Our research focuses on advancing translational digital technologies that leverage high-performance computing (HPC) and machine learning (ML) to augment, simplify, or even supplant traditional physical experimentation in scientific research and development (R&D).

 

The central objective revolves around formulating pioneering and efficient algorithms, along with software tools that can harness cutting-edge supercomputing capabilities to simulate and predict the physicochemical behavior of matter with an accuracy comparable to, or even surpassing, that of physical experiments.

 

The software solutions delivered by our research agenda yield a tangible impact on R&D in engineering and fundamental sciences, providing a more cost-effective, versatile, and fully automatable alternative to physical experiments.

 

This transformative approach accelerates the rate innovation and discovery, ushering in a new era of swift and pivotal leaps in key technological domains including but not limited to the design of novel therapeutics, catalysts, and meta-materials with unusual and highly desirable properties.