So they did a brute force search through possible candidates to find promising ones. Which makes a lot of sense but ... AI?
Guessing they need more funding now so they're making it sound like it's part of the LLM fad.
> 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/
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.
Nowhere in the article linked to by HN they say that, so it's easy to assume their use of ML is just marketing.