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Institute: search.filters.UBSInstitut.Institut für MaterialwissenschaftInstitute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungInstitute: search.filters.UBSInstitut.Institut für Theoretische Chemie

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    ItemOpen Access
    3D sub-nanometer analysis of glucose in an aqueous solution by cryo-atom probe tomography
    (2021) Schwarz, T. M.; Dietrich, C. A.; Ott, J.; Weikum, E. M.; Lawitzki, R.; Solodenko, H.; Hadjixenophontos, E.; Gault, B.; Kästner, J.; Schmitz, G.; Stender, P.
    Atom Probe Tomography (APT) is currently a well-established technique to analyse the composition of solid materials including metals, semiconductors and ceramics with up to near-atomic resolution. Using an aqueous glucose solution, we now extended the technique to frozen solutions. While the mass signals of the common glucose fragments CxHy and CxOyHz overlap with (H2O)nH from water, we achieved stoichiometrically correct values via signal deconvolution. Density functional theory (DFT) calculations were performed to investigate the stability of the detected pyranose fragments. This paper demonstrates APT’s capabilities to achieve sub-nanometre resolution in tracing whole glucose molecules in a frozen solution by using cryogenic workflows. We use a solution of defined concentration to investigate the chemical resolution capabilities as a step toward the measurement of biological molecules. Due to the evaporation of nearly intact glucose molecules, their position within the measured 3D volume of the solution can be determined with sub-nanometre resolution. Our analyses take analytical techniques to a new level, since chemical characterization methods for cryogenically-frozen solutions or biological materials are limited.
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    ItemOpen Access
    Performance of two complementary machine-learned potentials in modelling chemically complex systems
    (2023) Gubaev, Konstantin; Zaverkin, Viktor; Srinivasan, Prashanth; Duff, Andrew Ian; Kästner, Johannes; Grabowski, Blazej
    Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space. The complexity however makes interatomic potential development challenging. We explore two complementary machine-learned potentials - the moment tensor potential (MTP) and the Gaussian moment neural network (GM-NN) - in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family. Both models are equally accurate with excellent performance evaluated against density-functional-theory. They achieve root-mean-square-errors (RMSEs) in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training. Even for compositions not in training, relative energy RMSEs at high temperatures are within a few meV/atom. High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Å for the disordered quaternary included in, and ternaries not part of training. MTPs achieve faster convergence with training size; GM-NNs are faster in execution. Active learning is partially beneficial and should be complemented with conventional human-based training set generation.