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Select Paper: ChainSpace: Sequence-Aware Design for Machine Learning-Driven Polymer Engineering
Polymers have entered into every aspect of society, from familiar commodity items such as disposable plastic bags to high-value technological applications in electronics, energy, and medicine.
Polymers have entered into every aspect of society, from familiar commodity items such as disposable plastic bags to high-value technological applications in electronics, energy, and medicine.
PhD Studies: University of Minnesota
PhD Studies: University of Minnesota
In the modern drug discovery pharmaceutical pipeline, the vast majority of new drug candidates are extremely hydrophobic in nature, rendering them incompatible with the most popular, non-invasive, and low-cost route of administration for people worldwide, oral drug delivery.
To overcome this solubility problem, spray-dried dispersions (engineered solid-solid mixtures of amorphous drugs and polymeric carriers) have the potential of elevating drug solubility by orders of magnitude by maintaining drugs at supersaturation, thereby enhancing therapeutic potency and oral bioavailability over pharmaceutically-relevant timescales.
However, utilization of these materials is hindered by a lack molecular-level understanding between drugs and polymers. For my doctoral studies, rational approaches to establish structure-property relationships using well-defined, modular polymers were pursued.
Results elucidated new ways to carefully construct solid dispersion vehicles and controllably encapsulate drugs to direct storage and release properties. These findings provide essential synthetic tools and ingredients to meet unfulfilled drug formulation needs in today’s pharmaceutical landscape.
Postdoctoral Research: University of Chicago
Postdoctoral Research: University of Chicago
Polyelectrolyte complexes (PECs) can address broad societal needs as key components in advanced biomaterials. PECs are assemblies of oppositely-charged polymers in aqueous solutions, which can be used to judiciously design multifunctional nanomedicine carriers.
My current work involves studying the dynamics and structural evolution of multiblock PECs. The goal of this work is to better understand how chain microstructures, properties, and hierarchical architectures affect the assembly mechanism and kinetics of PEC formation, which can range from PEC-core micelles to stimuli-responsive hydrogels.
This work is being conducted under the Center for Hierarchical Materials Design (CHiMaD), a NIST Advanced Materials Center of Excellence program towards the goal of “Materials by Design” under President Obama’s 2011 Materials Genome Initiative (MGI).
Senior Polymer Scientist: 3M
Senior Polymer Scientist: 3M
Advances in polymer science and engineering have enabled the opportunity to create a near-infinite spectrum of macromolecules. This synthetic versatility poses a new dilemma: how can decisions be made on what to create, so that the expanded diversity in building blocks can be harnessed into useful new products and technologies?
Polymer informatics (integration of machine learning/AI with experiment, simulation, and theory) has given glimpses into properties prediction for such heteropolymers. However, this emerging paradigm requires organized data libraries of polymer systems and formulations, with labeled features that are searchable.
The Corporate Research Labs at 3M are engaged in an exciting effort to digitize our data and build an online infrastructure that is amenable for materials informatics, leveraging the materials science expertise of industrial research scientists with the proficiency of talented data scientists. This collaborative effort began at the end of 2018 - I was the first direct-hire into the Materials Informatics Group as an experimentalist with the goal of leading the development of high-throughput experimental capabilities in the Corporate Research Materials Lab.
Dr. Corinne Lipscomb, Chief Product Owner of Materials Informatics, was recently featured in the ACS POLY Member Spotlight and discusses this further here.
Nanite
Nanite
Nanite's AI-driven platform, SAYER™ combines cutting-edge high-throughput experimental and computational methods to design fit-for-purpose delivery vehicles for cargo and tissue specificity. Learn more about Nanite's platform online
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