Dennis Sheberla
Papers
1
Total Citations
775
H-Index
1
About
Dennis Sheberla is a leading researcher at the frontier of computational materials science and clean energy innovation. His work focuses on accelerating the discovery of novel materials for energy applications by integrating high-throughput computation, machine learning, and automated synthesis. Sheberla’s most cited work, "Accelerating the discovery of materials for clean energy in the era of smart automation" (2018), has garnered over 775 citations and lays out a visionary framework for combining robotics, artificial intelligence, and traditional chemistry to rapidly identify and develop next-generation energy materials. This influential perspective has helped shape the emerging field of self-driving laboratories. Beyond this landmark review, Sheberla is known for his contributions to the design and discovery of conductive metal-organic frameworks (MOFs) and porous materials for energy storage and conversion. His research has been instrumental in demonstrating how computational screening can dramatically reduce the time and cost of materials development. With a citation impact exceeding 775 for his most recognized work alone, Sheberla continues to drive the paradigm shift toward smart, automated materials discovery for a sustainable energy future.
Research Focus
Key Achievements
Top Papers
- 1