example input-output pairs (labelled data) Regression predicts a continuous value, e.g. to extract a reaction rate 𝒚 = 𝑓(𝐱) + ε Target variable Learned function Error We’ll start to cover the model (function) details in Lecture 5
35. Regression Example Predict the dielectric constant of a crystal K. Morita et al, J. Chem. Phys. 153, 024503 (2020) Support vector regression (r2=0.92) Note: outliers are often interesting cases (poor or exceptional data)
36. Regression Example Predict the dielectric constant of a crystal K. Morita et al, J. Chem. Phys. 153, 024503 (2020); https://github.com/slundberg/shap SHAP (SHapley Additive exPlanations) analysis is a method for interpreting ML models Relative importance of input features in making predictions A positive SHAP indicates a feature contributes to an increase in the prediction
37. Regression Example Predict the dielectric constant of a crystal K. Morita et al, J. Chem. Phys. 153, 024503 (2020); https://github.com/slundberg/shap CdCN2 Transparent breakdown of a predicted value Note: a physical connection is not implied (only a correlation)
38. Supervised Classification Model that maps an input to an output based on example input-output pairs (labelled data) Classification predicts a category, e.g. decision trees for reaction outcomes 𝒚 = 𝑓(𝐱) Class label Classifier Assignment can be absolute or probabilistic (e.g. 90% apple, 10% pear)
39. Classification Example G. H. Gu et al, npj Computational Materials 8, 71 (2022) Predict if a material will be stable or unstable Crystal likeness (CL) Probabilistic score for the class label
40. Classification Example G. H. Gu et al, npj Computational Materials 8, 71 (2022) ABX3 perovskite crystals Radius ratio rules Neural network model Improved selectivity for promising compositions Likelihood of formation
41. Unsupervised Learning Model that can identify trends or correlations within a dataset (unlabeled data) Clustering groups data by similarity, e.g. high-throughput crystallography 𝐱 → 𝑓(𝐱) Input data Transformation to new representation
42. Unsupervised Example Map materials space according to their features Hyunsoo Park et al, Faraday Discussions 256, 601 (2025) Dimensionality reduction techniques: PCA: Principal component analysis t-SNE: t-distributed stochastic neighbour embedding UMAP: Uniform manifold approximation and projection for dimension reduction
43. Reinforcement Learning Model that performs a series of actions by trial and error to achieve an objective Maximise reward, e.g. reaction conditions to optimise yield Agent ⇌ Environment Automated experiments Actions Samples, conditions, etc.
44. Reinforcement Example Achieve the highest score or outcome (e.g. AlphaGo) Image from https://towardsdatascience.com/how-to-teach-an-ai-to-play-games Untrained model Trained model Q-learning model
45. Reinforcement Example This familiar equation is a softmax (Boltzmann) policy Hyunsoo Park et al, Digital Discovery 3, 728 (2024) Optimisation of metal-organic frameworks
46. Class Outcomes 1. Define machine learning 2. Describe the three components of machine learning with examples 3. Explain the statistical metrics used to assess model performance Activity: Crystal hardness