IDTechEx Analyzes the Impact of Large Language Models on the Material Development Landscape
A common approach is to use machine learning models trained on databases of material structures and properties, which then capture the underlying structure-property relationship.
- A common approach is to use machine learning models trained on databases of material structures and properties, which then capture the underlying structure-property relationship.
- Large Language Models (LLMs) like the GPT3.5/4 models behind ChatGPT and Microsoft's Copilot use similar tactics to model language: in 2024, their power to enhance material development is becoming clear.
- This can make the activities of a SaaS firm look more like a consulting outfit, reducing the capacity to scale.
- The role of a materials informatics firm is to connect the expertise of materials scientists and data scientists/engineers to drive material development.