February 22, 2022
Staff Accomplishment

De Yoreo and Karniadakis Elected to the National Academy of Engineering

Two researchers among 111 new members elected in 2022

Two white men shown from the shoulders up on a pale background

Materials scientist Jim De Yoreo and applied mathematician George Karniadakis were elected to the National Academy of Engineering for their contributions to their respective fields.

(Composite image by Cortland Johnson | Pacific Northwest National Laboratory)

Two scientists from Pacific Northwest National Laboratory (PNNL), Jim De Yoreo and George Karniadakis, were elected to the National Academy of Engineering this year. De Yoreo is a Battelle fellow and chief scientist for Materials Synthesis and Simulation Across Scales at PNNL. Karniadakis is the Director of the Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PhILMs) project and a PNNL joint appointee with Brown University. They are two of 111 new members elected in 2022.

“I’m delighted to be elected to the National Academy of Engineering,” said De Yoreo. “It’s such an honor.”

Election to the National Academy of Engineering represents a high professional distinction for engineers. It honors individuals who have made outstanding contributions to research, education, or technical advances across engineering fields.

De Yoreo was recognized for his “advances in materials synthesis from nucleation to large-scale crystal growth.” His research interests focus on understanding the physics of materials formation, with a goal of developing knowledge on the relationship between synthesis conditions and the resulting material structures.

“Jim’s work has greatly enhanced the world’s understanding of how crystals and materials form,” said Lou Terminello, the associate laboratory director of the Physical & Computational Sciences Directorate at PNNL. “His international leadership in materials science makes this honor well deserved.”

De Yoreo is a physicist by training who holds a Ph.D. from Cornell University in physics. However, much of his work can be classified as materials science and engineering. His research has spanned a wide range of materials-related disciplines, focusing most recently on in situ atomic force microscopy and transmission electron microscopy investigations of interactions, assembly, and crystallization in biomolecular, biomineral, and nanoparticle systems.

De Yoreo has spent over three decades researching at national laboratories. He was the founding co-director of the Northwest Institute for Materials Physics, Chemistry, and Technology and is the deputy director of the Center for the Science of Synthesis Across Scales. He has joint appointments in the Materials Science and Engineering and Chemistry Departments at the University of Washington, where he actively mentors graduate student researchers. De Yoreo is a Fellow of the American Physical Society and the Materials Research Society, a Department of Energy Distinguished Scientist Fellow, and a member of the Washington State Academy of Sciences.

Karniadakis was selected by the National Academy of Engineering for his “computational tools, from high-accuracy algorithms to machine learning, and applications to complex flows, stochastic processes, and microfluidics.” Karniadakis is the Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering at Brown University and holds a joint appointment in PNNL’s Computational Mathematics group.

“I think that the community has recognized the efforts of my research group for over 30 years now to develop useful algorithms for research in industry and the national labs,” said Karniadakis. “Our philosophy has always been to look at tough science and engineering problems for which computational methods don't exist, or they are inadequate, and try to fill this gap by developing new algorithms on high-order discretizations, uncertainty quantification, multiscale modeling and scientific machine learning. My work at PNNL in the last eight years also reflects this philosophy and I am happy to be working with PNNL researchers and have the support of the Lab.”

At PNNL, Karniadakis directs the PhILMs project in the Advanced Computing, Mathematics, and Data Division. “George’s pioneering research on physics-informed neural networks has been instrumental to advance the foundations of scientific machine learning,” said Robert Rallo, Advanced Computing, Mathematics, and Data division director.