Machine Learning for Materials (Lecture 9)

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

Transcript

1. Aron Walsh & Hyunsoo Park Department of Materials Centre for Processable Electronics Machine Learning for Materials 9. Generative Artificial Intelligence 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. Class Outline Generative AI A. Large Language Models B. From Latent Space to Diffusion
4. Natural Language Processing (NLP) Branch of AI that focuses on the interaction between computers and human language Image from https://github.com/practical-nlp
5. Natural Language Processing (NLP) Branch of AI that focuses on the interaction between computers and human language Easy Hard Spell checking Text classification Information extraction Question answering Conversational agent
6. Natural Language Processing (NLP) Many statements are ambiguous and require context to be understood Let’s eat grandma? Essen wir Oma? 我们吃奶奶的饭? おばあちゃんを食べようか？ Mangeons grand-mère? 할머니랑 같이 먹어요? Does the ambiguity of the English phrase translate? (image from DALL-E 3)
7. Language Models Predictive text Using GPT-4 via https://github.com/hwchase17/langchain I love materials because of they their shape are like Top words ranked by probability “Temperature” of the text choices Sampling the distribution of probabilities (“creativity”) I love materials because they ignite a symphony of vibrant colors, tantalizing textures, and wondrous possibilities that dance in the realms of imagination, transcending boundaries and embracing the sheer beauty of creation itself. I love materials because they are essential. strong essential beautiful
8. Language Models Large refers to the size and capacity of the model. It must sample a literary combinatorial explosion 104 common words in English 108 two-word combinations 1012 three-word combinations 1016 four-word combinations Language must be represented numerically for machine learning models Token: discrete scalar representation of word (or subword) Embedding: continuous vector representation of tokens
9. Text to Tokens Example: “ZnO is a wide bandgap semiconductor” https://platform.openai.com/tokenizer [57, 77, 46, 374, 3094, 4097, 43554, 39290, 87836] Token-IDs The model looks up 768 dimensional embedding vectors from the (contextual) embedding matrix Note that Zn is split into two tokens (not ideal for chemistry)
10. Large Language Models T. N. Brown et al, arXiv:2005.14165 (2020) GPT = “Generative Pre-trained Transformer” Generate new content Trained on a large dataset Deep learning architecture User Prompt Encode to a vector Transformer layers analyse relationship between vector components; generate transformed vector Decode to words Response Key components of a transformer layer Self-attention: smart focus on different parts of input Feed-forward neural network: capture non-linear relationships
11. Large Language Models B. Geshkovski et al, arXiv:2312.10794 (2023) Ongoing analysis into the physics of transformer architectures, e.g. rapid identification of strong correlations and approach to mean field solutions Focus on important inputs Normalise for stability Non-linear transformation Normalise for stability
12. Large Language Models Image from https://towardsdatascience.com Deep learning models trained to generate text e.g. BERT (370M, 2018), GPT-4 (>1012, 2023) Recent models include: Llama-3 (Meta, 2024) Gemini-2 (Google, 2024) GPT-4 (OpenAI, 2023) PanGu-5 (Huawei, 2024)
13. Large Language Models T. N. Brown et al, arXiv:2005.14165 (2020) Essential ingredients of GPT and related models Diverse data Deep learning model Validation on tasks
14. Large Language Models T. N. Brown et al, arXiv:2005.14165 (2020) Essential ingredients of GPT and related models Diverse data Deep learning model Validation on tasks
15. Secret to Practical Success of LLMs RLHF = Reinforcement Learning Human Feedback; Drawing from @anthrupad Patterns Focus Alignment
16. Large Language Models What are the potential drawbacks and limitations of LLMs such as GPT? • Training data, e.g. not up to date, strong bias • Context tracking, e.g. limited short-term memory • Hallucination, e.g. generate false information • Ownership, e.g. fair use of training data • Ethics, e.g. appear human generated
17. LLMs for Materials Many possibilities, e.g. read a textbook and ask technical questions about the content “The Future of Chemistry is Language” A. D. White, Nat. Rev. Chem. 7, 457 (2023)
18. LLMs for Materials Language models tailored to be fact-based with clear context. Applied to one of my review papers https://github.com/whitead/paper-qa
19. LLMs for Materials L. M. Antunes et al, Nature Comm. 15, 10570 (2024); https://crystallm.com CrystaLLM: learn to write valid crystallographic information files (cifs) and generate new structures
20. LLMs for Materials CrystaLLM: learn to write valid crystallographic information files (cifs) and generate new structures Training set 2.2 million cifs Validation set 35,000 cifs Test set 10,000 cifs Custom tokens: space group symbols, element symbols, numeric digits. 768 million training tokens for a deep-learning model with 25 million parameters L. M. Antunes et al, Nature Comm. 15, 10570 (2024); https://crystallm.com
21. LLMs for Materials D. A. Boiko et al, Nature 624, 570 (2023) Integrate a large language model into scientific research workflows
22. LLMs for Materials Combine text and structural data for multi-model models using contrastive learning Hyunsoo Park, A. Onwuli and A. Walsh, ChemRxiv (2024) Rich representations for text-to-compound generation Denoising diffusion with Chemeleon
23. Class Outline Generative AI A. Large Language Models B. From Latent Space to Diffusion Dr Hyunsoo Park
24. How Can AI Understand the World ? “I love an apple” ? I love an apple Index One-hot Encoding Continuous Vectors I 0 [1, 0, 0, 0, 0] [0.7, 0.8, 0.9] love 1 [0, 1, 0, 0, 0] [0.7, 0.4, 0.3] an 2 [0, 0, 1, 0, 0] [0.4, 0.5, 0.4] apple 3 [0, 0, 0, 1, 0] [0.1, 0.3, 0.7] banana 4 [0, 0, 0, 0, 1] [0.1, 0.2, 0.7] 0.7 0.8 0.9 0.7 0.4 0.3 0.4 0.5 0.4 0.1 0.3 0.7 AI (Computer) Fact: AI is not that smart...
25. How Can AI Understand Materials ? We need to convert materials into meaningful numerical values ? AI (Computer) ? ?
26. Autoencoder P. Baldi and K. Hornik (1989); Schematic adapted from https://synthesis.ai Neural network compresses data into a deterministic latent space and reconstructs it back to the original
27. Autoencoder P. Baldi and K. Hornik (1989); Schematic adapted from https://synthesis.ai Lack of continuity and structure makes interpolated or random points unlikely to map to meaningful data
28. Autoencoder – Latent Space Latent space
29. Variational Autoencoder (VAE) D. P. Kingma and M. Welling (2013); Schematic adapted from https://synthesis.ai Neural network encodes data into a probabilistic latent space that is more suitable for sampling (generation)
30. Latent Space in VAE Transforming the latent space into a Gaussian distribution, N(μ,σ2 )
31. VAE for Materials Generation Decoder J. Hoffmann et al. (2019) VAE Encoder
32. Denoising Diffusion Model Instead of learning one step (VAE), we can learn data in multiple steps (Diffusion)
33. Denoising Diffusion Model Diffusion Era! State-of-the-art models like Dall-E, Midjourney adopt diffusion for generative image AI
34. Diffusion for Materials Generation Hyunsoo Park, A. Onwuli and A. Walsh, ChemRxiv (2024)
35. Applications to Materials Design Hyunsoo Park, A. Onwuli and A. Walsh, ChemRxiv (2024) Gen AI models can be used in different ways, e.g. • map from composition to crystal structure • unguided sampling of a random compound • guided sampling to specific properties
36. Generative Artificial Intelligence H. Park, Z. Li and A. Walsh, Matter 7, 2358 (2024) Growing number of generative architectures that can be tailored for scientific problems
37. Class Outcomes 1. Explain the foundations of large language models 2. Knowledge of the central concepts underpinning generative artificial intelligence Activity: Research challenge