AI-Driven Materials Discovery: Orbital Materials’ Innovative Approach to Combat Climate Change
In a groundbreaking effort to address climate change, New Jersey-based start-up Orbital Materials is leveraging artificial intelligence (AI) to discover materials aimed at reducing the reliance on fossil fuels. Launched in 2022, the company is on a mission to transform how materials are synthesized and commercialized, all while contributing to cleantech advancements that could revolutionize the industry.
At the Forefront of Materials Science
Founders and Funding
Orbital Materials, co-founded by James Gin-Pollock, Jonathan Godwin, and Daniel Miodovnik, is headquartered in both London and Princeton, New Jersey. The venture has raised an impressive $21 million from notable investors, including Radical Ventures, Sequoia Capital, and Toyota Ventures.
Innovative AI Model
The cornerstone of Orbital’s approach is its proprietary AI model, which was developed to predict the properties of new materials utilizing public datasets, computer simulations, and in-house experimental results. This technology has the potential to streamline the materials discovery process, focusing on pivotal applications relevant to clean technology, such as catalysts for biobased chemical production and advanced water treatment solutions.
One of the most promising innovations emerging from Orbital’s lab is a sorbent material designed for capturing carbon dioxide from the atmosphere, offering a potentially lower-cost alternative to current carbon capture technologies.
From DeepMind to Orbital
Jonathan Godwin’s Vision
Before co-founding Orbital, Jonathan Godwin honed his skills at DeepMind, Google’s AI research subsidiary, where he trained AI systems for materials discovery. Frustrated with the limitations of translating AI predictions into tangible materials at a tech giant, he took the bold step to create a company dedicated to making a real-world impact.
"Our AI model starts with a random distribution of atoms," Godwin explains. "It analyzes their interactions and iteratively rearranges them, gradually honing in on useful new materials."
The Challenges Ahead
Predictive Limitations in AI Models
While Orbital’s AI has shown promise in predicting certain properties, such as material absorption capabilities, challenges remain regarding stability and manufacturability. Godwin emphasizes the importance of pairing the AI’s efficiency with the invaluable intuition and experience of seasoned chemists.
"We needed a different type of company—one that has strong industrial chemists who can successfully bring materials to market," he notes.
Striving for Commercialization
Getting to Market
Orbital Materials aims not only to synthesize new materials but also to validate them within their intended applications to ensure functionality. The company intends to join forces with major firms for scale-up and commercialization, facing emerging engineering challenges head-on.
To spearhead these efforts, Godwin recruited Thomas McDonald as the firm’s Chief Scientific Officer. With a deep background in carbon capture materials—having co-founded Mosaic Materials, which was acquired by Baker Hughes—McDonald understands the lengthy journey from laboratory innovation to commercialization. He believes AI can significantly shorten these timelines.
"If we get this right, we can set a new precedent for research and development," McDonald states.
Industry-Wide Inspirations
Many organizations are eager to incorporate AI into material creation, from tech giants like Google to newer enterprises such as Materials Nexus. Even traditional chemical firms are exploring this frontier; BASF’s venture capital arm recently invested in AI-focused materials discovery venture, Solve.
Despite exciting prospects, experts warn that significant challenges lie ahead. Milad Abolhasani, a researcher at North Carolina State University, points out that the lack of standardized data can hinder the effectiveness of AI models, creating hurdles for wider adoption across the industry.
Embracing Collaborative Innovation
AI as a Co-Pilot
Robotic laboratories that utilize AI to perform automatic experiments will undoubtedly help bridge existing data gaps, according to Abolhasani. However, many researchers are only beginning to explore these systems. At this stage, Godwin asserts that AI should augment rather than replace human expertise in chemistry.
"The software acts as a copilot," he elaborates. "It serves as a hypothesis generator and a creativity aid for chemists."
Join the Conversation
As Orbital Materials forges ahead in the quest for sustainable solutions, it exemplifies the innovative potential of AI in materials discovery. The potential applications are vast, promising enhanced efficiency in combating climate change while reshaping the future of chemistry.
What are your thoughts on the intersection of AI and materials science? Join the conversation below and share your insights!
