Carnegie Mellon University

David Yaron

David Yaron

Professor, Chemistry


1990     Ph.D., Harvard University


Theory, computational, semi-empirical quantum chemistry, electronic structure theory, materials theory, photophysics, spectroscopy, machine learning


Computational Modeling of Organic Semiconductors

A central goal of our research is to develop a reliable, semi-empirical quantum chemistry approach to the excited electronic states of conjugated polymers, and to use this to predict structure-property relationships of relevance to device design. Our techniques have greatly expanded the questions that can be addressed with the INDO (Intermediate Neglect of Differential Overlap) method. These include a dielectric model that provides the first consistent theory of both the neutral and charged excited states, and computational optimizations that allow calculations on ensembles of disordered structures, such that we can model amorphous materials.

New Approaches to Semiempirical Electronic Structure Theory

We have also begun development of new approaches to semi-empirical quantum chemistry that use machine learning to take better advantage of molecular similarity. We first create a set of ab initio data containing detailed data on how a functional group behaves in a hundreds of different chemical environments. The challenge is extracting from this data, sufficient information to predict its behavior in a new environment.


Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning
Haichen Li, Christopher R. Collins, Thomas G. Ribelli, Krystof Matyjaszewski, Geoffrey J. Gordon, Tomasz Kowalewski, and David J. Yaron, Mol Syst Des Eng. advance article

Constant size descriptors for accurate machine learning models of molecular properties
Christopher R. Collins, Geoffrey J. Gordon, O. Anotole von Lilienfeld, and David J. Yaron, J. Chem. Phys. 148, 241718 (2018);

Tuning the Molecular Weight Distribution from Atom Transfer Radical Polymerization Using Deep Reinforcement Learning
Haichen Li, Christopher R. Collins, Thomas G. Ribelli, Krzysztof Matyjaszewski, Geoffrey J. Gordon, Tomasz Kowalewski, David J. Yaron , arXiv:1712.04516 [physics.chem-ph]

Constant Size Molecular Descriptors For Use With Machine Learning
Collins CR, Gordon GJ, von Lilienfeld OA, Yaron DJ. arXiv preprint arXiv:1701.06649. 2017, 1 (23)

In-Situ Platinum Deposition on Nitrogen-Doped Carbon Films as a Source of Catalytic Activity in a Hydrogen Evolution Reaction
Eric Gottlieb, Maciej Kopeć, Manali Banerjee, Jacob Mohin, David Yaron, Krzysztof Matyjaszewski, and Tomasz Kowalewski, ACS Appl. Mater. Interfaces, 8 (33), pp 21531–21538 (2016).

Conjugated polymers with repeated sequences of group 16 heterocycles synthesized through catalyst-transfer polycondensation
Chia-Hua Tsai, Andria Fortney, Yunyan Qiu, Roberto R. Gil, David Yaron, Tomasz Kowalewski, and Kevin J. T. Noonan,  J. Am. Chem. Soc., 2016, 138 (21), pp 6798– 6804 (2016).

A Least-Squares Commutator in the Iterative Subspace Method for Accelerating Self-Consistent Field Convergence
Haichen Li and David J. Yaron, J. Chem. Theory Comput., 12 (11), pp 5322–5332 (2016).

The Efficiency of Worked Examples Compared to Erroneous Examples, “Tutored Problem Solving, and Problem Solving in Computer-Based Learning Environments.”
McLaren, Bruce M., Tamara van Gog, Craig Ganoe, Michael Karabinos, and David Yaron. Computers in Human Behavior 55, Part A (February): 87–99. (2016).

Imitation Learning for Accelerating Iterative Computation of Fixed Points in Quantum Chemistry
Tanha, Matteus, Tse-Han Huang, Geoffrey J. Gordon, and David Yaron, Paper presented at the 12th European Workshop on Reinforcement Learning (EWRL 2015), Lille, France, July (2015).

Spectral Fine Tuning of Cyanine Dyes: Electron Donor-Acceptor Substituted Analogues of Thiazole Orange
Rastede, Elizabeth E., Matteus Tanha, David Yaron, Simon C. Watkins, Alan S. Waggoner, and Bruce A. Armitage, Photochemical & Photobiological Sciences: Official Journal of the European Photochemistry Association and the European Society for Photobiology 14 (9). The Royal Society of Chemistry: 1703–12 (2015).

Worked Examples are more Efficient for Learning than High-Assistance Instructional Software
McLaren, Bruce M., Tamara van Gog, Craig Ganoe, David Yaron, and Michael Karabinos, International Journal of Artificial Intelligence in Education. Vol. 9112 Springer: 710-713 (2015).

Modeling Field-Induced Quenching in Poly(p-Phenylene Vinylene) Polymers and Oligomers
Legaspi, Christian M., Linda A. Peteanu, and David J. Yaron, The Journal of Physical Chemistry. B 119 (24) 7625–34 (2015).

Making Sense of Students’ Actions in an Open-Ended Virtual Laboratory Environment
Gal, Ya’akov, Oriel Uzan, Robert Belford, Michael Karabinos, and David Yaron, Journal of Chemical Education 92 (4). American Chemical Society: 610–16 (2015).

Developing Coarse-Grained Site Models for Excited Electronic States of Conjugated Polymers
Collins, Christopher R., and David J. Yaron, In Proceedings of SPIE 9549, Physical Chemistry of Interfaces and Nanomaterials XIV, edited by Sophia C. Hayes and Eric R. Bittner. Vol. 9549 (2015),


2013–present Professor, Carnegie Mellon University
1998–2013 Associate Professor, Carnegie Mellon University
1992–1998 Assistant Professor, Carnegie Mellon University
1990–1992 Postdoctoral Fellow, MIT

Awards and Distinctions

2017 Teaching Innovation Award, Carnegie Mellon University
2004 Award for Excellence: Post-secondary Educator
Carnegie Science Center Pittsburgh
2003 Classics Award for Best Learning Object in Chemistry
Editor’s Choice for Exemplary Learning Object in any domain
Merlot Digital Library
2001 Julius Ashkin Teaching Award
Mellon College of Science, Carnegie Mellon
2000 Henry Dreyfus Teacher-Scholar Award
The Camile and Henry Dreyfus Foundation
1999 Visiting Professor
Ecole Normale Superieure de Cachan, France