Two assistant professors in Chemistry, Dominika Zgid and Paul Zimmerman, have been awarded CAREER grants. These major 5-year grants are the National Science Foundation's most prestigious awards in support of junior faculty. They are awarded to those "who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research."
Dominika Zgid's project is: "Novel Green's function methods for predicting experimentally relevant quantities for solids and molecules." Her aim is to develop new computational tools to study large molecules and solids in which the correlated motion of electrons is very important. In modern science and technology, materials chemistry plays a big role in the production of advanced optoelectronic materials, semiconductors and superconductors, solar cell and battery materials. Dr. Zgid is also actively engaged in public outreach for minorities in Science, Technology, Education and Mathematics (STEM) by organizing workshops for middle school girls.
Paul Zimmerman's project is: "Predictive Discovery of Complex Reaction Mechanisms." His aim is to develop fully computational, low-cost and highly accurate methods that are able to predict the outcome of chemical reactions starting only from the feedstock molecules.
For more details, see the abstracts below from the National Science Foundation.
Zgid abstract from the NSF.gov:
Dominika Zgid, of the University of Michigan, is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop new computational tools to study large molecules and solids in which the correlated motion of electrons is very important. In modern science and technology, materials chemistry plays a big role in the production of advanced optoelectronic materials, semiconductors and superconductors, solar cell and battery materials.
To enable the discovery of new materials and to answer experimental questions, theory has to predict experimentally relevant, measurable quantities. In the last fifty years, the majority of quantum chemistry research was focused on the development of methodological advances for molecular systems. Currently molecular problems can be described very accurately. However, for large strongly correlated molecules and solids, quantum chemistry still lacks computational tools that describe electronic correlation accurately in a systematically improvable manner and deliver experimentally useful predictions. The development of novel ab-initio theoretical methods that are at the interface of quantum chemistry and condensed matter physics and are capable of delivering useful experimental predictions for solids is the major aim of this research. This interdisciplinary project involves training and mentoring of graduate students and postdocs by allowing them to understand their research in the broadest possible sense. The research prepares them for a wide range of careers.
Dr. Zgid is also actively engaged in public outreach for minorities in Science, Technology, Education and Mathematics (STEM) by organizing workshops for middle school girls.
The Green's function language provides a natural link to experiment, since spectra can be readily calculated without the cumbersome excited state formalism present in wave function or density theories. Green's function methods are controlled, reliable, and systematically improvable and may easily be generalized by employing embedding methods to work for solids or large molecules. In order to calculate excitation spectra, this project implements the Bethe-Salpeter equation with a second order Green's function method and self-energy embedding approaches. The formalism is calibrated on small molecules and subsequently extended to solids by using embedding methods. Since the realism and predictive power of quantum mechanical simulations depend on the accuracy of modelling all electrons, significant attention is given to the investigation of effective Hamiltonian approaches that aim to make Green's function embedding methods quantitative for realistic molecular and crystalline systems. Finally, since the Green's function is a large object that can be calculated in parallel, the investigation focuses on efficient ways of expressing Green's functions in computer implementations. A major outcome of the project is software containing efficient, reliable and systematically improvable Green's function embedding methods for solids that is released to the public. Additionally, Dr. Zgid's research group is preparing a series of lecture notes for graduate students explaining Green's functions in order to reduce the language barrier frequently experienced by quantum chemists when working with the Green's function formalism. The proposed interdisciplinary research involves training and mentoring of graduate students and postdocs allowing them to understand their research in the broadest possible sense and prepares them for wide range of careers. Additionally, Dr. Zgid also takes part in the "Science for tomorrow" program for middle school students from underserved communities in Michigan.
Zimmerman abstract from NSF.gov:
Paul Zimmerman at the University of Michigan is supported by the Chemical Theory, Models and Computational Methods Program and the Computational and Data-Enabled Science and Engineering (CDS&E) Program to develop new tools to predict the outcome of chemical reactions. Computational chemistry has a long history of using the principles of quantum mechanics to create tools which provide detailed, accurate explanations for a wide variety of chemical processes. Many of these tools have reached sufficient accuracy that they can be applied to discover chemical reactions without requiring prior insight from experiment. Unfortunately, the high computational cost of these methods has prevented their broad use in predicting chemical reactivity, especially in cases where the chemistry is highly complex or poorly understood.
This grant supports the development of fully computational, low-cost and highly accurate methods that are able to predict the outcome of chemical reactions starting only from the feedstock molecules. The application focus of these methods is catalytic reactions, providing a highly useful tool for research into chemistries that can be applied at an industrial level to create high-value chemical products. These tools are available to the wider computational chemistry community, enabling maximized impact of the developments to a great number of problems in chemical reactivity. Due to the diversity of potential applications for this research, the students involved in this project gain not only algorithm development abilities, but also fundamental insights into processes governing molecular behavior, cutting-edge computational research experience, and problem-solving abilities that are needed to address the challenges of the 21st century. The proposed methods are transformed into educational strategies that merge introductory laboratory exercises with real-world research, starting with a pilot study in honors organic chemistry.
The research program builds upon work by the Zimmerman group that shows chemical reactions can be described in terms of a small number of localized, anharmonic reaction coordinates. These reaction coordinates consist of interatomic distances, angles, and torsions, and are a reliable, transferable basis for describing atomic motion. By employing advanced single-ended chain-of-states optimization algorithms which search along these coordinates for plausible chemical reactions, this research methodology can efficiently and reliably predict reactive events without guidance from chemical intuition. The main objective of ongoing work is to expand this method to cover transition metal elements, enable efficient conformation searches in systems with floppy degrees of freedom, and develop machine learning algorithms to automatically process chemical data and provide great enhancements in computational efficiency. In sum, these new reaction search techniques enable predictive reaction discovery in a wide variety of large, highly complicated chemical systems where chemical knowledge alone is not yet sufficient to make accurate predictions. These methods are being applied to challenging cases in catalysis where uncharacterized side reactions are severe impediments to efficient product formation. Ongoing discovery of these undesired reaction pathways lead to the chemical insight required to improve reaction selectivity and catalyst stability. These tools allow beginning chemistry students to hypothesize and evaluate reactions in silico, resulting in a means for students to perform research at an early stage of their studies. Such pedagogical tools are distributed to educators outside of the University of Michigan to maximize their impact.