Paulette Clancy

About

Paulette Clancy is the Edward J. Schaefer Professor in Engineering at the Department of Chemical and Biomolecular Engineering at Johns Hopkins University. She is known for her work in computational materials processing, focusing on developing new machine learning approaches to advance materials discovery and processing, including Bayesian optimization and active learning. Her research encompasses a broad scope of materials, from semiconductors to image processing and biomimetic situations, with a specialization in complex solution processing scenarios. Clancy is the director of research for the JHU Data Science and AI initiative, associate director of the Johns Hopkins Center for Integrated Structure-Mechanical Modeling and Simulation (CISMMS), and a fellow of the Hopkins Extreme Materials Institute (HEMI). She leads a prominent research group studying atomic- and molecular-scale modeling of semiconductor materials, including traditional silicon-based compounds and all-organic materials. Her group's research areas include advanced organic materials such as covalent organic frameworks and organic electronics, algorithm development involving force field development and Bayesian optimization, electronic materials like III-IV semiconducting materials, and nucleation and crystal growth of hybrid organic/inorganic perovskites and quantum dot nanocrystals. Her lab focuses on understanding the links between processing, structure, and function in advanced materials, with current…

Research topics

• Computer Science
• Artificial Intelligence
• Information Retrieval
• Nanotechnology
• Library science
• Organic chemistry
• Chemistry
• Computational biology
• Programming language
• Inorganic chemistry

Selected publications

• Sulfur-Donor Solvents Strongly Coordinate Pb²⁺ in Hybrid Organic–Inorganic Perovskite Precursor Solutions

  The Journal of Physical Chemistry C · 2020 · 64 citations

  Strong coordination between Lewis-basic processing additives and the Lewis-acidic lead halide in hybrid organic–inorganic perovskite (HOIP) precursor solutions is required to solubilize the lead halide, and subsequently access the appropriate crystallization kinetics and attain the desired morphology of perovskite active layers. While oxygen-donor solvents and additives, such as dimethylformamide and dimethyl sulfoxide, are widely used for perovskite processing, we demonstrate here that “soft” s…

  DOI

• Cost-effective materials discovery: Bayesian optimization across multiple information sources

  Materials Horizons · 2020 · 39 citations

  Senior authorCorresponding

  Multi-information source Bayesian optimization and how it can be used to capture relevant information from cheap approximations to accelerate research in the materials sciences.

  DOI

• Machine learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications

  Journal of Magnesium and Alloys · 2024-06-01 · 29 citations

  preprintOpen access

  Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability, biocompatibility, and impressive mechanical characteristics. However, their rapid in-vivo degradation presents challenges, notably in upholding mechanical integrity over time. This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy, ZX10. Utilizing rapid, cost-efficient char…

  PublisherDOI

• Rational design of optimal bimetallic and trimetallic nickel-based single-atom alloys for bio-oil upgrading to hydrogen

  Nature Communications · 2025-03-18 · 15 citations

  articleOpen access

  Designing highly active, cost-effective, stable, and coke-resistant catalysts is a hurdle in commercializing bio-oil steam reforming. Single-atom alloys (SAAs) are captivating atomic ensembles crosschecking affordability and activity, yet their stability is held questionable by trial-and-error synthesis practices. Herein, we employ descriptor-based density functional theory (DFT) calculations to elucidate the stability, activity, and regeneration of Ni-based SAA catalysts for acetic acid dehydro…

  PublisherOA PDFDOI

• Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning

  Scientific Reports · 2024-08-02 · 9 citations

  articleOpen access

  In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in op…

  PublisherOA PDFDOI

Recent grants

• New, GK-12 Grass Roots: Advancing Education in Renewable Energy and Cleaner Fuels through Collaborative Graduate Fellow/Teacher/Grade-School Student Interactions

  NSF · $1.4M · 2011–2015

• NEB: Ultimate Electronic Device Scaling Using Structurally Precise Graphene Nanoribbons

  NSF · $1.2M · 2011–2016

Frequent coauthors

• Kentaro Okano

  Kobe University

  1600 shared

• Raymond Woon Sing Wong

  King's College London

  1600 shared

• I Ibrahim

  1600 shared

• Jodie L. Lutkenhaus

  Texas A&M University

  1600 shared

• Jeremy J. Baumberg

  University of Cambridge

  1600 shared

• Paul F. Scott

  University of Cambridge

  1600 shared

• Bernhardt L. Trout

  1600 shared

• Allison Holloway

  Nanjing University

  1600 shared

Labs

• Computational Materials LabPI

Education

• D.Phil., Chemistry

  University of Oxford

  1977

Awards & honors

• American Institute of Chemical Engineers (AIChE) National Wo…
• Alice Cook Award for services promoting women in science at…
• Zellman Warhaft award for the promotion of diversity in Corn…
• Edward J. Schaefer Professor in Engineering

Similar researchers at Johns Hopkins University

• Denis Wirtzh-109
• Chao Wangh-93
• Jennifer Elisseeffh-87