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1. Exploiting kinetics for assembly of multicomponent nanoparticle networks with programmable control of heterogeneity

   https://doi.org/10.1039/c9cc09027d  

   Davis, Jessica L.; Silva, Karunamuni L.; Brock, Stephanie L. (January 2020, Chemical Communications)

   Kinetic control of metal chalcogenide nanoparticle oxidative assembly is realized by varying the redox potential of the chalcogenide, structure (wurtzite vs. zinc blende), and ligand chain length. This knowledge is exploited to form two-component (ZnS + CdSe) hybrid aerogels with minimal heterobonding (phase-segregated) or maximal heterobonding (intimately mixed). 

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2. Influence of excess silicon on polytype selection during metal-mediated epitaxy of GaN nanowires

   https://doi.org/10.1063/5.0210669  

   Liu, A; Xi, Z; Li, M; Yang, J C; Qi, L; Goldman, R S (July 2024, Applied Physics Letters)

   We have examined the origins of polytype selection during metal-mediated molecular-beam epitaxy of GaN nanowires (NWs). High-angle annular dark-field scanning transmission electron microscopy reveals [111]-oriented zinc blende (ZB) NWs and [0001]-oriented wurtzite (WZ) NWs, with SixNy at the interface between individual NWs and the Si (001) substrate. Quantitative energy dispersive x-ray spectroscopy reveals a notably higher Si concentration of 7.0% ± 2.3% in zinc blende (ZB) NWs than 2.3% ± 1.2% in wurtzite (WZ) NWs. Meanwhile, density functional theory calculations show that incorporation of 8 at. % Si on the Ga sublattice inverts the difference in formation energies between WZ and ZB GaN, such that the ZB polytype of GaN is stabilized. This identification of Si and other ZB polytype stabilizers will enable the development of polytype heterostructures in a wide variety of WZ-preferring compounds. 

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3. Nanoscale compositional segregation in epitaxial AlScN on Si (111)

   https://doi.org/10.1039/d2nh00567k  

   Zhang, Xiaoman; Stach, Eric A.; Meng, W. J.; Meng, Andrew C. (March 2023, Nanoscale Horizons)

   We report the growth of epitaxial wurtzite AlScN thin films on Si (111) substrates with a wide range of Sc concentrations using ultra-high vacuum reactive sputtering. Sc alloying in AlN enhances piezoelectricity and induces ferroelectricity, and epitaxial thin films facilitate systematic structure-based investigations of this important and emerging class of materials. Two main effects are observed as a function of increasing Sc concentration. First, increasing crystalline disorder is observed together with a structural transition from wurtzite to rocksalt at ∼30 at% Sc. Second, nanoscale compositional segregation consistent with spinodal decomposition occurs at intermediate compositions, before the wurtzite to rocksalt phase boundary is reached. Lamellar features arising from composition fluctuations are correlated with polarization domains in AlScN, suggesting that composition segregation can influence ferroelectric properties. The present results provide a route to the creation of single crystal AlScN films on Si (111), as well as a means for self-assembled composition modulation. 

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4. A de novo protein catalyzes the synthesis of semiconductor quantum dots

   https://doi.org/10.1073/pnas.2204050119  

   Spangler, Leah C.; Yao, Yueyu; Cheng, Guangming; Yao, Nan; Chari, Sarangan L.; Scholes, Gregory D.; Hecht, Michael H. (December 2022, Proceedings of the National Academy of Sciences)

   De novo proteins constructed from novel amino acid sequences are distinct from proteins that evolved in nature. Construct K (ConK) is a binary-patterned de novo designed protein that rescues Escherichia coli from otherwise toxic concentrations of copper. ConK was recently found to bind the cofactor PLP (pyridoxal phosphate, the active form of vitamin B 6 ). Here, we show that ConK catalyzes the desulfurization of cysteine to H 2 S, which can be used to synthesize CdS nanocrystals in solution. The CdS nanocrystals are approximately 3 nm, as measured by transmission electron microscope, with optical properties similar to those seen in chemically synthesized quantum dots. The CdS nanocrystals synthesized using ConK have slower growth rates and a different growth mechanism than those synthesized using natural biomineralization pathways. The slower growth rate yields CdS nanocrystals with two desirable properties not observed during biomineralization using natural proteins. First, CdS nanocrystals are predominantly of the zinc blende crystal phase; this is in stark contrast to natural biomineralization routes that produce a mixture of zinc blende and wurtzite phase CdS. Second, in contrast to the growth and eventual precipitation observed in natural biomineralization systems, the CdS nanocrystals produced by ConK stabilize at a final size. Future optimization of CdS nanocrystal growth using ConK—or other de novo proteins—may help to overcome the limits on nanocrystal quality typically observed from natural biomineralization by enabling the synthesis of more stable, high-quality quantum dots at room temperature. 

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5. Efficient exploration of reaction pathways using reaction databases and active learning

   https://doi.org/10.1063/5.0235715  

   Kuryla, Domantas; Csányi, Gábor; van_Duin, Adri_C T; Michaelides, Angelos (March 2025, The Journal of Chemical Physics)

   Lian, T (Ed.)

   The fast and accurate simulation of chemical reactions is a major goal of computational chemistry. Recently, the pursuit of this goal has been aided by machine learning interatomic potentials (MLIPs), which provide energies and forces at quantum mechanical accuracy but at a fraction of the cost of the reference quantum mechanical calculations. Assembling the training set of relevant configurations is key to building the MLIP. Here, we demonstrate two approaches to training reactive MLIPs based on reaction pathway information. One approach exploits reaction datasets containing reactant, product, and transition state structures. Using an SN2 reaction dataset, we accurately locate reaction pathways and transition state geometries of up to 170 unseen reactions. In another approach, which does not depend on data availability, we present an efficient active learning procedure that yields an accurate MLIP and converged minimum energy path given only the reaction end point structures, avoiding quantum mechanics driven reaction pathway search at any stage of training set construction. We demonstrate this procedure on an SN2 reaction in the gas phase and with a small number of solvating water molecules, predicting reaction barriers within 20 meV of the reference quantum chemistry method. We then apply the active learning procedure on a more complex reaction involving a nucleophilic aromatic substitution and proton transfer, comparing the results against the reactive ReaxFF force field. Our active learning procedure, in addition to rapidly finding reaction paths for individual reactions, provides an approach to building large reaction path databases for training transferable reactive machine learning potentials. 

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