Gershoni Poranne Renana 2880x880

Renana Gershoni-Poranne

As a Branco Weiss Fellow, Dr. Renana Gershoni-Poranne will work on inverse design of new polycyclic aromatic frameworks for use within organic electronics by applying deep generative models. This will be key to designing new molecular structures that can provide optimized functionalities for these devices, and the methodologies developed can assist molecular discovery for other desired functions, such as fluorescence, anti-cancer activity, adhesion, conductivity, and more.


Israel (Israeli and US Citizenship)

Academic Career

  • Assistant Professor of Computational Chemistry, Schulich Faculty of Chemistry, Technion, Israel, 2021-present
  • Habilitation in Computational Physical Organic Chemistry, ETH Zurich, Switzerland, 2017-2021
  • Postdoctoral Researcher in Computational Physical Organic Chemistry, ETH Zurich, Switzerland, 2015-2017
  • PhD in Physical Organic Chemistry, Technion, Israel, 2010-2015
  • MSc (summa cum laude) in Organic Chemistry, Technion, Israel, 2007-2010
  • BSc (summa cum laude) – Molecular Biochemistry, Technion, Israel, 2004-2007

Major Awards

  • The VATAT Fellowship for Excellent Female Post-Doctoral Scholars 2016-2018
  • The Weissman and Jacknow Prize for Sustained Excellence in Teaching 2015
  • The Schulich Prize for Excellence in Teaching 2009 & 2014
  • The Szego Prize for Excellence in Teaching 2009, 2010, 2013, & 2014
  • Participant of the Lindau Nobel Laureate Meeting 2013
  • The Schulich Prize for Excellence for PhD Students 2012-2014
  • Schulich Prize for Excellence for MSc Students 2007-2009
  • Schulich Prize for Excellence in Undergraduate Studies 2007
  • Knesset Award for Excellent Undergraduate Students 2007
  • Sharett Foundation Scholarship for Excellence in Music – Classical Singing 2007 & 2008


Branco Weiss Fellow Since

Research Category
Computational Physical Organic Chemistry, Computer Science

Research Location
Schulich Faculty of Chemistry, Technion, Haifa, Israel

Human quality of life is defined, to a large extent, by the type of compounds and materials that can be made to fulfill various needs: drugs, composites, fuels, adhesives, packing and insulation materials, energy harvesting compounds, etc. To optimize these functions and make them more suitable for sustainability, better compounds are required. But owing to the limited effectiveness of the strategies currently employed in molecular discovery, the development of new compounds is an arduous and expensive task. Inverse design — the process of constructing a target molecule to meet the desired property or function — has the potential to radically change the way new molecules are discovered. To locate a molecule with specified properties, it’s necessary to be able to uniquely map structural features to chemical behaviors. The main challenge therefore lies in the fact that our understanding of the very complex relationship between structure and function is limited.
Details of Research
To address these challenges, Dr. Gershoni-Poranne will combine chemistry and computer science techniques to investigate the relationship between the structure of polycyclic aromatic hydrocarbons and their electronic behavior. Polycyclic aromatic hydrocarbons are pervasive in chemistry and materials science, and are especially important in the field of organic electronics. Dr. Gershoni-Poranne will use high-throughput quantum chemical calculations to construct a database which will supply the data needed for the application of machine learning algorithms and the training of deep generative models. The overarching goal is to design optimal candidates for various organic electronic-based uses — including photovoltaics, field-effect transistors, and light-emitting diodes — as a demonstration of the utility of deep learning within the realm of chemistry, and for inverse design in particular. This will enable resource-efficient molecular discovery, paving the way to more effective and environmentally responsible molecules and materials in the future.