Accelerating Discovery: How the Materials Project Is Helping to Usher in the AI Revolution for Materials Science

The Materials Project platform uses high-throughput computational modeling at the National Energy Research Scientific Computing Center (NERSC) to screen large libraries of materials for specific purposes. Properties are calculated using advanced computational methods and validated against real-world experiments. This approach allows researchers to rapidly test and evaluate many different materials, accelerating the discovery process.

The platform also provides standardized datasets formatted for training machine-learning systems, including detailed information about a material's electron density. Such curated data allow researchers to validate new AI models against performance benchmarks. This extensive preparation eliminates the months typically required to assemble and clean materials datasets, allowing researchers to focus on developing new AI algorithms and making scientific discoveries.

During the pandemic, the Materials Project's AI-readiness allowed materials research to continue despite site-access restrictions to experimental research laboratories. "Experimental materials scientists who traditionally performed hands-on laboratory experiments turned to digital tools to analyze data and run simulations while working remotely. And today, a modern platform like the Materials Project is now expected to operate around the clock to support a user community that has grown by a factor of 2.5 since May 2022," said Huck.

To support this growing demand, Huck and team worked with industry partners such as MongoDB, a leading database for modern applications, the observability platform Datadog, and the cloud computing provider Amazon Web Services to migrate the Materials Project to a cloud-based infrastructure that supports everything from rapid property searches to massive data downloads, and interactive tools enabling real-time exploration of how different materials relate to each other. This innovative cloud infrastructure ensures a 99.98% uptime, the industry standard for high availability.

From database to discovery

The Materials Project has been adopted across universities, research labs, and companies worldwide, serving research into batteries, semiconductors, catalysts, and structural materials.

"The Materials Project serves as a strong bridge between industry and academia by providing the entire research community with transparently developed open-source tools."
- Brian Storey, Toyota Research Institute Vice President

Longtime user Toyota Research Institute (TRI), which is headquartered in Los Altos, California, and has facilities in Cambridge, Massachusetts, and Ann Arbor, Michigan, has relied on the Materials Project's open-source tools and data to develop new materials. TRI is a research and scientific development subsidiary of Toyota Motor Corporation focused on developing technologies in artificial intelligence, vehicle automation, materials science, and robotics.

TRI researchers reported the discovery of LiMOCl4 (M=Nb, Ta), new solid electrolytes for solid-state batteries, through a molecular structure identified in the Materials Project. Researchers are interested in advancing solid-state batteries to overcome the limitations in charging and efficiency of current lithium-ion battery technologies.

"The Materials Project serves as a strong bridge between industry and academia by providing the entire research community with transparently developed open-source tools. Almost every industrial effort focused on AI for materials discovery - either at established companies or new startups - is being led by one of the many brilliant young scientists who have been trained at the Materials Project. Their fingerprints are everywhere," said Brian Storey, Toyota Research Institute Vice President.

The Microsoft Corp. has also used the Materials Project to train models for materials science, most recently to develop a tool called MatterGen, a generative model for inorganic materials design. Microsoft Azure Quantum developed a new battery electrolyte using data from the Materials Project.

Other notable studies used the Materials Project to successfully design functional materials for promising new applications. In 2020, researchers from UC Santa Barbara, Argonne National Laboratory, and Berkeley Lab synthesized Mn_1+xSb, a magnetic compound with promise for thermal cooling in electronics, automotive, aerospace, and energy applications. The researchers found the magnetocaloric material through a Materials Project screening of over 5,000 candidate compounds.

In addition to accessing the vast database, the materials community can also contribute new data to the Materials Project through a platform called MPContribs. This allows national lab facilities, academic institutions, companies, and others who have generated large data sets on materials to share that data with the broader research community.

Other community contributions have expanded coverage into previously unexplored areas through new material predictions and experimental validations. For example, Google Deepmind - Google's artificial intelligence lab - used the Materials Project to train initial GNoME (graph networks for materials exploration) models to predict the total energy of a crystal, a key metric of a material's stability. Through that work, which was published in the journal Nature in 2023, Google DeepMind contributed nearly 400,000 new compounds to the Materials Project, broadening the platform's vast toolkit of material properties and simulations.

The Materials Project contributes or manages more datasets registered with the Department of Energy's Office of Science and Technical Information (OSTI) Data ID Service than any other platform, signifying its leadership in open science and data sharing, and setting standards for data management and accessibility through search engines such as Google Dataset Search. Today, it is just one of seven DOE Office of Science Public Reuseable (PuRe) Data Resources that make curated data publicly available to further scientific discovery and technical knowledge.

The platform's vast library of materials data has not only helped to inspire new energy technologies but also the next generation of materials scientists. "Grad students, postdocs, and professors at public and private colleges and universities rely on the Materials Project to be available 24/7 as a resource for their research. The fact that we're getting cited in research papers more than six times a day on average now shows how much of an educational resource the Materials Project has become in just a decade," said Huck.

Connecting to autonomous labs

As materials science embraces data-driven discovery, the Materials Project's curated datasets position it as an essential infrastructure for AI-powered materials design. The platform is continuing to evolve its machine learning capabilities, with plans for enhanced computational methods and improved handling of complex materials behavior.

"One of the exciting areas that we've been working on is connecting this simulation pipeline to autonomous experiments carried out at Berkeley Lab's A-Lab. Not only are we simulating things in the computer, but we're also bringing new materials into reality," said Jain.

The A-Lab is a fully automated lab that uses robots guided by artificial intelligence to speed up materials science discoveries. Since its launch in 2023, the A-Lab has collaborated with the Materials Project to synthesize novel materials with promise for future technologies.