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Omri Abarbanel

Computational Chemistry PhD Candidate @ Hutchison lab, University of Pittsburgh.
Download full CV HERE.

EXPERIENCE

  • 2022 – Current

    Consultant, Fourth River Solutions

    Providing consulting services to startups, performing market analysis, customer discovery, and more.

  • 2018 – Current

    PhD Candidate, Hutchison Group, Pittsburgh

    Using quantum calculations, machine learning and genetic algorithms to search for novel materials with optimized properties.

  • 2016 – 2018

    Research Intern, Ulijn Group, New York

    Research project - using Second Harmonics Generation imaging to characterize peptide self-assembly.

  • 2016 – 2018

    Teaching Assistant, CUNY Hunter College, New York

    Teaching general and organic chemistry undergraduate level labs.

  • 2011 – 2016

    IT, Social and Digital Media Director, Israel Tourism Office, New York

    Managing the computer systems of the 5 Israeli Ministry of Tourism offices around the US. Managing the social media pages (Facebook, Twitter, YouTube) of the Israeli Ministry of Tourism in the US, as well as the US-targeted tourism website.

  • 2010 – 2011

    SaaS Tier 1, RSA, Israel

    Supporting and monitoring systems used for cyber security for US and European banks.

  • 2007 – 2010

    Network Administrator, Israel Defense Force

    Managing the computer networks used by over 300 end-users, as well as writing SQL-like reports.

RESEARCH INTERESTS

  • Material Discovery
  • Machine Learning
  • Computational Chemistry
  • Data Science

SKILLS

  • Python

    90%

  • Bash

    75%

  • Data Visualization (Matplotlib, Seaborn, Plotly, etc.)

    80%

  • Data Manipulation (Pandas, numpy, etc.)

    75%

  • Machine Learning (Scikit-Learn, Pytorch, Tensorflow, Keras)

    70%

  • SQL

    50%

PROJECTS

2022

xTB Parsing Support for cclib
Adding xTB parsing support for the cclib python package.

2023

Pi-System Features for Machine Learning
Creating a descriptors for conjugated molecules for machine learning.

2021

Machine Learning to Accelerate Screening for Marcus Reorganization Energies
Combining machine learning and quantum mechanical features to find conjugated polymers with low reorganization energy

2022

Finding High-Spin Polymers Using Genetic Algorithms
Accelerating the search for novel materials using genetic algorithms.

RECENT PUBLICATIONS

[All Publications]
  1. QupKake: Integrating Machine Learning and Quantum Chemistry for micro-pKa Predictions
    Abarbanel, Omri, and Hutchison, Geoffrey
    ChemRxiv, 2023
  2. Strategies for Computer-Aided Discovery of Novel Open-Shell Polymers
    Abarbanel, Omri D., Rozon, Julisa, and Hutchison, Geoffrey R.
    The Journal of Physical Chemistry Letters, vol. 13, pp. 2158-2164, 2022
  3. Using Genetic Algorithms to Discover Novel Ground- State Triplet Conjugated Polymers
    Abarbanel, Omri D, Hutchison, , and R, Geoffrey
    Jul, 2022