Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Debra Audus (Fed)

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 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). 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. This effort is 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. Both 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

  • “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
  • “Characterizing patchy particle self-assembly,” Penn Polymer Symposium, University of Pennsylvania, Philadelphia, PA, 2017
  • “Self-assembly of patchy particles: coupling of directional and isotropic interactions,” Joint Soft Matter Seminar, Forschungszentrum Jülich, Jülich, Germany, 2016
  • “Accelerating materials discovery through the development of polymer databases,” American Physical Society March Meeting, Baltimore, MD, 2016
  • “Coupling of isotropic and directional interactions in patchy particles,” Complex Fluids Design Consortium Annual Meeting, Santa Barbara, CA, 2016

Research Opportunities

Contact me for further details. Current programs include:


  • 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


Networks and interfaces as catalysts for polymer materials innovation

Michael Deagen, Dylan Walsh, Debra Audus, Kenneth Kroenlein, Juan de Pablo, Kaoru Aou, Kyle Chard, Klavs Jensen, Bradley Olsen
Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial

Leveraging Theory for Enhanced Machine Learning

Debra Audus, Austin McDannald, Brian DeCost
The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the
Created July 30, 2019, Updated December 8, 2022