Gas–liquid interfacial nucleation can influence organic crystallization, yet its mechanistic role in polymorph selection remains poorly understood. Here, we demonstrate that nucleation at the gas–liquid interface of flufenamic acid solutions governs polymorph selection and gives rise to a pronounced concentration-dependent polymorphism, wherein low initial concentrations favor the nucleation of form I, higher concentrations yield form III, and intermediate concentrations selectively produce the metastable form IV. In situ synchrotron-based grazing-incidence wide-angle X-ray scattering (GIWAXS) directly resolves the formation and evolution of prenucleation assemblies at the interface, revealing three distinct interfacial molecular evolution pathways correlated with the emergence of specific polymorphs. Molecular dynamics (MD) simulations combined with energy calculations within the hybrid quantum mechanics/molecular mechanics (QM/MM) embedded-cluster framework further reveal interfacial enrichment, orientational bias, and conformer-dependent stabilization of stacking motifs, providing a microscopic interpretation of the experimentally observed selectivity. Together, these results establish the gas–liquid interface as an active structural selector that reshapes molecular organization prior to nucleation, offering a mechanistic framework for understanding and controlling polymorphism in evaporation-driven crystallization.
Yu Liu, Xu Zhang, Xingfan Zhang et al.· Journal of the American Chem...· 0 citations
The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.
Bruno Camino, C. R. A. Catlow, J. Buckeridge et al.· 0 citations