The AI-for-materials landscape in 2026: who is building what

Materials discovery went from an academic side-project to a funded race over the last two years. This post is a plain roundup of who has shipped what — no Accretion numbers, just the public record, for anyone trying to understand the field before picking a vendor or a research direction.

The model layer

Google DeepMind's GNoME (Graph Networks for Materials Exploration) used graph neural networks to screen candidate crystal structures at a scale no manual search could match, expanding the catalog of known stable inorganic materials by roughly an order of magnitude — from the tens of thousands into the hundreds of thousands. Microsoft Research followed with MatterGen, a generative diffusion model that designs a structure directly from a target property rather than screening a candidate list, paired with MatterSim, a simulator for evaluating those designs under realistic conditions.

The specialists

Schrödinger, longer-established in computational chemistry, has extended its physics-based simulation platform with machine learning for both materials and drug discovery — a reminder that the ML layer works best paired with a physics engine that already understands the domain. A newer cohort — Orbital Materials and CuspAI among them — is applying the same generative-plus-simulation pattern to narrower, high-stakes targets: clean energy materials and carbon capture sorbents specifically, rather than materials discovery in general.

The pattern across all of it is consistent: generative models propose, graph neural networks estimate properties cheaply, and physics-based simulation (usually DFT) checks the results that matter before anyone trusts them enough to synthesize. That three-stage loop is not any one company's secret — it is becoming the field's default architecture, which is exactly why the differentiator is shifting from "do you have a model" to "how rigorously do you validate what it proposes."

We built Accretion Core around the same loop for that reason. Where we think we differ is covered on the Platform page — this post is deliberately about everyone else.