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Search Publications by: Debra Audus (Fed)

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Displaying 1 - 25 of 47

Effect of Cosolvents on the Phase Separation of Polyelectrolyte Complexes

September 13, 2024
Author(s)
Yuanchi Ma, Robert Ivancic, Jan Obrzut, Debra Audus, Vivek Prabhu
Evidence is shown that cosolvent mixtures control the coacervation of mixtures of oppositely charged polyelectrolytes. Binary and ternary solvent mixtures lead to non-monotonic solubility as a function of measured average dielectric constants of the

The Block Copolymer Phase Behavior Database

August 10, 2024
Author(s)
Nathan Rebello, Akash Arora, Hidenobu Mochigase, Tzyy-Shyang Lin, Debra Audus, Bradley Olsen
The Block Copolymer Database (BCDB) is a platform that allows users to search, submit, visualize, benchmark, and download experimental phase measurements and their associated characterization information for di- and multiblock copolymers. To the best of

Predicting the toughness of compatibilized polymer blends

June 19, 2024
Author(s)
Robert Ivancic, Debra Audus
Polymer blends can yield superior materials by merging the unique properties of their components. However, these mixtures often phase separate, leading to brittleness. While compatibilizers can toughen these blends, their vast design space makes

Correlating Near-Infrared Spectra to Bulk Properties in Polyolefins

February 28, 2024
Author(s)
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. However, NIR does not provide insights into the chain composition, conformation, and topology of

Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance

January 10, 2024
Author(s)
Jiale Shi, Dylan Walsh, Weizhong Zou, Nathan Rebello, Michael Deagen, Katharina Fransen, Xian Gao, Debra Audus, Bradley Olsen
Synthetic polymers, in contrast to small molecules and deterministic biomacromolecules, are typically ensembles composed of polymer chains with varying numbers, lengths, sequences, chemistry, and topologies. While numerous approaches exist for measuring

Quantifying Pairwise Similarity for Complex Polymers

September 6, 2023
Author(s)
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 importance, pairwise chemical similarity for polymers remains an open problem. Here, a similarity

AI for Materials

April 25, 2023
Author(s)
Debra Audus, Kamal Choudhary, Brian DeCost, A. Gilad Kusne, Francesca Tavazza, James A. Warren
The application of artificial intelligence (AI) methods to materials re- search and development (MR&D) is poised to radically reshape how materials are discovered, designed, and deployed into manufactured products. Materials underpin modern life, and

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

February 20, 2023
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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