About

Emily A. Carter is the Gerhard R. Andlinger Professor in Energy and the Environment and a Professor of Mechanical and Aerospace Engineering at Princeton University. She is also affiliated with the Andlinger Center for Energy and the Environment and Applied and Computational Mathematics. Since joining Princeton in 2004, she has developed a distinguished career in physical chemistry, focusing on the development and application of quantum mechanical simulation techniques to enable the discovery and design of materials for sustainable production of fuels, chemicals, and materials. Her research supports the creation of innovative solutions to global challenges related to energy and the environment. Dr. Carter has held numerous leadership roles, including founding director of Princeton’s Andlinger Center for Energy and the Environment, Dean of the School of Engineering and Applied Science, and senior strategic advisor at the Princeton Plasma Physics Laboratory. Her work at PPPL since 2022 involves diversifying research into electromanufacturing, solar radiation management, microelectronics, and quantum information science. She has an extensive publication record with over 475 publications and patents, and her contributions have been recognized through election to prestigious academies such as the U.S. National Academy of Sciences, the American Academy of Arts and Sciences, and the Royal Society. Her academic background includes a B.S. in Chemistry from UC Berkeley and a Ph.D. in Physical Chemistry from Caltech.

Research topics

• Chemistry
• Materials science
• Computer Science
• Optoelectronics
• Organic chemistry
• Mathematics
• Engineering
• Computational chemistry
• Nanotechnology
• Engineering physics
• Physics
• Photochemistry
• Chemical engineering
• Physical chemistry
• Environmental science
• Chemical physics
• Database
• Pure mathematics
• Inorganic chemistry

Selected publications

• Learning Traversable Scene Structures for Embodied Navigation with Movable Object Constraints Authors

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Combining Density Functional Embedding Theory and DMRG-NEVPT2 to Treat Large Active Spaces: Addressing Electronic Structure Complexity in Single-Atom Alloys

  Journal of Chemical Theory and Computation · 2026-02-19

  articleOpen accessSenior author

  -electron valence state second-order perturbation theory (DMRG-NEVPT2) methods in the PySCF code. Using embedded DMRGSCF and embedded DMRG-NEVPT2, we analyze CO adsorption on Ni-, Rh-, Pd-, and Pt-doped Ag(100) with different active spaces. We show that the active spaces approachable with conventional multireference methods lead to overbinding of CO due to an inability to treat all of the dopant d-orbitals on equal footing. Larger active spaces, which are easily treated by both DMRGSCF and DMRG-NEVPT2, yield much more reasonable adsorption free energies. Our findings suggest that future multireference calculations of these systems should similarly employ active spaces containing all of the dopant d-orbitals along with sp-band orbitals of the host metal near the Fermi level. Emb-DMRG-NEVPT2 is a method that can be broadly applied to study catalytic reactions on metal surfaces when large active spaces are required.

  PublisherDOI

• Insights into Nonelectroactive C–C Bond Formation on Cu(100) during Electrochemical CO ₂ Reduction from Multiconfigurational Wavefunction Theory

  The Journal of Physical Chemistry C · 2026-02-27 · 2 citations

  articleOpen accessSenior authorCorresponding

  Carbon–carbon (C–C) bond formation is necessary for hydrocarbon (and oxygenate) synthesis beyond methane (and formate/formic acid) during electrochemical CO and CO2 reduction (ECOR and ECO2R). Cu has notable ability to form hydrocarbons compared to other pure metals. In particular, the (100) facet of face-centered cubic Cu forms ethylene competitively with H2 and methane during both ECOR and ECO2R. Past simulations based on density functional theory (DFT) with standard exchange-correlation functional approximations predict fast nonelectroactive C–C bond formation channels involving adsorbed (*) CO together with another *CO, formyl (*CHO), or hydroxymethylidyne (*COH), forming OC*–*CO, OC*–CHO*, and OC*–*COH, respectively. Such simulations support the prevailing hypothesis that emergence of C2 products is kinetically determined at the early stages of the reduction chemistry. Here we show, via simulations with more accurate many-body, i.e., “correlated”, wavefunction theory (enabled by an embedding scheme), that the coupling of *CO with a *CO or a *COH (previously predicted at the same level of theory to kinetically dominate over *CHO as the one-electron reduction product of *CO) is highly activated (kinetically impeded), with free energy barriers >1 eV, in contradiction to previous DFT-based simulations. Intriguingly, we find that the coupling of two adjacent *COHs incurs only a small barrier (<0.3 eV) and is exoergic (< –1 eV); however, given the predicted low surface mobility of *COH, the emergence of HOC*–*COH is also improbable, at least at low *COH coverages. We therefore conclude that it is highly unlikely for *CO to participate in nonelectroactive C–C bond formation on pristine Cu(100), contrary to conventional wisdom, and that the energetically favorable *COH dimerization may occur only after substantial buildup of *COH on the surface.

  PublisherDOI

• C–C Bond Formation during Electrochemical CO₂ Reduction on Pristine Cu(100) Unlikely to Involve AdsorbedCO at Any Potential

  Figshare · 2026-02-25

  articleSenior author

  Formation of hydrocarbons containing two or more carbon atoms (C₂₊) during heterogeneous electrochemical CO and CO₂ reduction (ECOR and ECO₂R) only occurs, among pure metals, on Cu electrodes. Moreover, the activity and selectivity is facet dependent, with Cu(100) generally preferentially forming ethylene over methane. Previously, we found via quantum-mechanics-based modeling that, unlike standard density functional theory, more accurate correlated wavefunction methods predict that non-electroactive coupling pathways involving two adsorbed COs (*CO) or a *CO and a *COH to form C–C bonds on Cu(100) are kinetically inhibited, with the former also thermodynamically unfavorable. Here, we extend that embedded complete active space second order perturbation theory (ECASPT2) study, further showing that electrochemical coupling of two *COs to form an anionic dimer [OC*–*CO]^(1+δ)–, followed by protonation to form [OC*–*COH]^δ−, is not kinetically competitive with the reduction of *CO to *COH at relevant ECO/CO₂R potentials. Our simulations therefore suggest that the ability of Cu(100) to electrochemically synthesize C₂₊ molecules from CO and CO₂ is unlikely to be via *CO, at least on pristine Cu(100). Instead, hydrogenated CO species (*COH, *CH_<i>x</i>OH, or *CH_<i>x</i>) are most likely to be the key intermediates in C–C bond formation.

  DOI

• Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion-Pairing

  arXiv (Cornell University) · 2026-03-16

  preprintOpen accessSenior author

  Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving training and computational efficiency of machine learned interatomic potentials. We demonstrate the framework on Ca2+-CO32- ion pairing in aqueous solution, a key process underlying CO2 mineralization in seawater. As proof of principle, we first show that finetuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to large-scale simulations of complex aqueous and interfacial chemical processes.

  PublisherDOI

• C–C Bond Formation during Electrochemical CO ₂ Reduction on Pristine Cu(100) Unlikely to Involve Adsorbed CO at Any Potential

  Journal of the American Chemical Society · 2026-02-12 · 2 citations

  articleOpen accessSenior author

  ) are most likely to be the key intermediates in C-C bond formation.

  PublisherDOI

• Learning Traversable Scene Structures for Embodied Navigation with Movable Object Constraints

  Preprints.org · 2026-01-26

  preprintOpen access

  Understanding how movable objects affect navigability is critical for embodied agents operating in realistic environments. This study proposes a learning-based approach to infer traversable scene structures under object mobility constraints. A neural graph encoder is trained to predict passability relations between spatial regions conditioned on object states, using RGB-D observations and interaction feedback. The model is trained on 15,000 simulated navigation trajectories generated in rearranged indoor scenes. Quantitative evaluation shows that the learned scene structure reduces navigation failure due to blocked paths by 28.4% and improves average navigation efficiency by 16.7% compared with static scene graph representations.

  PublisherOA PDFDOI

• Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion-Pairing

  ArXiv.org · 2026-03-16

  articleOpen accessSenior author

  Achieving chemical accuracy for molecular simulations remains a central challenge in computational chemistry. Here, we present an embedded correlated wavefunction transfer learning (ECW-TL) framework for accurately simulating molecular dynamics in the condensed phase. ECW-TL incorporates high-level electron exchange and correlation effects in ECW theory while preserving training and computational efficiency of machine learned interatomic potentials. We demonstrate the framework on Ca2+-CO32- ion pairing in aqueous solution, a key process underlying CO2 mineralization in seawater. As proof of principle, we first show that finetuning a DFT-revPBE-D3(BJ) baseline model with embedded-DFT-SCAN data reproduces the DFT-SCAN free-energy surface within 1 kcal/mol across all solvation states. Extending the framework to embedded MP2 and localized natural-orbital CCSD(T) further refines the free-energy profile, revealing the crucial role of exact electron exchange and correlation in determining ion-pair stability and structure. ECW-TL thus provides a general, data-efficient route for transferring CW accuracy to large-scale simulations of complex aqueous and interfacial chemical processes.

  PublisherOA PDF

• Multi-Objective Design of Heat Sink Fins for Thermal Efficiency and Manufacturability

  International Journal of Engineering Advances · 2025-03-17

  articleOpen access

  As power densities in modern electronics increase, efficient thermal management is essential. Conventional heat sink designs often fail to balance heat dissipation, airflow resistance, and manufacturability. This study proposes an AI-driven optimization framework, integrating deep reinforcement learning (DRL) and multi-objective genetic algorithms (MOGA), to refine fin geometries while ensuring fabrication feasibility. Unlike conventional methods, this approach incorporates additive manufacturing constraints, bridging the gap between computational optimization and real-world implementation. Validated through computational fluid dynamics (CFD) simulations and experimental fabrication, the optimized design achieved a 14.3% reduction in maximum temperature and a 32.8% decrease in thermal resistance, ensuring a more uniform temperature distribution. It also maintained stable cooling performance across airflow variations, confirming its adaptability. Manufacturability analysis revealed height deviations of up to 0.4 mm, which could affect airflow, while thickness deviations remained within ± 0.05 mm, indicating high precision. These results highlight the importance of integrating fabrication constraints early in the design process to ensure optimization benefits translate into practical performance. This study shows that AI-driven optimization can enhance heat sink efficiency and reliability, offering a scalable approach for high-power electronics. Future work should refine manufacturing compensation models and transient thermal analysis to further improve real-world applicability.

  PublisherOA PDFDOI

• Innovations in Sustainable Transportation Infrastructure for Cities

  American Journal Of Civil Construction And Environmental Engineering · 2025-06-30

  articleOpen access1st authorCorresponding

  As cities continue to grow and urbanize, sustainable transportation infrastructure has become essential for reducing carbon emissions, promoting environmental health, and improving urban mobility. Innovative approaches to sustainable transportation infrastructure are transforming the way cities plan and design transportation systems. This article explores the latest innovations in sustainable transportation infrastructure, focusing on green mobility solutions, energy-efficient public transit systems, and smart urban planning. It highlights the role of sustainable transportation in mitigating climate change, improving air quality, and promoting equitable access to transportation options for all city residents.

  PublisherOA PDFDOI

Recent grants

• Linear Scaling Electronic Structure Methods for Molecules and Materials

  NSF · $600k · 2009–2013

• CDS&E: Correlated Wavefunction Methods for Gas and Condensed Phases

  NSF · $420k · 2013–2018

• Linear Scaling Electronic Structure Methods for Molecules and Materials

  NSF · $420k · 2005–2009

Frequent coauthors

• William C. Chueh

  2921 shared

• Robert H. Socolow

  2918 shared

• S. Sampath

  Arunai Engineering College

  2916 shared

• Kate Bandoo

  Imperial College London

  2916 shared

• Sam Keltie

  University of Twente

  2916 shared

• Dirk M. Guldi

  Friedrich-Alexander-Universität Erlangen-Nürnberg

  2916 shared

• Yan Yu

  Princeton University

  2916 shared

• Molina Santos

  Aarhus University

  2916 shared

Education

• Postdoctoral Fellow, Chemistry

  University of Colorado

  1988

• PHD, Chemistry

  California Institute of Technology

  1987

• BS, Chemistry

  University of California Berkeley

  1982

Awards & honors

• Election to the U.S. National Academy of Sciences
• Election to the American Academy of Arts and Sciences
• Election to the U.S. National Academy of Inventors
• Election to the U.S. National Academy of Engineering
• Election to the European Academy of Sciences