More than 30 years ago, the phrase “green chemistry” emerged with the Pollution Prevention Act of 1990. At that time, the U.S. Environmental Protection Agency implemented programs focused on the treatment and disposal of hazardous materials.
Green chemistry continues to be a goal for scientists working to reduce or eliminate hazardous materials. One recent project led by the Heather Kulik Laboratory at the Massachusetts Institute of Technology (MIT) used the Expanse supercomputer at the San Diego Supercomputer Center (SDSC) at UC San Diego.
Kulik worked with MIT chemical engineering graduate student Gianmarco Terrones on simulations of high performance iridium phosphors, or luminescent substances. Kulik’s and Terrones’ findings were recently published in Chemical Science.
Their study, titled “Low-cost machine learning prediction of excited state properties of iridium-centered phosphors,” demonstrated the development of fast, accurate models that assess phosphor properties such as color and duration of light emission.
“Our research focuses on the use of data-driven computer models (i.e., machine learning), which have a speed advantage over the usual ab initio first principles computer modeling approach – the data-driven models can be trained directly on experimental data as well, and can thus bypass certain accuracy limitations of ab initio models. These data-driven models can be used to identify good phosphors and bad phosphors, and answer questions like, does this type of ligand make a phosphor brighter or dimmer (leading to design rules).”Gianmarco Terrones, MIT chemical engineering graduate student
The research represents one of the first applications of machine learning to the specific chemistry of iridium-centered complexes and revealed design rules for synthesizing iridium phosphors with desired properties, such as emission lifetime.
You can read more about this story here: Low-cost machine learning prediction of excited state properties of iridium-centered phosphors published in the journal, Chemical Science
Institution: SDSC (San Diego Supercomputer Center)
Funding Agency: Office of Naval Research, DARPA, NSF/XSEDE, Alfred P. Sloan Foundation, NSF Graduate Research Fellowship Program
Grant Numbers: The Office of Naval Research (grant nos. N00014-18-1-2434 and N00014-20-1-2150) provided primary support for this work. Support for machine learning feature development was provided by DARPA (grant no. D18AP00039). Computational work on SDSC resources was supported by National Science Foundation (NSF) Extreme Science and Engineering Discovery Environment (grant no. ACI-1548562). Additional support was received from the Alfred P. Sloan Foundation (grant no. G-2020-14067) and the NSF Graduate Research Fellowship Program (grant no. 1122374).
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.