UC San Diego NanoEngineers to lead MRSEC research thrust on Predictive Assembly
|Professors Andrea Tao (left) and Tod Pascal lead the predictive assembly research thrust of the $18 million grant.|
San Diego, Calif., July 8, 2020 -- In some ways, the field of materials science is where the pharmaceutical sciences were twenty years ago. A team of University of California San Diego researchers is working to change that. The team makes up the "predictive assembly" research thrust of the new $18M Materials Research Science and Engineering Center (MRSEC) funded by the National Science Foundation (NSF).
Today, computational and predictive tools are used in the pharmaceutical industry in order to design "small molecule" drugs with particular properties and behaviors. The challenge is that the design-before-you-synthesize approach hasn't worked for the larger-scale materials that are critical for many applications beyond small-molecule drugs. That's the work that will be done by the team led by nanoengineering professors Andrea Tao and Tod Pascal from the UC San Diego Jacobs School of Engineering.
Andrea Tao uses surface chemistry and self-assembly concepts to build mesoscale materials. Tod Pascal couples theory with high performance computing to predict structure, properties, and microscopic signatures of complex materials.
Together, Tao and Pascal tapped into a diverse set of expertise at UC San Diego in order to create a team that will change the way advanced materials are designed and synthesized. The team includes UC San Diego experts in inorganic chemistry, condensed matter physics, nanoengineering, and computational and data sciences.
“Many of the life-saving drugs we have today were born in computer simulations from twenty years ago—those calculations revealed key interactions of molecules with their cellular targets, providing insights into how they might perform in the body,” said Tao. “We’d like to do the same for nanoscale molecules and particles, not just to understand how they perform on their own, but also to understand how we can use these objects like building blocks to construct new types of materials.”
“Many of the properties of a material that might be useful in building something like an enzyme-selective membrane for energy storage applications or a switchable optical metamaterial for holographic displays do not emerge until that material gets to a size much larger than a typical drug molecule,” said UC San Diego nanoengineering professor Tod Pascal. He leads the computational effort of the MRSEC and is co-director of the predictive assembly team.
“Indeed, we are only now starting to appreciate that the collective behavior of systems with lots of smaller subunits is quite different than systems with only two or three of these building blocks, a phenomenon known as emergent behavior,” said Pascal.
With the unparalleled capabilities within UC San Diego's San Diego Supercomputer Center, Halicioglu Data Sciences Institute, and the NSF-funded Science Gateways Community Institute, Tao, Pascal, and their team are deploying the most advanced computational tools available to understand and design materials assembly processes from the ground up. This is an ambitious endeavor that has previously been intractable.
“Our strategy is to start with simulations based on accurate quantum mechanics, so that we are confident we can describe the microscopic interactions in these building blocks correctly,” Pascal continued. “Research in the lab of Francesco Paesani in the chemistry department has pioneered efficient approaches for doing this, with the latest advances using artificial intelligence and machine learning to accelerate these calculations.
"From there, we construct more approximate models that still retain the critical physics, such as those being developed in the lab of Gaurav Arya in the mechanical engineering department of Duke University. These models allows us to consider larger, more elaborate systems and before you know it, we can accurately describe assemblies of proteins with tunable catalytic sites, such as those currently being studied in the labs of professors Joshua Figueora and Akif Tezcan in the chemistry department, or viral capsid proteins currently being developed in the lab of Nicole Steinmetz in the nanoengineering department,” said Pascal.
Another unique capability of the Predictive Assembly research thrust of the new UC San Diego MRSEC is the tight connection to spectroscopy.
“Time-resolved scattering techniques, such as those being developed by Alex Frano in the physics department, is one of the cornerstones of our efforts, as it allows us to investigate the dynamics of complex assemblies in real time. What’s even more exciting is that we are now developing methods to directly simulate these spectra on the computer,” said Pascal.
Two world renowned experts in spectroscopy, Susan Habas at the National Renewable Energy Laboratory (NREL) and Tony van Burren at Lawrence Livermore National Lab (LLNL), will join the effort and expand the available spectroscopic tools that will be used to characterize these complex materials systems.
The predictive assembly team will work closely with the larger UC San Diego MRSEC team to create a MesoMaterials Design Facility.
“This is an effort to bring the two cultures of theory and synthesis together,” said Tao. “The MesoMaterials Design Facility will also serve as a resource to the larger materials science community across the country and across the world—providing a portal for others to design new properties into new materials.”
The predictive assembly team is focusing on the assembly of materials at the so-called mesoscale—sizes just larger than molecular and nanometer dimensions—where some of the most interesting properties of materials and their behavior are determined. More familiar properties such as strength, flexibility, and reactivity, and more esoteric behaviors such as quantum mechanical confinement and plasmonic coupling are mostly determined by the mesoscale structure of a material.
“Take metal nanoparticles as an example," said Tao. "Many of the catalysts used in the petrochemical industry, the sensor elements used in medical diagnostic kits, and the electrodes in rechargeable batteries rely on assemblies of metal nanoparticles to perform their functions. However, such metal nanoparticle ensembles have properties that are very different from an isolated metal nanoparticle. Very subtle changes in seemingly minor factors, like the orientation of the particles relative to each other, can exert profound changes in the properties of a material. The efficiency of a catalyst, the sensitivity of a medical diagnostic test, how many times a battery can be recharged, even the color of a material are all determined by these mesoscale interactions. So learning the rules for how matter can be put together on this scale will allow us to rationally design and predict new materials with revolutionary properties.”
Predictive assembly team
Co-Lead of predictive assembly team
nanoengineering professor, UC San Diego
Co-Lead of predictive assembly team
nanoengineering professor, UC San Diego
Founding professors on the predictive assembly team
Francesco Paesani, chemistry and biochemistry professor, UC San Diego
Joshua Figueroa, chemistry and biochemistry professor, UC San Diego
Akif Tezcan, chemistry and biochemistry professor, UC San Diego
Alex Frano, physics professor, UC San Diego
Guarav Arya, mechanical engineering and materials professor, Duke University
External National Lab Collaborators
Susan Habas, Nanoscience & Materials Chemistry, NREL
Tony van Buuren, Materials Science Division, LLNL
Jacobs School of Engineering