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Joshua Schrier Is Newest Alvarez Fellow in Computing Sciences

October 5, 2005


New Alvarez Fellow Joshua Schrier (center) is congratulated by CRD Director Horst Simon (left) and High Performance Computing Research Department Head Juan Meza. (Photo: Roy Kaltschmidt, LBNL)

When Joshua Schrier thinks about how to explain his field of research to his parents, he knows that he won’t get far talking about selfconsistent fields, wave functions and how the interaction of electrons at the organic-inorganic interface affects the properties in nanocrystals.

So, he tells them he’s working on research that could lead to more efficient and less expensive solar cells. As a post-doc in the Computational Research Division’s Scientific Computing Group, Schrier is working with Lin-Wang Wang to develop new computational models to study the properties of nanoscale materials.

Schrier, who joined the Lab in May after earning his Ph.D. in chemistry at UC Berkeley, has just been named as the fourth recipient of the Luis Alvarez Computational Science Fellowship. The award honors Nobel Laureate Alvarez, an LBNL physicist who was an early advocate of using computers to gather and analyze experimental data.

“It’s a great honor,” said Schrier. Not long after applying for the fellowship, he was browsing at Moe’s Books on Berkeley’s Telegraph Avenue when he came across a volume of papers by Alvarez, compiled by his fellow researchers. “What was amazing to me was the scope of things he was interested in during his life — from the extinction of dinosaurs to secret chambers in the pyramids to beta decay to the bubble chamber — he did well in many fields, making solid contributions.”

Schrier was also impressed by Alvarez’ flair for invention. After going on a photo safari in Africa and being bothered by a jittery camera, Alvarez invented a self-stabilizing camera to get clearer shots. “He was always inventing new ways to study problems,” Schrier said.

And in the emerging field of nanotechnology, Wang and Schrier are also coming up with new methods for studying problems involving the behavior of electrons, which determine a material’s properties. These properties can be used for different purposes, such as processing information. One way of tapping into these properties has been to move the electrons, as is done in transistors. But now scientists are looking into a new idea of controlling the spin of electrons, rather than moving them, to process and store information. If successful, such systems would require less power and would be more robust.

At UC Berkeley, Schrier was a member of a group studying electron spin in semiconductors, as well as working on semiconductor nanocrystals. At the Lab, he is still working on semiconductor nanocrystals, but for different properties, and with different types of quantum mechanical methods. The experimental work is led by LBNL’s Paul Alivisatos. His group is growing crystals just a few nanometers in diameter.

“That’s the size where an electron really feels trapped, really feels the edges of the structure,” Schrier said. Because the electrons behave differently near the edges of such crystals, changing the size of the crystal can change the properties of the material.

In the simplest model of nanocrystals, the electrons live in a “box” with infinitely high walls. It is the confinement produced by these walls that changes the energy levels of the confined electrons. As an example, Schrier uses a violin or guitar string, which makes a sound when a wave vibrates the string. Confining this area of vibration, such as by placing a finger on a fret, changes the frequency of the vibration and the resulting sound. Similarly, by changing the size of an area where electrons live, the energy levels — and properties — can be “tuned.”

“There are very intensive experimental projects studying these problems and looking at a wide range of properties and how they scale with size and shape — or how the properties can be tuned,” he said. “As we move from the bulk scale down to the atomic level, we find that many assumptions no longer apply. So, we need to start our quantum mechanical description at the atomic level and build the system up from there. This lets us treat problems such as defects, disorder and surface reconstruction in a systematic way.”

The standard computational method has been to run long series of calculations of the energy potential of a system, at each step recalculating how the electrons respond to the revised potentials. Finally, at some point, the models results in electrons which are self-consistent, the potential stops changing, and the model for that system is complete.

But Wang has come up with a different, more efficient method of modeling nanosystems. Called “charge patching,” the method assumes that the characteristics of a nanosystem are about the same as a bulk system. The method then extracts “motifs,” or descriptions of the electron density of the atoms based on their chemical “neighborhood,” and moves them around, patching them together to form a good approximation of the actual density.

This approach sidesteps the computationally intensive self-consistency method and, running on a supercomputer at NERSC, produces results in just a few hours. The other method could take weeks to run, Schrier said.

The behavior of electrons at the interface of two materials is of particular interest to members of Alivisatos’ group who are looking at developing solar cells by combining nanocrystals with organic polymers. These types of devices would be cheaper than the typical silicon-based solar cells in use today. The idea is that sunlight would excite the polymer, and the excitation would transfer into the nanocrystal and move toward electrodes, generating electricity in the process. “But we don’t have good theoretical models for treating such a system,” Schrier said. “Our goal is to come up with better methods based on charge patching so we can improve our understanding of the inorganicorganic interfaces at the nanoscale.”

About Computing Sciences at Berkeley Lab

High performance computing plays a critical role in scientific discovery. Researchers increasingly rely on advances in computer science, mathematics, computational science, data science, and large-scale computing and networking to increase our understanding of ourselves, our planet, and our universe. Berkeley Lab’s Computing Sciences Area researches, develops, and deploys new foundations, tools, and technologies to meet these needs and to advance research across a broad range of scientific disciplines.