Driving and characterizing nucleation of urea and glycine polymorphs in water

Journal Article · Thu Feb 09 00:00:00 EST 2023 · Proceedings of the National Academy of Sciences of the United States of America

DOI:https://doi.org/10.1073/pnas.2216099120· OSTI ID:1923947

Zou, Ziyue ^[1]; Beyerle, Eric R. ^[2]; Tsai, Sun-Ting ^[3]; Tiwary, Pratyush ^[4]

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1. Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742
2. Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742
3. Department of Physics, University of Maryland, College Park, MD 20742
4. Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework “reweighted autoencoded variational Bayes for enhanced sampling (RAVE).” We study two molecular systems—urea and glycine—in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.

• Research Organization: University of Maryland, College Park, MD (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: SC0021009
• OSTI ID: 1923947
• Alternate ID(s): OSTI ID: 1991225
• Journal Information: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 7 Vol. 120; ISSN 0027-8424
• Publisher: Proceedings of the National Academy of Sciences
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Enhanced Sampling
Machine Learning
Molecular Simulations
Nucleation