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Debra Audus ()

Leader, Polymer Analytics Project

Research Interests

Theory aware machine learning

The application of machine learning to polymer physics has traditionally struggled with two major challenges: a lack of large, curated datasets and the need to understand the physics behind the machine learning prediction. Here we aim to simultaneously tackle these challenges through the incorporation of domain knowledge often in the form of theory, thus, providing improved predictions for smaller datasets while improving explainability. This effort is under the Polymer Analytics project.

Polymer databases

To spur advances in machine learning for polymer science, we also collaborate with external partners to develop the polymeric databases that such algorithms require. In collaboration with MIT, University of Chicago, Citrine Informatics, and Dow we are developing a Community Resource for Innovation in Polymer Technology (CRIPT). This project is led by Prof. Brad Olsen and funded by the NSF Convergence Accelerator. Using FAIR data principles, CRIPT helps users input, visualize and share polymeric data. In collaboration with Prof. Ian Foster, Prof. Juan de Pablo and colleagues at University of Chicago we are developing the Polymer Property Predictor and Database. Specifically, we are exploring methods for extracting polymer properties from the literature using machine learning and are also working on building a polymer rheology database based on experiments from the Center for Hierarchical Materials Design (CHiMaD). These efforts are under the Polymer Analytics project.

Circular economy

The need to address the growing waste stream of plastics has become a global challenge. To enable next generation recycling, we apply simulation techniques and machine learning to improve characterization of a common polymer, linear low density polyethylene and to identify methods for upcycling mixed waste streams through compatibilization. We also are investigating how to improve near infrared measurements of polyolefins through correlation with other slower, measurement techniques. All of these efforts are in the Polymer Analytics project and in collaboration with the Macromolecular Architectures project.

Hydrodynamic properties of dilute particle solutions

We help develop ZENO, a Monte Carlo based code that computes several quantities including the intrinsic viscosity and hydrodynamic radius. It has been previously shown to generate results within experimental uncertainty. More information including links to the source code and web app version can be found at

Polyelectrolyte complexation

Aqueous solutions of oppositely charged polymers, under certain conditions, can macrophase separate yielding a polymer rich phase known as a complex coacervate and a polymer poor phase often with vanishing polymer concentrations. We explore this phase separation in systems of copolymers containing charged blocks and neutral, hydrophilic blocks, as well as homopolymers using a combination of simulation and theory. This effort is a part of the Fundamentals of Macromolecular Self Assembly project.

A selected list of publications is below. For a complete list see Google Scholar.

Recent invited presentations

  • "Incorporating polymer theory in machine learning for improved prediction and understanding," Polymer Physics Gordon Research Conference, South Hadley, MA, 2022

  • "Leveraging polymer theory for improved machine learning," Materials Research Society Spring Meeting, Virtual, 2022

  • "Enabling machine learning for polymer science," University of Delaware NRT Community Hour, Virtual, 2022

  • "Overcoming data scarcity in polymer science," CHiMaD/NIST ODI Seminar Series, Virtual, 2022

  • "Improving prediction and understanding with theory aware machine learning," American Physical Society March Meeting, Chicago, IL, 2022

  • “Enabling polymer informatics through databases,” Center for Integrated Nanotechnologies Annual Meeting Symposia, Santa Fe, NM, 2019 

  • “Towards polymer informatics: databases, infrastructure and beyond,” NIST/Industry Polymer Surface/Interface Consortium Meeting, NIST, Gaithersburg, MD, 2019
  • “Modeling the complex with the simple: case studies in soft matter,” Graduate Simulation Seminar Series Keynote, University of California, Santa Barbara, CA, 2019
  • “Databases and beyond: enabling polymer informatics,” Machine Learning in Science and Engineering, Atlanta, GA, 2019
  • “Towards polymer informatics: databases, infrastructure and beyond,” 31st International Symposium on Polymer Analysis and Characterization, Rockville, MD, 2018
  • “Enabling polymer informatics through database creation,” MGI-PI Meeting, College Park, MD, 2018
  • “Accelerating polymeric database creation,” ACS Spring Meeting, New Orleans, LA, 2018

Research Opportunities

Contact me for further details. Current programs include:


  • Communications Materials Outstanding Referee, 2023
  • World Materials Research Institutes Forum Young Materials Scientist Finalist, 2014
  • National Research Council Postdoctoral Fellowship, 2013-2015
  • American Physical Society Frank J. Padden Jr. Award Finalist, 2012
  • School for Scientific Thought Fellowship, UC Santa Barbara, 2010
  • Materials Research Laboratory Diversity Fellowship, UC Santa Barbara, 2010
  • Air Products Fellowship, 2010
  • CSP Technologies Fellowship, 2009


Correlating Near-Infrared Spectra to Bulk Properties in Polyolefins

Bradley Sutliff, Shailja Goyal, Tyler Martin, Peter Beaucage, Debra Audus, Sara Orski
The industry standard for sorting plastic wastes is near-infrared (NIR) spectroscopy, which offers rapid and nondestructive identification of various plastics

Quantifying Pairwise Similarity for Complex Polymers

Jiale Shi, Nathan Rebello, Dylan Walsh, Michael Deagen, Bruno Salomao Leao, Debra Audus, Bradley Olsen
Defining the similarity between chemical entities is an essential task in polymer informatics, enabling ranking, clustering, and classification. Despite its
Created July 30, 2019, Updated March 24, 2023