Atomic models often look like the image to the left. The blue lines represent orbital paths of the electrons – subatomic, negatively-charged particles that whiz around the nucleus of the atom. Seeing electrons requires using high-resolution non-contact atomic force microscopy (HR-AFM) and spectroscopy that measures molecular-scale forces on the order of piconewtons (pN), one-trillionth of a Newton. But researchers want to do more than just see these electrons, they want to predict where they’ll be as well.

Study co-author James R. Chelikowsky explains why being able to predict where a particular electron is in an atom is important. “By directly observing the signatures of electron orbitals using techniques such as atomic force microscopy,” Chelikowsky said, “we can gain a better understanding of the behavior of individual atoms and molecules, and potentially even how to design and engineer new materials with specific properties. This is especially important in fields such as materials science, nanotechnology, and catalysis.”

By using ACCESS resource Stampede2, researchers were able to finish their project much faster. “The built-in software packages on TACC’s Stampede2 helped us to perform data analysis much more easily,” said Dingxin Fan, the study co-first author.

Supercomputers, in many ways, allow us to control how atoms interact without having to go into the lab. Such work can guide the discovery of new materials without a laborious ‘trial and error’ procedure.

James R. Chelikowsky, UT Austin

If you have a project that could benefit from access to supercomputing resources like the ones used in this story, you can visit the allocations page to get started with ACCESS. You can read more about this story here: Seeing Electron Orbital Signatures 

Project Details

Institution: TACC (Texas Advanced Computing Center)
University: UT Austin, Princeton University
Funding Agency:  Funding came from ExxonMobil through the Princeton E-ffiliates Partnership of the Andlinger Center for Energy and the Environment, the Welch Foundation (grant F-2094), and the National Science Foundation (grant No. DMR-2011750).
Grant Number: DMR-2011750

The science story featured here, allocated through August 31, 2022, was enabled through Extreme Science and Engineering Discovery Environment (XSEDE) and supported by National Science Foundation grant number #1548562. Projects allocated September 1, 2022 and beyond are enabled by the ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.