Satya Nadella on LinkedIn: With our new MatterGen model, we’re applying the next generation of AI to…


AI = Sustainability? 🧠🌳

I believe AI will become a powerful force for a systems change and real sustainability, that we never (really) knew we needed.

Similar to how Generative AI (input to output) will morph into Regenerative AI (adapt & evolve) in the coming couple of years, the way we make physical goods and products will also go through a paradigm shift.

How could this pan out? 👇

Firstly, in the next five years or so, embedded AI will enable physical products to perform complex human tasks (by becoming robots), which enables usership instead of ownership as the more economically rational business model (e.g. pay for autonomous mobility), which in turn promotes the incentive to make products for longevity and renewability. ”Squeeze out more cashflow from our circulating product”. This would also promote a sharing economy and avoid overconsumption.

Secondly, AI will enable factory robots to both make and remake a much wider spectrum of products than today, effectively decreasing the comparative advantage of low-cost labor. This promotes manufacturing in close proximity to point of (usership) consumption. Reshoring, after 50 years of offshoring. This would avoid a lot of transport, and create new jobs in high-cost regions.

Thirdly, with closer manufacturing proximity, the interest for highly advanced renewable materials could substantially increase. The past and current (good) craze for renewable materials is a precursor for what might happen for renewable materials. But we need AI to scale this deploy- and development. AI will lead to regenerative materials. And THIS is why the article below is super intriguing (it’s a signal for a lot more to come). 👀

And lastly, when renewable materials starts replacing depletive materials, the inherent volatility in our economy will decrease. Theoretically, servicing customers through renewable materials could stabilize our economy, conflicts, and inflation. Among other things..

In essence, I believe that: 

🔑 AI is an essential key to unlock the current linear gridlock and business restructuring. AI is tech, business models, demographics, and climate; everything all at once.

🔬 AI embedded into the physical world is a greatly underappreciated theme. It’s mostly about software now, but the end-game is hardware. (AI will likely mirror the 85% physical GDP).

🦄 Usership is a trojan horse (enabled by AI) for enabling a regenerative incentive, and a wave from which next-gen unicorns will arise.

🌳 Shift from ”more stuff is more profits” to ”less stuff is more profits” – enabled by AI.

#AI #sustainability

View profile for Tian Xie

Researcher & Project lead at Microsoft Research | AI for materials discovery


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Generative AI has revolutionized how we create text, images and code. How about new materials? We at Microsoft Research #AI4Science are thrilled to announce MatterGen: our generative model that enables broad property-guided materials design.

Link 👇
https://lnkd.in/dedfdbbq

The central problem in materials science is to discover materials with desired properties. Traditionally, it has been done by first finding novel materials and then filtering down based on the application. This is like trying to generate the image of a cat by first creating a million different images and then searching for the one with a cat.

MatterGen is a diffusion model that can instead directly generate novel materials with desired property conditions – including chemistry, symmetry, and material properties – similar to how DALL·E 3 tackles image generation.

MatterGen outperforms a previous SOTA model (CDVAE) in generating 2.9 times more stable and novel structures, and produces structures that are 17.5 times closer to the energy local minimum. It also outperforms screening in proposing high bulk modulus candidate structures, and improves upon substitution and random structure search when targeting a particular chemical system.

We believe MatterGen is an important step forward in AI for materials design. Our results are currently verified via DFT, which has many known limitations. Experimental verification remains the ultimate test for real-word impact, and we hope to follow up with more results.

None of this would be possible without the highly collaborative work between Andrew Fowler, Claudio Zeni, Daniel Zuegner, Matthew Horton, Robert Pinsler, Ryota Tomioka, and myself, our amazing interns Xiang Fu, Aliaksandra Shysheya, and Jonathan Crabbé, as well as Jake Smith, Lixin Sun and the entire AI4Science Materials Design team. We are also grateful for all the help from the Microsoft Research AI4Science team and Microsoft Azure Quantum team.

#artificialintelligence #generativeai #materialsscience #ai4science #microsoft #ai #materials

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