Materialism: A Materials Science Podcast Episode 75: Large Language Models in Materials Science
25 snips
Oct 12, 2023 The podcast explores the potential impact of large language models in material science, discussing their applications in various fields and highlighting a paper on how LLMs can transform material science and chemistry. It delves into the benefits and challenges of using LLMs in coding, proposes tools for literature reviews and education, and shares experiences with LLMs in different domains. The hosts acknowledge sponsors and engage with listeners.
AI Snips
Chapters
Transcript
Episode notes
How LLMs Become Conversational
- Large language models are fundamentally next-word predictors that can be fine-tuned to behave like conversational agents.
- Reinforcement learning from human feedback made ChatGPT dramatically more useful for dialogue and domain tasks.
Four Practical LLM Use Areas
- The hackathon projects fell into four categories: predictive modeling, automation/interfaces, knowledge extraction, and education.
- These categories show practical ways LLMs can assist materials and chemistry workflows.
In-Context Learning Helps Low-Data Chemistry
- LLMs can use text-based molecular representations (SMILES) and in-context learning to perform predictions with very little data.
- Supplying domain rules as text biases the model and improves low-data performance.