AI comes up with battery design that uses 70 per cent less lithium

>rediscovered a decade's worth of collective knowledge

Unless one can guarantee this knowledge wasn't in the training data, it's not rediscovered, it's regurgitated.

Good example of the bias that exist with AI: when the output/performance can be interpreted in two different manners, I consistently find people unconsciously choosing the option painting the AI in a better light.

I don't think AI / deep learning is useless, but I do think it's overrated at the moment.

They used a machine-learning model to predict the physical properties of each material. That is how they managed to narrow it down from millions of possibilities and find something worth actually trying to synthesize.

In an age where everyone questions if conventional intelligence does work, artificial intelligence will surely be popular.

But yes, it probably is just expert software. We sometime call it intelligent, although it may just be some physics model with a lot of experience in engineering pragmatic assumptions to reduce brute forcing likely optimal solutions.

From the paper’s introduction:

> Machine learning (ML) models for materials science have the potential to vastly expedite the computational discovery process. State-of-the-art ML models can predict the results of physics-based quantum mechanical calculations but are several orders of magnitude faster, making them ideal for predicting general material properties [5–7]. In addition to direct property prediction, combining universal ML potentials such as M3GNet [8], CHGNet [9], and GNoME [10] has made it possible to perform geometric optimization, and hence evaluate thermodynamic stability, for arbitrary combinations of elements and structures. The significant speed advantage of ML-based techniques over direct simulation has made it possible to explore materials across a vast chemical space that greatly exceeds the number of known materials.

Models mentioned:

M3GNet: M3GNet is a new materials graph neural network architecture that incorporates 3-body interactions. A key difference with prior materials graph implementations such as MEGNet is the addition of the coordinates for atoms and the 3×3 lattice matrix in crystals, which are necessary for obtaining tensorial quantities such as forces and stresses via auto-differentiation.

Source: https://materialsvirtuallab.github.io/m3gnet/

CHGNet: A pretrained universal neural network potential for charge-informed atomistic modeling (see publication). Crystal Hamiltonian Graph neural Network is pretrained on the GGA/GGA+U static and relaxation trajectories from Materials Project, a comprehensive dataset consisting of more than 1.5 Million structures from 146k compounds spanning the whole periodic table.

Source: https://github.com/CederGroupHub/chgnet

GNoME: GNoME Is A State-Of-The-Art Graph Neural Network (GNN) Model. The Input Data For GNNs Take The Form Of A Graph That Can Be Likened To Connections Between Atoms, Which Makes GNNs Particularly Suited To Discovering New Crystalline Materials.

Source: https://gmnomeai.cloud/

Honestly I hate this new trend of AI/ML papers not even describing the model anymore. They used a graph neural network model called M3Gnet. It's mentioned in the APPENDIX.

Don’t guess. RTFP.

This is the next thing, a renewable type of battery storage device that is also carbon net negative ? Sign me the fuck up ! Who wants to fund the pot-A.I.-to battery startup with me ?

The potatoes need to be suggested by the AI first.

Another case https://twitter.com/ylecun/status/1746547187234775304

You think this is restricted to US manufacturing? It's the "Organic" everything of today.

This is a finance disease at the moment—I don't think it's particularly related to the US beyond finance's relation to the US.

> Vijay Murugesan at the Pacific Northwest National Laboratory in Washington state was one of the scientists who picked up the phone. He and his colleagues suggested additional screening criteria for the AI. After more elimination rounds, Murugesan’s team ultimately picked one of the AI’s suggestions to synthesise in the lab. It stood out because half of what Murugesan would have expected to be lithium atoms were replaced with sodium. He says that this is a very novel recipe for an electrolyte and that having the two elements together opens questions about the basic physics of how the material works inside a battery.

Seems it wasn't quite so obvious.

In all fairness to this article...

It does look like they are describing a sort of Lithium/Sodium mix rather than a 'pure' Sodium-ion battery, which is (at least to me) slightly novel.

It would then raise a bigger question: If the AI 'came up with it', does that mean that it can't be patented? Or does that depend on whether it came up with the manufacturing technique?

There are other axes to consider. Cost, thermal stability, supply chain reliability, toxicity. Any of which might make these “better” than lithium-ion for some applications (such as home or datacenter UPS batteries).

They used a machine-learning model to predict the physical properties of each candidate material. Since ML and AI are (incorrectly) basically synonymous at this point, the news outlet is probably the one that s/ML/AI/'d.