When
Presenter:
Noa Marom
Professor
Carnegie Mellon University, Dept. Materials Science and Engineering
Abstract:
Department of Materials Science and Engineering, Carnegie Mellon University
Molecular crystals are bound by dispersion (van der Waals) interactions, whose weak nature gives rise to polymorphism, the ability of a compound to crystallize in different structures. Crystal structure profoundly influences the physical and chemical properties, and hence the functionality of molecular solids in applications including pharmaceuticals, electronic devices, and energetic materials. Therefore, the ability to predict the structure and properties of molecular crystals is of paramount importance. To this end, we combine first principles simulations with machine learning.
Molecular crystal structure prediction (CSP) is challenging because it requires searching a high-dimensional configuration space with high accuracy. CSP workflow have two main components, structure generation and stability ranking. For structure generation, we develop the Genarris code [1], which generates random structures in all compatible space groups with physical constraints on intermolecular distances. Machine learned interatomic potentials (MLIPs) are trained on large data sets of first principles simulations [2], typically density functional theory (DFT) to achieve DFT-level accuracy at the computational cost of classical force fields. We have interfaced Genarris with several types of MLIPs for geometry optimization and stability ranking [1,3,4]. We have shown that MLIPs can completely replace both early-stage screening with classical force fields and final ranking with DFT, paving the way to high-throughput CSP [4].
One of the optoelectronic applications of molecular crystals is singlet fission (SF), the conversion of one photogenerated singlet exciton into two triplet excitons. SF has the potential to increase the efficiency of solar cells by harvesting two charge carriers from one high-energy photon, whose excess energy would otherwise be lost to heat. The realization of SF-based solar cells is hindered by the dearth of suitable materials. The excited-state properties of molecular crystals can be calculated using many-body perturbation theory (MBPT) within in the GW approximation and the Bethe-Salpeter equation (BSE) [5]. The computational cost of GW+BSE is prohibitive for large-scale exploration of the chemical space, and also for generating large amounts of training data. This calls for ML approaches that work well with small data. We have generated the PAH101 dataset [6] of GW+BSE results for 101 polycyclic aromatic hydrocarbons (PAH) and used the sure-independence-screening-and-sparsifying-operator (SISSO) ML algorithm to train models to predict the excited-state properties of crystalline organic semiconductors [6,7,8]. These models can be used to explore the chemical space and discover new candidate materials for organic optoelectronic devices.
[1] Y. Yang et al. J. Chem. Theory Comput. 21, 11318 (2025)
[2] V. Gharakhanyan et al., Scintific Data, 13, 354 (2026)
[3] K. S. Nayal et al. Crystal Growth & Design 25, 9092 (2025)
[4] V. Gharakhanyan et al., arXiv 2508.02641 (2025)
[5] X. Wang et al. J. Phys. Chem. C 128, 7841 (2024)
[6] S. Gao et al. Scientific Data 12, 679 (2025)
[7] X. Liu et al., npj Comput. Mater., 8, 70 (2022)
[8] S. Gao et al. Digital Discovery 4, 1306 (2025)
Bio:
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, now the Oden Institute) 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 was promoted to Associated Professor in 2021 and to Professor in 2025. 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. Her achievements in research and in large-scale computing have been recognized by several awards, including the Sanibel Symposium Young Investigator Award (2016), NSF CAREER (2016), the DOE Innovative and Novel Computational Impact on Theory and Experiment (INCITE) Award (2017, 2018, 2019), the Charles E. Kaufman Young Investigator Award (2017), the IUPAP Young Scientist Prize in Computational Physics (2018), The George Tallman Ladd Award of the CMU College of Engineering (2020), the ACS COMP OpenEye Outstanding Junior Faculty Award (2021), and the CMU College of Engineering Dean’s Early Career Fellowship (2021). In 2025 she was selected as a Fellow of the American Physical Society (APS). She serves as an Associate Editor of npj Computational Materials.
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