Orbital Materials
Assembly Line
Orbital Materials and AWS enter strategic partnership to develop technologies for data center decarbonization and efficiency
Orbital Materials (Orbital), a company that uses its proprietary AI platform to incubate new advanced materials and climate technologies, and Amazon Web Services (AWS), an Amazon.com company, have announced that they have entered into a multi-year partnership to use AI to develop new data center decarbonization and efficiency technologies.
Orbital is utilizing its proprietary AI platform to design, synthesize and test new technologies and advanced materials for data center-integrated carbon removal and cooling technology. Together, AWS and Orbital will evaluate the scalability and performance of these new technologies in removing carbon and increasing efficiency.
Developing new advanced materials has traditionally been a slow process of trial and error in the lab. Orbital replaces this with generative AI design, radically improving the speed and efficacy of materials discovery and new technology commercialization. Its first product is a carbon removal technology utilizing a proprietary active material. Since establishing its lab in the first quarter of 2024, Orbital has achieved a 10x improvement in its material’s performance through the use of its AI platform - an order of magnitude faster than traditional development and breaking new ground in carbon removal efficacy. Orbital plans to deploy and test its carbon removable technology by the end of 2025.
Introducing ‘Orb’ - the world’s fastest and most accurate AI model for simulating advanced materials
We are proud to release “Orb” - the world’s best AI model for advanced material simulation. It is more accurate than comparable models from Google and Microsoft and 5x faster for large-scale simulations than the leading available alternative. We are releasing Orb under a permissive open-source license - free for non-commercial uses and startups, to maximise the impact of this technology and accelerate development efforts of teams around the globe.
Orb is Orbital Materials’ AI-based universal interatomic potential, designed for simulating advanced materials at scale. It achieves state of the art performance in terms of both speed and accuracy relative to other AI-based interatomic potentials. Orb models can be used directly for accurate energy estimation and geometry/cell optimization of crystalline materials, as well as being fast enough to be used directly in molecular dynamics or monte carlo simulations.
Orb uses an attention augmented Graph Network-based Simulator (GNS), a type of Message Passing Neural Network (MPNN). MPPNs operate on graphs and have an iterative message passing phase, in which latent representations of each node are updated as an aggregation of messages passed between a node’s neighboring nodes and edges. In physical terms, early iterations of message passing capture local atomic interactions, which are hierarchically re-used and composed in later iterations to model larger chemical structures.
Foundation Models for Materials Discovery: Our Investment in Orbital Materials
Fortunately, innovations in artificial intelligence have led to the emergence of foundation models, which are trained on vast amounts of data and leading to models that can be used across numerous applications. Those foundation models have the potential to enable inverse design, a method of material development that expedites the process by using the specific required properties as an input and generating the new material design as an output. This approach has the potential to revolutionize material development across industries, which is why we are excited to announce Toyota Ventures’ investment in Orbital Materials through our Frontier Fund.
The team has trained a 3D foundation model, named LINUS, for crystal structures and small molecules. Instead of screening millions of materials in hopes of finding one with a specific property, LINUS generates a material based on a given property in a single calculation. To do this, the team has developed a new version of the “transformer”, a model typically used for natural language processing, to allow the model to learn the relationships between the 3D structures of materials and their properties. Advanced materials that absorb and catalyze are crucial in various industries such as carbon capture, sustainable fuels, water treatments, biofeedstock upgrades, and battery recycling.