About

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. She worked as an Application Engineer in the Process Development and Control Division of Applied Materials from 2002 to 2004. In 2010, she earned a Ph.D. in Chemistry from the Weizmann Institute of Science, where she was awarded the Shimon Reich Memorial Prize of Excellence for her thesis. Following her doctorate, she conducted 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, Noa Marom was an Assistant Professor in the Physics and Engineering Physics Department at Tulane University. In 2016, she joined the Materials Science and Engineering Department at Carnegie Mellon University as an Assistant Professor, was promoted to Associate 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 and an affiliate of the Scott Institute for Energy Innovation. Her research achievements and contributions to large-scale computing have been recognized by numerous awards, including the Sanibel Symposium Young Investigator Award, NSF CAREER, the DOE INCITE Award, the Charles E. Kaufman Young Investigator Award, the IUPAP Young Scientist Prize in Computational Physics, The George Tallman Ladd Award of the CMU College of Engineering, the ACS COMP OpenEye Outstanding Junior Faculty Award, and the CMU College of Engineering Dean's Early Career Fellowship. In 2025, she was selected as a Fellow of the American Physical Society. She also serves as an Associate Editor of npj Computational Materials.

Research topics

• Materials science
• Physics
• Condensed matter physics
• Artificial Intelligence
• Computer Science
• Quantum mechanics
• Chemistry
• Photochemistry
• Atomic physics
• Algorithm
• Mathematics
• Electrical engineering
• Thermodynamics
• Engineering physics
• Nuclear physics
• Composite material
• Optoelectronics
• Statistical physics

Selected publications

• Structure Prediction of Organic/Inorganic Interfaces with Genarris

  Journal of Chemical Theory and Computation · 2026-04-23

  articleOpen accessSenior authorCorresponding

  Organic/inorganic interfaces perform critical functions in organic electronic devices, and their structure can affect device performance. In addition, interactions with the substrate can promote the growth of otherwise metastable thin-film structures by epitaxial templating. Predicting the structure of organic/inorganic interfaces by computer simulations can aid in the design of interfaces with desirable properties. We present Genarris Interfaces, a structure generator for organic/inorganic interfaces. Genarris Interfaces uses epitaxy matrices to impose commensurism with the substrate. Film structures are generated in all layer groups that are compatible with the substrate symmetry and the requested number of molecules per unit cell. Clustering and down-selection are performed to reduce the number of structures while maintaining the diversity of packing motifs. Finally, relaxation and stability ranking are performed using dispersion-inclusive density functional theory (DFT). We demonstrate the application of Genarris for the interfaces of PTCDA on Ag(111), TCNE on Au(111), and naphthalene on Cu(111). In all cases, we find generated structures that resemble experimental scanning tunneling microscopy (STM) images. The electronic structure agrees well with spectroscopy experiments, where available. We envision Genarris Interfaces being used to predict the structure of organic/inorganic interfaces, to generate initial populations for other structure prediction algorithms, and to generate data sets for training machine learning models.

  PublisherDOI

• Visualization of Tunable Electronic Structure of Monolayer TaIrTe$_4$

  ArXiv.org · 2026-01-16

  articleOpen access

  Monolayer TaIrTe$_4$ has emerged as an attractive material platform to study intriguing phenomena related to topology and strong electron correlations. Recently, strong interactions have been demonstrated to induce strain and dielectric screening tunable topological phases such as quantum spin Hall insulator (QSHI), trivial insulator, higher-order topological insulator, and metallic phase, in the ground state of monolayer TaIrTe$_4$. Moreover, charge dosing has been demonstrated to convert the QSHI into a dual QSHI state. Although the band structure of monolayer TaIrTe$_4$ is central to interpreting its topological phases in transport experiments, direct experimental access to its intrinsic electronic structure has so far remained elusive. Here we report direct measurements of the monolayer TaIrTe$_4$ band structure using spatially resolved micro-angle-resolved photoemission spectroscopy (microARPES) with micrometre-scale resolution. The observed dispersions show quantitative agreement with density functional theory calculations using the Heyd-Scuseria-Ernzerhof hybrid functional, establishing the insulating ground state and revealing no evidence for strong electronic correlations. We further uncover a pronounced electron-hole asymmetry in the doping response. Whereas hole doping is readily induced by electrostatic gating, attempts to introduce electrons via gating or alkali metal deposition do not yield a rigid upward shift of the Fermi level. Fractional charge calculations demonstrate that added electrons instead drive band renormalization and shrink the band gap. Taken together, our experimental and theoretical results identify the microscopic mechanism by which induced charges reshape the band topology of monolayer TaIrTe$_4$, showing that doping can fundamentally alter the electronic structure beyond the rigid band behaviour that is typically assumed.

  PublisherOA PDF

• Visualization of Tunable Electronic Structure of Monolayer TaIrTe$_4$

  arXiv (Cornell University) · 2026-01-16

  preprintOpen access

  Monolayer TaIrTe$_4$ has emerged as an attractive material platform to study intriguing phenomena related to topology and strong electron correlations. Recently, strong interactions have been demonstrated to induce strain and dielectric screening tunable topological phases such as quantum spin Hall insulator (QSHI), trivial insulator, higher-order topological insulator, and metallic phase, in the ground state of monolayer TaIrTe$_4$. Moreover, charge dosing has been demonstrated to convert the QSHI into a dual QSHI state. Although the band structure of monolayer TaIrTe$_4$ is central to interpreting its topological phases in transport experiments, direct experimental access to its intrinsic electronic structure has so far remained elusive. Here we report direct measurements of the monolayer TaIrTe$_4$ band structure using spatially resolved micro-angle-resolved photoemission spectroscopy (microARPES) with micrometre-scale resolution. The observed dispersions show quantitative agreement with density functional theory calculations using the Heyd-Scuseria-Ernzerhof hybrid functional, establishing the insulating ground state and revealing no evidence for strong electronic correlations. We further uncover a pronounced electron-hole asymmetry in the doping response. Whereas hole doping is readily induced by electrostatic gating, attempts to introduce electrons via gating or alkali metal deposition do not yield a rigid upward shift of the Fermi level. Fractional charge calculations demonstrate that added electrons instead drive band renormalization and shrink the band gap. Taken together, our experimental and theoretical results identify the microscopic mechanism by which induced charges reshape the band topology of monolayer TaIrTe$_4$, showing that doping can fundamentally alter the electronic structure beyond the rigid band behaviour that is typically assumed.

  PublisherDOI

• Computational Input and Output Files for: "Combining Quasiparticle Self-Consistent GW and Machine-Learned DFT+U in Search of Half-Metallic Heuslers"

  Open MIND · 2026-02-19

  datasetSenior author

  This dataset contains the complete set of computational input and output files associated with the manuscript: “Combining Quasiparticle Self-Consistent GW and Machine-Learned DFT+U in Search of Half-Metallic Heuslers”. The archive includes all files required to reproduce the electronic structure calculations reported in the study, including: Input files for PBE, PBE+U, HSE, and QPGW calculations Machine-learned Hubbard U parameter workflows and related data Structural files for all investigated Heusler compounds Main output files from production calculations All computational parameters and methodological details are described in the associated publication.

  DOI

• Structure Prediction of Organic/Inorganic Interfaces with Genarris

  Open MIND · 2026-02-03

  datasetSenior author

  This repository contains all the structure files generated by Genarris Interfaces. Structure pools of intermediate steps can be found under the directory of each interface system, and all DFT relaxed structures are stored under the `relaxed_interfaces` directory.

  DOI

• Computational Input and Output Files for: "Combining Quasiparticle Self-Consistent GW and Machine-Learned DFT+U in Search of Half-Metallic Heuslers"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-19

  datasetOpen accessSenior author

  This dataset contains the complete set of computational input and output files associated with the manuscript: “Combining Quasiparticle Self-Consistent GW and Machine-Learned DFT+U in Search of Half-Metallic Heuslers”. The archive includes all files required to reproduce the electronic structure calculations reported in the study, including: Input files for PBE, PBE+U, HSE, and QPGW calculations Machine-learned Hubbard U parameter workflows and related data Structural files for all investigated Heusler compounds Main output files from production calculations All computational parameters and methodological details are described in the associated publication.

  PublisherDOI

• Open Molecular Crystals 2025 (OMC25) dataset and models

  Scientific Data · 2026-02-04 · 6 citations

  articleOpen access

  The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly available datasets with property labels. To address this challenge, we introduce the Open Molecular Crystals 2025 (OMC25) dataset, a collection of over 27 million molecular crystal structures containing 12 elements and up to 300 atoms in the unit cell. The dataset was created by relaxing over 230,000 randomly constructed molecular crystal structures-representing approximately 50,000 organic molecules-using dispersion-inclusive density functional theory (DFT) with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional combined with Grimme's D3 dispersion correction (PBE+D3). OMC25 comprises diverse chemical compounds capable of forming different intermolecular interactions and a wide range of crystal packing motifs. We provide information on the dataset's construction, composition, and properties. To demonstrate the quality and use cases of OMC25, we trained and evaluated state-of-the-art open-source machine learning interatomic potentials. By making this dataset publicly available, we aim to accelerate the development of accurate and efficient machine learning models for molecular crystals.

  PublisherOA PDFDOI

• Combining Quasiparticle Self-Consistent $GW$ and Machine-Learned DFT+$U$ in Search of Half-Metallic Heuslers

  Open MIND · 2026-02-24

  preprintSenior author

  Half-metallic Heusler compounds are of significant interest for spintronics. For device fabrication, compounds that can be epitaxially grown on III-V semiconductors are particularly attractive. We present a first-principles investigation of four Co-based and two Ni-based Heusler compounds that are lattice-matched to InAs. The results of density functional theory (DFT) using semi-local and hybrid functionals are compared to quasiparticle self-consistent $GW$ (QPGW). We also consider DFT with machine-learned Hubbard $U$ corrections [npj Computational Materials 6, 180 (2020)] with a new Bayesian optimization (BO) objective function to determine the $U$ values that yield the closest agreement with the QPGW band structure and magnetic moments. We find that DFT+U(BO) can adequately reproduce the key QPGW features in most cases. Our results reveal a strong method dependence of the degree of spin polarization at the Fermi level and, in some cases, even the dominant spin channel (majority or minority). Of the materials studied here, Co$_2$TiSn and Co$_2$ZrAl are the most likely to be half-metals, and Co$_2$MnIn is likely to be a near-half-metal.

  DOI

• Combining Quasiparticle Self-Consistent $GW$ and Machine-Learned DFT+$U$ in Search of Half-Metallic Heuslers

  ArXiv.org · 2026-02-24

  articleOpen accessSenior author

  Half-metallic Heusler compounds are of significant interest for spintronics. For device fabrication, compounds that can be epitaxially grown on III-V semiconductors are particularly attractive. We present a first-principles investigation of four Co-based and two Ni-based Heusler compounds that are lattice-matched to InAs. The results of density functional theory (DFT) using semi-local and hybrid functionals are compared to quasiparticle self-consistent $GW$ (QPGW). We also consider DFT with machine-learned Hubbard $U$ corrections [npj Computational Materials 6, 180 (2020)] with a new Bayesian optimization (BO) objective function to determine the $U$ values that yield the closest agreement with the QPGW band structure and magnetic moments. We find that DFT+U(BO) can adequately reproduce the key QPGW features in most cases. Our results reveal a strong method dependence of the degree of spin polarization at the Fermi level and, in some cases, even the dominant spin channel (majority or minority). Of the materials studied here, Co$_2$TiSn and Co$_2$ZrAl are the most likely to be half-metals, and Co$_2$MnIn is likely to be a near-half-metal.

  PublisherOA PDF

• Structure Prediction of Organic/Inorganic Interfaces with Genarris

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-03

  datasetOpen accessSenior author

  This repository contains all the structure files generated by Genarris Interfaces. Structure pools of intermediate steps can be found under the directory of each interface system, and all DFT relaxed structures are stored under the `relaxed_interfaces` directory.

  PublisherDOI

Recent grants

• Collaborative Research: Data Driven Discovery of Singlet Fission Materials

  NSF · $400k · 2021–2024

• EAGER: MATDAT18 Type-1: Collaborative Research: Data Driven Discovery of Singlet Fission Materials

  NSF · $238k · 2018–2020

• CAREER: Structure Prediction and Design of Molecular Crystals with the GAtor Genetic Algorithm Package

  NSF · $658k · 2016–2021

Frequent coauthors

• Alexandre Tkatchenko

  University of Luxembourg

  33 shared

• Leeor Kronik

  Weizmann Institute of Science

  27 shared

• Bohdan Schatschneider

  California State Polytechnic University

  25 shared

• Xingyu Liu

  Ningbo University of Technology

  21 shared

• James R. Chelikowsky

  The University of Texas at Austin

  21 shared

• Derek Dardzinski

  Carnegie Mellon University

  18 shared

• Leslie Leiserowitz

  Weizmann Institute of Science

  18 shared

• C. J. Palmstrøm

  18 shared

Education

• B.S., Materials Engineering

  Technion - Israel Institute of Technology

  2003

• B.A., Physics

  Technion - Israel Institute of Technology

  2003

• Ph.D.

  Weizmann Institute of Science

  2010

Awards & honors

• Shimon Reich Memorial Prize of Excellence for her thesis
• Sanibel Symposium Young Investigator Award (2016)
• NSF CAREER (2016)
• DOE Innovative and Novel Computational Impact on Theory and…
• Charles E. Kaufman Young Investigator Award (2017)