The Poranne Group is a research group working in the field of computational physical organic chemistry. Our work focuses on polycyclic aromatic system, ranging from fundamental investigation into molecular properties and structure-property relationships to use of machine-learning and deep-learning models for data-driven molecular design and discovery. We uncover useful and intuitive connections between structural features and molecular properties, and develop user-friendly pipelines and methods that help connect these abstract properties to real-world synthetic strategies.
In addition, we work closely with collaborators around the world to better understand the reactivity and behavior of polycyclic aromatic systems, and to leverage their unique properties for various applications. Though our current focus in on polycyclic aromatic systems, we are happy to explore other research directions.
The group believes in an inclusive and collaborative culture, where team-work and mutual respect are top priorities. We are always open to receiving new members who are excited about learning and who are motivated to work towards advancing our understanding of chemistry and molecular design.
Renana Gershoni Poranne is an Assistant Professor of Computational Chemistry at the Schulich Faculty of Chemistry at the Technion-Israel Institute of Technology, where she is a Branco Weiss Fellow and Career Advancement Fellow.
Before joining the faculty at the Technion, Renana was a Senior Scientist (Group Leader) in the group of Prof. Dr. Peter Chen at the Laboratorium für Organische Chemie at the ETH Zürich. Her promotion to Senior Scientist and Lecturer in July 2017 followed a two-year post-doctoral period (as a VATAT postdoctoral fellow) in the same group. She completed her PhD studies under the supervision of Prof. Amnon Stanger in the Schulich Faculty of Chemistry at the Technion, working on elucidation of the properties of aromatic compounds and developing methodologies for the identification and quantification of aromaticity in polycyclic aromatic hydrocarbons. Prior to that, she received her MSc Summa cum Laude for her work on functionalization of corannulene in the group of Prof. Ehud Keinan, and her BSc Summa cum Laude in Molecular Biochemistry.
Renana’s research interests lie in the field of computational physical organic chemistry, with particular emphasis on development of methods and tools for better understanding of the physical properties and reactivity of organic and organo-metallic compounds. The work in her group ranges from investigation of fundamental molecular properties and concepts—such as aromaticity, dispersion, metallophilic interactions, catalysis, and mechanism elucidation—to application of machine-learning and deep-learning models for molecular design of novel polycyclic aromatic systems and discovery of structure-property relationships.
Gershoni-Poranne* and A. Stanger*
Chapter 4: NICS – Nucleus Independent Chemical Shifts in Aromaticity: Modern Computational Methods and Applications, 2021 Edited by I. Fernandez.
Markert, E. Paenurk, and R. Gershoni-Poranne*
Prediction of Spin Density, Baird-Antiaromaticity, and Singlet-Triplet Energy Gap in Triplet-State Polybenzenoid Systems from Simple Structural Motifs
Chemistry – A European Journal, 2021, 27, 6923.
Selected for a Cover Feature
Denoted as a Hot Paper
Paenurk, S. Feusi, and R. Gershoni-Poranne
* Predicting Bond-currents in Polybenzenoid Hydrocarbons with an Additivity Scheme
Journal of Chemical Physics, 2021, 154, 024110.
Invited contribution for the Issue Honoring Women in Chemical Physics and Physical Chemistry
Wahab, F. Fleckenstein, S. Feusi, and R. Gershoni-Poranne*
Predi-XY: A Python program for automated generation of NICS-XY-Scans based on an Additivity Scheme Electronic Structure, 2020, 2, 047002
Invited contribution for the Emerging Leaders issue Selected as Editor’s Choice paper
Finkelstein and R. Gershoni-Poranne*
An Additivity Scheme for Aromaticity: The Heteroatom Case
ChemPhysChem 2019, 20, 1508-1520.
Piecing it Together: An Additivity Scheme for Aromaticity using NICS-XY-Scans
Chemistry – A European Journal 2018, 24, 4165-4172.
Gershoni-Poranne,* A. P. Rahalkar, and A. Stanger*
The Predictive Power of Aromaticity: Quantitative Correlation between Aromaticity and Ionization Potentials and HOMO-LUMO Gaps in Oligomers of Benzene, Pyrrole, Furan, and Thiophene
Physical Chemistry Chemical Physics 2018, 20, 14808-14817