AI-powered
podcast player
Listen to all your favourite podcasts with AI-powered features
Fluid Intelligence: Simulating Solutions with Tim Duignan
"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis
Neural Networks Predict Material Transformations
1min Snip
00:00
Summary
Transcript
Episode notes
Neural networks trained to predict material transformations showed the ability to foresee structural changes even with variable interaction strengths between different material phases. This capability was particularly impressive as it could predict phase transformations accurately despite ignoring solvent molecules, which were believed to be crucial. The traditional approach of using continuum solvent models proved to be inefficient and limited in generalizability, requiring manual effort in parameter fitting and equation building. In contrast, the neural network model automatically learned to construct a continuum-solver model in just a few hours, demonstrating its efficiency and ability to reveal emergent phenomena that were previously thought to be impossible. This breakthrough showcased the potential of neural networks in material science, offering a faster and more effective alternative to traditional models.
Get the Snipd
podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered
Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any
moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share
& Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered
podcast player
Listen to all your favourite podcasts with AI-powered features
Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode