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Search Publications by: Debra Audus (Fed)

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Displaying 1 - 18 of 18

Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure

February 20, 2023
Dylan Walsh, Weizhong Zou, Ludwig Schneider, Reid Mello, Michael Deagen, Joshua Mysona, Tzyy-Shyang Lin, Juan de Pablo, Klavs Jensen, Debra Audus, Bradley Olsen
Polymeric materials are integral components of nearly every aspect of modern life. However, developing cheminformatic solutions for polymers has been difficult since they are large stochastic molecules with hierarchical structures spanning multiple length

Networks and interfaces as catalysts for polymer materials innovation

October 27, 2022
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 intelligence (AI) and machine learning (ML)—play a foundational role in materials research and

Leveraging Theory for Enhanced Machine Learning

August 26, 2022
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 need to understand the physics behind the machine-learning prediction. The former problem is

Molecular Mass Dependence of Interfacial Tension in Complex Coacervation

June 11, 2021
Debra Audus, Samim Ali, Artem Rumyantsev, Yuanchi Ma, Juan J. de Pablo, Vivek Prabhu
The interfacial tension of coacervates, the liquidlike phase composed of oppositely charged polymers that coexists at equilibrium with a supernatant, forms the basis for multiple technologies. Here we present a comprehensive set of experiments and

Active Learning Yields Better Training Data for Scientific Named Entity Recognition

November 1, 2019
Roselyne B. Tchoua, Aswathy Ajith, Zhi Hong, Logan T. Ward, Kyle Chard, Debra Audus, Shrayesh N. Patel, Juan J. de Pablo
Despite significant progress in natural language processing, machine learning models require substantial expert-annotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort

June 8, 2019
Roselyne B. Tchoua, Aswathy Ajith, Zhi Hong, Logan T. Ward, Kyle Chard, Debra Audus, Shrayesh N. Patel, Juan J. de Pablo, Ian Foster
Scientific Named Entity Referent Extraction is often more complicated than traditional Named Entity Recognition (NER). For example, in polymer science, chemical structure may be encoded in a variety of nonstandard naming conventions, and authors may refer

Valence, loop formation and universality in self-assembling patchy particles

February 7, 2018
Debra J. Audus, Francis W. Starr, Jack F. Douglas
Patchy particles have emerged as an attractive model for phase separation and self-assembly in globular proteins solutions, colloidal patchy particles, and molecular fluids where directional interactions are operative. In our previous work, we extensively

Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline

November 27, 2017
Roselyne B. Tchoua, Kyle Chard, Debra Audus, Logan T. Ward, Lequieu Joshua, Juan J. de Pablo, Ian Foster
The emerging field of materials informatics has the potential to greatly reduce time-to-market and development costs for new materials. The success of such efforts hinges on access to large, high-quality databases of material properties. However, many such

Polymer Informatics: Opportunities and Challenges

September 15, 2017
Debra J. Audus, Juan J. de Pablo
We are entering an era where large volumes of scientific data, coupled with algorithmic and computational advances, can reduce both the time and cost of developing new materials. This emerging field known as materials informatics has gained acceptance for

A Hybrid Human-Computer Approach to the Extraction of Scientific Facts from the Literature

June 28, 2016
Roselyne B. Tchoua, Kyle Chard, Debra Audus, Jian Qin, Juan J. de Pablo, Ian Foster
A wealth of valuable data is locked within the millions of research articles published each year. Reading and extracting pertinent information from those articles has become an unmanageable task for scientists. This problem hinders scientific progress by

Field-based simulations of nanostructured polyelectrolyte gels

August 11, 2015
Debra J. Audus, Glenn Fredrickson
Nanostructured, responsive hydrogels composed of oppositely charged triblock copolymers with charged end-blocks and neutral, hydrophilic mid-blocks in aqueous solution were recently discovered. Due to electrostatic interactions, the end-blocks microphase

Interplay of particle shape and suspension properties: a study of cube-like particles

March 20, 2015
Debra J. Audus, Ahmed M. Hassan, Edward J. Garboczi, Jack F. Douglas
With advances in anisotropic particle synthesis, particle shape is now a feasible parameter for tuning suspension properties. However, there is a need to determine how these newly synthesized particles affect suspension properties and a need to solve the