CAREER: Unveiling the Dynamics of Liquid-like Macromolecular Condensed Phases from Nucleation to Stability

The study of how macromolecules in solutions group together is essential to advance research in pharmaceuticals and biotechnology. The fundamental mechanisms governing this process is poorly understood. This knowledge gap limits our ability to precisely control these processes for therapeutic and industrial innovation. This project uses advanced computer modeling to explore the fundamental steps, aiming to unlock new possibilities for designing drug delivery systems, improving food preservation, and developing self-healing materials. This research will also provide insights into how certain cellular structures linked to aging and diseases behave. The project integrates education and outreach to enhance STEM participation and awareness. Activities include developing a summer program for high school students, fostering undergraduate research experiences, and creating publicly available resources. These efforts aim to inspire students to pursue careers in molecular and computational sciences, aligning with NSF’s mission to advance science and promote societal welfare. This CAREER project investigates the nucleation and growth of liquid-like macromolecular condensed phases using multiscale computational techniques. The research targets critical gaps in understanding nonclassical nucleation pathways, mesoscale clustering, and long-term condensate behavior. Free energy methods will map the stability landscapes of mesoscale clusters and identify metastable states, while advanced Monte Carlo simulations will quantify nucleation barriers under varying molecular compositions. Additionally, continuum modeling will bridge molecular dynamics and macroscopic behavior, characterizing growth, coalescence, and dissolution of condensates over extended timescales. Specific research aims include: (1) identifying the role of mesoscale clusters in nonclassical nucleation; (2) quantifying the effects of molecular composition on nucleation free energy; and (3) elucidating the determinants of long-timescale growth and stability using dynamical density functional theory. This work will provide mechanistic insights critical for applications in pharmaceuticals, material science, and biotechnology. Educational initiatives complement this research by broadening access to computational science through hands-on workshops, mentorship programs, and interdisciplinary courses, fostering a new generation of STEM innovators. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.