Research Area: Computational Materials Science
Noa Marom received a B.A. in Physics and a B.S. in Materials Engineering, both Cum Laude, from the Technion- Israel Institute of Technology in 2003. From 2002 to 2004 she worked as an Application Engineer in the Process Development and Control Division of Applied Materials. In 2010 she received a Ph.D. in Chemistry from the Weizmann Institute of Science. She was awarded the Shimon Reich Memorial Prize of Excellence for her thesis. She then pursued postdoctoral research at the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin. From 2013 to 2016 she was an Assistant Professor in the Physics and Engineering Physics (PEP) Department at Tulane University. In 2016 she joined the Materials Science and Engineering Department at Carnegie Mellon University as an Assistant Professor. She holds courtesy appointments in the Department of Chemistry and the Department of Physics. She is a member of the Pittsburgh Quantum Institute (PQI) and an affiliate of the Scott Institute for Energy Innovation. She has recently received the NSF CAREER, DOE INCITE (2017,2018), and Charles E. Kaufman Young Investigator awards. In 2018 she was awarded the IUPAP Young Scientist Prize in Computational Physics.
EducationEducation: Ph.D., Weizmann Institute of Science, 2010
The Marom group combines quantum mechanical simulations with machine learning and optimization algorithms to computationally design materials with desired properties for various applications.
Through the portal of computer simulations we gain access to the vast configuration space of materials structure and composition. We can explore the uncharted territories of materials that have not been synthesized yet and predict their properties from first principles, based solely on the knowledge of their elemental composition and the laws of quantum mechanics. Since the Schrödinger equation can be solved exactly only for very small systems (=the hydrogen atom), we employ approximate methods within the framwork of density functional theory (DFT) and many-body perturbation theory (MBPT) to apply quantum mechanics to systems, such as molecular crystals and interfaces, with up to several hundred atoms. The computational cost of quantum mechanical simulations increases rapidly with the accuracy of the method, the size of the system, and the number of trial structures sampled, therefore we run our calculations on some of the world's most powerful supercomputers.
To navigate the configuration space and identify the most promising candidates, we use optimization algorithms. For example, genetic algorithms are guided to the most promising regions by the evolutionary principle of survival of the fittest. Machine learning (ML) uses statistical models based on "training data" to make predictions for new data points. We employ ML to accelerate predictions for materials properties and unveil hidden correlations in data generated by our simulations. We apply several types of ML algorithms for different purposes, such as optimization, classification, clustering, feature selection, sampling, and finding structure-property correlations. ML algorithms are integrated with quantum mechanical simulations in fully automated complex workflows.
- X. Wang, R. Tom, X. Liu, D. Congreve, and N. Marom "An energetics perspective on why there are so few triplet-triplet annihilation emitters", J. Mater. Chem. C, https://doi.org/10.1039/D0TC00044B (2020).
- R. Tom, T. Rose, I. Bier, H. O'Brien, A. Vazquez-Mayagoitia, and N. Marom "Genarris 2.0: A Random Structure Generator for Molecular Crystals", Comput. Phys. Commun., 250, 107170 (2020).
- F. Curtis, X. Li, T. Rose, A. Vazquez-Mayagoitia, S. Bhattacharya, L. M. Ghiringhelli, and N. Marom "GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction", J. Chem. Theory Comput., 14, 2246 (2018).
- X. Wang, X. Liu, C. J. Cook, B. Schatschneider, and N. Marom "On the Possiblity of Singlet Fission in Crystalline Quaterrylene", J. Chem. Phys. 148 184101 (2018).
- J. Knight, X. Wang, L. Gallandi, O. Dolgounitcheva, X. Ren, J. V. Ortiz, P. Rinke, T. Körzdörfer, and N. Marom "Accurate Ionization Potentials and Electron Affinities of Acceptor Molecules III: A Benchmark of GW Method", J. Chem. Theory Comput. 12, 615 (2016).
- S. Bhattacharya, B. H. Sonin, C. J. Jumonville, L. M. Ghiringhelli, and N. Marom “Computational Design of Nanoclusters by Property-Based Genetic Algorithms: Tuning the Electronic Properties of (TiO2)n Clusters”, Phys. Rev. B 91, 241115(R) (2015).