About

Professor Marina S. Leite is the Principal Investigator of the Leite Lab, specializing in Materials Science and Engineering. Her research group focuses on renewable energy and advanced devices, with a particular emphasis on the study and development of perovskite solar cells and related optoelectronic materials. The lab's work includes characterization, photovoltaic properties, computational modeling, and nanoscale electrical imaging of perovskite materials, aiming to improve the stability and performance of these solar cells. Professor Leite leads a diverse team of graduate and undergraduate students, post-doctoral scholars, and visiting researchers, fostering a collaborative environment dedicated to advancing materials science for energy applications.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Systems engineering
• Electrical engineering
• Engineering
• Political Science
• Data science
• Materials science
• Business
• Chemistry
• Nanotechnology
• Biological system
• Engineering physics
• Statistics
• Econometrics
• Algorithm
• Optoelectronics
• Mathematics
• World Wide Web
• Risk analysis (engineering)
• Telecommunications
• Software engineering
• Reliability engineering

Selected publications

• Nanoscale imaging of local electrical behavior in halide perovskites

  Journal of materials research/Pratt's guide to venture capital sources · 2026-01-05

  articleOpen accessSenior author

  Abstract Halide perovskites (HPs) exhibit favorable characteristics for photovoltaics and LEDs including optimal optoelectronic properties, bandgap tunability, and defect tolerance. However, these devices are currently limited by their instability and the mechanism behind their degradation is not currently fully understood. Atomic force microscopy (AFM), specifically conductive (c-AFM) and Kelvin-probe force microscopy (KPFM), have been widely utilized to investigate the nanoscale electrical behavior of HPs to further the understanding of the factors driving material and device degradation. In this mini-review, we briefly discuss the operating principles of c-AFM and KPFM and highlight experiments in which these techniques have been employed to investigate the effects of grain boundaries, chemical composition, and environmental factors to the electrical stability of HPs. We also provide an overview of recent applications of machine learning (ML) to automate AFM data extraction and analysis and share a perspective on the opportunities for ML methods in AFM measurements of HPs. Graphical abstract

  PublisherDOI

• Reversible, Photo‐Induced Lattice Distortions in Halide Perovskites

  Advanced Materials · 2026-03-03

  articleSenior authorCorresponding

  ABSTRACT The unique characteristics of soft‐lattice halide perovskites (HPs) have motivated extensive research into their application in semiconductor devices. Mechanically and chemically modulated strain in monocrystalline HPs improves stability and phase purity. However, a comprehensive understanding of the photoexcitation‐driven lattice distortions arising from the strong electron‐phonon coupling paired with the dynamics of the A‐site cation remains underdeveloped. Here, we present the transient lattice distortions occurring in single crystal MAPbBr 3 , FAPbBr 3 , and CsPbBr 3 in response to above‐bandgap light excitation. Using an X‐ray probe, we uncover reversible and hysteresis‐free photoinduced lattice distortion. We find that the photoinduced distortion in the HPs is a function of the pump power, with CsPbBr 3 showing the highest resilience against lattice deformation with a 0.062% change in its out‐of‐plane lattice parameter. Conversely, the organic HPs unveil a stronger interaction with photocarriers resulting in more significant yet elastic distortion, with MAPbBr 3 exhibiting up to 0.3% change. We demonstrate the modality of this distortion by varying the excitation power over 20 distinct states and cycles, highlighting the suitability of HPs as building blocks for electrostriction devices. Our findings represent a key step toward establishing HPs as ideal platforms for optical and strain‐driven switchable photonic devices.

  PublisherDOI

• From Materials to Machine Learning: A Photonics-Based Design of Thermophotovoltaic Emitters

  ACS Photonics · 2026-04-22

  articleSenior authorCorresponding

  Thermophotovoltaics (TPV) generate electricity through the conversion of radiation from an optical emitter, whose emissive spectra can be shaped to optimize efficiency. Among proposed emitter designs, thin-film multilayers offer a practical balance of spectral control, thermal stability, and manufacturability. In this Perspective, we first analyze the theoretical limits of spectral shaping on TPV efficiency and power, second we examine real materials as bulk emitters. Third, we emphasize how multilayer coatings with high refractive index contrast and aperiodic thicknesses enhance TPV performance by mitigating spectral losses. Fourth, we highlight machine learning as a scalable tool for navigating the multilayer parameter space through a representative comparison against a traditional algorithm. Finally, we discuss practical considerations for implementing emitters and further potential of machine learning for TPV. Together, these insights outline a materials- and photonics-driven pathway for next-generation TPV systems, where selective substrates, robust coatings, and data-driven optimizations push device efficiencies toward their fundamental limits.

  PublisherDOI

• Predicting the dynamic behavior of halide perovskites through machine learning models

  2026-03-05

  article1st authorCorresponding
  PublisherDOI

• Impacts of environmental stressors on charge carrier lifetimes in Pb-free halide perovskites

  2025-03-19

  articleSenior author

  Halide perovskites are strong contenders in becoming the next leading photovoltaic and light emitting technology. However, the dynamic nature of trap states in response to environmental stressors can alter the behavior of charge carriers in ways not well understood. Here, we investigate the impact of temperature and relative humidity on the minority carrier lifetimes in FASnI3-yBry family of compositions, by means of in situ time-resolved photoluminescence. We track the carrier dynamics as a function of time, environmental conditions, and excitation parameters to determine the impact of compositional variability on formation and energetics of trap states. Our results reveal that Sn-based perovskites show photostability from 10-60 oC at relative humidity below 10%. Furthermore, we observe shorter lifetimes in Br-rich films, suggesting lower crystallinity and higher density of trap states and self-doping compared to I-rich films.

  PublisherDOI

• Author response for "An AI-accelerated pathway for reproducible and stable halide perovskites"

  2025-06-26

  peer-reviewSenior author
  PublisherDOI

• Ultra-high-temperature photonics: a materials' screening perspective

  2025-09-16

  article1st authorCorresponding

  In this talk I will present our recent progress towards identifying and testing materials that are suitable for high-temperature photonics. First, I will provide an overview of our materials’ screening approach with >2,800 material combinations with melting point >2,000 oC to identify optical emitters that enable thermophotovoltaics with power conversion efficiency >50%. Second, we analyze the performance of SiC/AlN option, where we found this material combination to be stable up to 1,200 oC in air and 1,500 oC in inert environments, using in situ high-temperature optical measurements. Third, I will show how primary colors can be achieved by using refractory metals and their oxides, including a detailed characterization of their optical response.

  PublisherDOI

• Using automated experiments and machine learning to identify composition-property relationships in halide perovskites

  2025-03-19

  articleSenior author

  We use a custom-built, high throughput, in situ photoluminescence (PL) characterization setup to collect sufficient data to train a variety of machine learning (ML) models and to quantify the degradation of several different perovskite compositions. We compare forecasts of PL of several CsyFA(1-y)Pb(BrxI(1-x))3 perovskites in response to temperature cycling with extreme gradient boosting (XGBoost) algorithms. These models enable predictions of PL figures of merit (peak location, area, intensity, and full-width half max (FWHM)), with up to 98% accuracy. Furthermore, we create a generalized model that can predict the PL behavior of ten compositions unseen during model training. The relative feature importance and correlations between environmental inputs and optical performance show that composition dominates sample degradation.

  PublisherDOI

• An AI-accelerated pathway for reproducible and stable halide perovskites

  Chemical Society Reviews · 2025-01-01 · 9 citations

  articleSenior authorCorresponding

  Halide perovskites (HPs) have remarkable optoelectronic properties, and in the last decade their photovoltaic power conversion efficiency and light-emitting diode efficiency have skyrocketed. Despite the surge in research on these burgeoning materials, two key challenges in the field remain: material irreproducibility and instability. Their behavior is especially dynamic in response to environmental stressors, due to complex interactions with the perovskite crystal lattice. In this review, we survey the latest achievements in HP materials research accomplished with the assistance of artificial intelligence (AI), through the implementation of automated experimentation and machine learning (ML) data analysis. Automated synthesis and characterization tackle problems with material irreproducibility by systematically controlling parameters with very high precision, creating massive datasets, and allowing methodical comparisons from which unbiased conclusions can be drawn. AI can reveal otherwise unnoticed trends, inform future experiments with the highest potential information gain, and forecast future performance. The review concludes with a forward viewpoint of how human-assisted closed-loop laboratories and shared databases allow halide perovskite materials' processing, properties, and performance to be potentially optimized with AI, accelerating the development of highly reproducible and stable optoelectronic devices.

  PublisherDOI

• Transition-Metal Nitrides for High-Temperature Structural Colors

  ACS Applied Materials & Interfaces · 2025-05-08 · 10 citations

  articleOpen accessSenior authorCorresponding

  Transition-metal nitrides (TMNs), such as hafnium nitride (HfN), titanium nitride (TiN), and zirconium nitride (ZrN), have emerged as highly promising materials in photonics and plasmonics, drawing significant interest due to their optical properties comparable to those of conventional plasmonic materials like Ag and Ag, with remarkable thermal and chemical stability. While TMNs possess high bulk melting points and impressive resistance to degradation, the impact of scaling down to nanoscale dimensions and exposure to oxidizing environments under high temperatures on their optical properties remains largely underexplored. In this work, we establish a comprehensive experimental framework combining in situ optical characterization and ex situ surface analysis to explore the behavior of TMNs at 600 °C with exposure to oxygen. This oxidation process enables gradual color transitions in TMNs, thereby opening pathways for innovative applications in high-temperature structural color for printing. We further investigate aluminum oxide (Al2O3) as a protective coating to effectively prevent oxidation and preserve optical behaviors up to 830 °C, making these coatings suitable for applications in demanding thermal environments. The findings highlight TMNs’ potential in next-generation high-temperature photonic devices, balancing optical performance and durability in challenging environments.

  PublisherDOI

Recent grants

• Integrated Research and Education on Metal Alloys with On-Demand Optical Response

  NSF · $390k · 2016–2020

• Tackling Instability in Perovskite Solar Cells through Machine Learning

  NSF · $464k · 2020–2026

• Nanoscale spectroscopy of hybrid perovskite solar cells: resolving the role of humidity on device stability through in situ microscopy

  NSF · $300k · 2016–2020

• Integrated Research and Education on Metal Alloys with On-Demand Optical Response

  NSF · $282k · 2019–2022

• Resolving Interphases in Solid Electrolyte Batteries through Time-of-Flight Secondary Ion Mass Spectroscopy

  NSF · $327k · 2020–2023

Frequent coauthors

• Jeremy N. Munday

  54 shared

• G. Medeiros‐Ribeiro

  Universidade Federal de Minas Gerais

  32 shared

• Elizabeth M. Tennyson

  31 shared

• A. Alec Talin

  Sandia National Laboratories California

  27 shared

• Chen Gong

  24 shared

• Dmitry Ruzmetov

  United States Army Combat Capabilities Development Command

  20 shared

• Tao Gong

  Shenzhen Polytechnic

  18 shared

• John M. Howard

  University of Maryland, College Park

  18 shared

Labs

• Leite LabPI

Education

• PhD physics, Physics

  Universidade Estadual de Campinas

  2008

• M.S., Physics

  State University of Campinas

  2003

• B.S., Chemistry

  Federal University of Pernambuco

  2000

Awards & honors

• Optica Fellow (2025)
• SPIE Fellow (2025)