Not going to do all your legwork for you, but there are tons of fields that are changing rapidly because of AI. In Material Science, there's a thorny problem of how to accelerate material development, or even how to perform non-destructive testing of materials.
> AI, primarily through generative AI models, has dramatically changed our approach by accelerating the design process significantly. These models can predict material properties from extensive datasets, enabling rapid prototyping and evaluation that used to take years. We can now iterate designs quickly, focusing on the most promising materials early in the development phase, enhancing both efficiency and creativity in materials science. This is a huge leap forward because it reduces the time and cost associated with traditional materials development, allowing for more experimentation and innovation.
> One notable application is using deep learning models to infer the internal properties of materials from surface data. This technology is groundbreaking, particularly for industries like aerospace and biomedical, where non-destructive testing is crucial. These models can predict internal flaws or stresses by analyzing external properties without physically altering the material. This capability is essential for maintaining the integrity of critical structures and devices, making materials safer and more reliable while saving time and resources. Other recent advances are in multimodal AI, where such models can design materials and understand and generate multiple input and output types, such as text, images, chemical formulas, microstructural designs, and much more.
I don't consider that anything good. Design is just about making new products faster, which is a bad thing as it accelerates consumerism. And medical scans? That might help maybe a thousand extra people at the cost of gigagwatts of energy used that is polluting the entire planet.
To me, all of those positives are dwarfed by negatives.
Increasing research iteration speed is not speculation. Showing double the rate of detecting issues in scans is also not speculation.
Drawing distinctions between LLMs and other kinds of ML and AI is not particularly interesting: it's all machines using pattern recognition to automate things that previously took thought.
> AI, primarily through generative AI models, has dramatically changed our approach by accelerating the design process significantly. These models can predict material properties from extensive datasets, enabling rapid prototyping and evaluation that used to take years. We can now iterate designs quickly, focusing on the most promising materials early in the development phase, enhancing both efficiency and creativity in materials science. This is a huge leap forward because it reduces the time and cost associated with traditional materials development, allowing for more experimentation and innovation.
> One notable application is using deep learning models to infer the internal properties of materials from surface data. This technology is groundbreaking, particularly for industries like aerospace and biomedical, where non-destructive testing is crucial. These models can predict internal flaws or stresses by analyzing external properties without physically altering the material. This capability is essential for maintaining the integrity of critical structures and devices, making materials safer and more reliable while saving time and resources. Other recent advances are in multimodal AI, where such models can design materials and understand and generate multiple input and output types, such as text, images, chemical formulas, microstructural designs, and much more.
https://professional.mit.edu/news/articles/revolutionizing-m...
There's lots of other examples.
New ways to create COVID vaccines: https://www.nature.com/articles/s41586-025-09442-9
More effective than humans at reading medical scans: https://www.weforum.org/stories/2025/03/ai-transforming-glob...
AI is already intensely useful, and will only continue to improve.