Materials Project Cited 32,000 Times, Accelerating Battery & Quantum Computing

The Materials Project, launched by a team at Lawrence Berkeley National Laboratory, is the most-cited resource for materials data and analysis tools. It has been cited over 32,000 times in peer-reviewed studies, driving advancements in fields like battery technology and quantum computing. Today, the project serves more than 650,000 registered users worldwide.

Materials Project: Impact on Materials Science Research

The Materials Project facilitates rapid materials discovery through high-throughput computational modeling at NERSC, screening vast libraries for targeted properties. Calculations utilizing advanced methods are validated with experiments, enabling researchers to quickly evaluate materials and accelerate innovation—currently encompassing over 200,000 materials and 577,000 molecules. This computational approach provides a significant advantage when experimental data is limited, as is the case for over 99% of known compounds. This platform delivers massive datasets—465 terabytes in the last two years—specifically formatted to train machine-learning systems and includes details like electron density.

By eliminating months of data preparation, researchers can focus on algorithm development and new discoveries; this AI-readiness proved critical during pandemic-related lab closures. A recent migration to cloud infrastructure, ensuring 99.98% uptime, supports a user base that has grown 2.5 times since May 2022.

Kristin Persson’s Vision: Automated Materials Screening

Kristin Persson’s vision centered on creating an automated materials screening tool to accelerate the design of new materials, particularly for energy technologies. The Materials Project delivers access to over 200,000 computed materials and 577,000 molecules, providing a vast resource for researchers without requiring programming expertise. This open-source framework, powered by supercomputers, aims to democratize materials knowledge and encourage collaborative innovation. The platform utilizes high-throughput computational modeling to rapidly assess materials, calculating properties and validating them with experiments.

This approach delivers 465 terabytes of data—enough for millions of high-resolution photos—and crucially prepares datasets for machine learning applications, saving researchers months of data preparation. By providing AI-ready, curated data, the Materials Project enables faster materials discovery and supports ongoing research even with limited access to physical laboratories.

650,000 Users & 32,000 Citations Demonstrate Growth

With over 650,000 registered users, the Materials Project is demonstrably popular, experiencing a 2.5x growth in its user base since May 2022. Daily usage reaches 5,000 users, indicating consistent reliance on the platform’s resources for materials science research. The impact of the Materials Project extends beyond user numbers, evidenced by its over 32,000 citations in peer-reviewed studies. This widespread recognition fuels advances in areas like battery technology, quantum computing, and industrial catalysts. In the last two years alone, the platform delivered 465 terabytes of data, supporting machine learning applications and accelerating materials discovery by eliminating extensive data preparation.

NERSC Supercomputers Enable High-Throughput Computation

NERSC supercomputers are fundamental to the high-throughput computational modeling at the core of the Materials Project. This platform leverages NERSC’s capabilities to screen vast libraries of materials, calculating properties using advanced methods and validating them with experimental data. Consequently, researchers can rapidly assess numerous materials, significantly speeding up discovery and design processes. The platform’s use of NERSC enables standardized datasets perfectly formatted for machine-learning training, including detailed electron density information. Supporting a community exceeding 650,000 users, the infrastructure ensures 99.98% uptime for continuous access to this data powerhouse.

AI-Ready Data & Cloud Infrastructure Support Discovery

The Materials Project facilitates AI development by providing curated, high-quality data, essential for training effective machine-learning models. This allows scientists to concentrate on algorithm development and accelerate materials discovery in areas like batteries and catalysts. This ensures 99.98% uptime and supports rapid data access, property searches, and interactive exploration of material relationships. Standardized datasets, including detailed electron density information, enable researchers to validate new AI models against established benchmarks.