Google Deepmind AI tool discovers 2.2 million new materials and creates robotic laboratory to synthesize these new materials

Amazing stuff! Mind boggling! Breathtaking! This is just the beginning!

Google managed to have two related scientific papers published in the journal Nature on the same day! That by itself is a feat!

"... We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials. ...
We’ve made GNoME’s predictions available to the research community. We will be contributing 380,000 materials that we predict to be stable to the Materials Project, which is now processing the compounds and adding them into its online database. ..."

"... Among the new discoveries are "52,000 new layered compounds similar to graphene that have the potential to revolutionize electronics with the development of superconductors," ... "Previously, about 1,000 such materials had been identified. We also found 528 potential lithium ion conductors, 25 times more than a previous study, which could be used to improve the performance of rechargeable batteries." ...

What's more, the Deepmind team has also worked with Berkeley Lab to create and demonstrate a robotic laboratory capable of synthesizing these new crystals autonomously. In a paper published today, the Deepmind team reported that the robotic lab has already successfully synthesized 41 of these new materials – the potential for further acceleration here is remarkable. ..."

From the abstract:

"To close the gap between the rates of computational screening and experimental realization of novel materials, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics."

From the abstract:

"Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Building on 48,000 stable crystals identified in continuing studies, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Our work represents an order-of-magnitude expansion in stable materials known to humanity. Stable discoveries that are on the final convex hull will be made available to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. Of the stable structures, 736 have already been independently experimentally realized. The scale and diversity of hundreds of millions of first-principles calculations also unlock modelling capabilities for downstream applications, leading in particular to highly accurate and robust learned interatomic potentials that can be used in condensed-phase molecular-dynamics simulations and high-fidelity zero-shot prediction of ionic conductivity."

Deepmind AI tool catapults materials science 800 years into the future

Millions of new materials discovered with deep learning AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies

An autonomous laboratory for the accelerated synthesis of novel materials (open access)

Scaling deep learning for materials discovery (open access)

Fig. 1: Autonomous materials discovery with the A-Lab.

Fig. 1: GNoME enables efficient discovery.