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Funding Information: The authors gratefully acknowledge support from the Aalto University Internal Seed Fund, the Academy of Finland through project nos. 316601 and 341589, the FinnCERES BioEconomy flagship, and the Finnish Center for Artificial Intelligence (FCAI). The data and code that were used in this study are freely available online. Publisher Copyright: © 2022 American Chemical Society. All rights reserved. Novel biorefineries could transform lignin, an abundant biopolymer, from side-stream waste to high-value-Added byproducts at their site of production and with minimal experiments. Here, we report the optimization of the AquaSolv omni biorefinery for lignin using Bayesian optimization, a machine learning framework for sample-efficient and guided data collection. This tool allows us to relate the biorefinery conditions like hydrothermal pretreatment reaction severity and temperature with multiple experimental outputs, such as lignin structural features characterized using 2D nuclear magnetic resonance spectroscopy. By applying a Pareto front analysis to our models, we can find the processing conditions that simultaneously optimize the lignin yield and the amount of β-O-4 linkages for the depolymerization of lignin into platform chemicals. Our study demonstrates the potential of machine learning to accelerate the development of sustainable chemical processing techniques for targeted applications and products.

Controlled deposition of colloidal nanoparticles using self-assembly is a promising technique for, for example, manufacturing of miniaturized electronics, and it bridges the gap between top-down and bottom-up methods. However, selecting materials and geometry of the target surface for optimal deposition results presents a significant challenge. Here, we describe a predictive framework based on the Derjaguin–Landau–Verwey–Overbeek theory that allows rational design of colloidal nanoparticle deposition setups. The framework is demonstrated for a model system consisting of gold nanoparticles stabilized by trisodium citrate that are directed toward prefabricated sub-100 nm features on a silicon substrate. Experimental results for the model system are presented in conjunction with theoretical analysis to assess its reliability. It is shown that three-dimensional, nickel-coated structures are well suited for attracting gold nanoparticles and that optimization of the feature geometry based on the proposed framework leads to a systematic improvement in the number of successfully deposited particles.

We report a comprehensive study of aqueous halide adsorption on nanoparticles of gold and palladium that addresses several limitations hampering the use of atomistic modeling as a tool for understanding and improving wet-chemical synthesis and related applications. A combination of thermodynamic modeling with density functional theory (DFT) calculations and experimental data is used to predict equilibrium shapes of halide-covered nanoparticles as a function of the chemical environment. To ensure realistic and experimentally relevant results, we account for solvent effects and include a large set of vicinal surfaces, several adsorbate coverages as well as decahedral particles. While the observed stabilization is not significant enough to result in thermodynamic stability of anisotropic shapes such as nanocubes, non-uniformity in the halide coverage indicates the possibility of obtaining such shapes as kinetic products. With regard to technical challenges, we show that inclusion of surface-solvent interactions lead to qualitative changes in the predicted shape. Furthermore, accounting for non-local interactions on the functional level yields a more accurate description of surface systems., 10 pages, 7 figures, 1 table

Alkanethiolate monolayers on gold are important both for applications in nanoscience as well as fundamental studies of adsorption and self-assembly at metal surfaces. While considerable experimental effort has been put into understanding the phase diagram of these systems, theoretical work based on density functional theory (DFT) has long been hampered by the inability of conventional exchange-correlation functionals to describe dispersive interactions. In this work, we combine dispersion-corrected DFT calculations using the new vdW-DF-CX functional with the ab initio thermodynamics method to study the stability of dense standing-up and low-coverage lying-down phases on Au(111). We demonstrate that the lying-down phase has a thermodynamic region of stability starting from thiolates with alkyl chains consisting of n ? 3 methylene units. This phase emerges as a consequence of a competition between dispersive chain-chain and chain-substrate interactions, where the strength of the latter varies more strongly with n. A phase diagram is derived under ultrahigh-vacuum conditions, detailing the phase transition temperatures of the system as a function of the chain length. The present work illustrates that accurate ab initio modeling of dispersive interactions is both feasible and essential for describing self-assembled monolayers.

While it is well established that elevated temperatures can induce surface roughening of metal surfaces, the effect of a high electric field on the atomic structure at ambient temperature has not been investigated in detail. Here we show with atomic resolution using in situ transmission electron microscopy how intense electric fields induce reversible switching between perfect crystalline and disordered phases of gold surfaces at room temperature. Ab initio molecular dynamics simulations reveal that the mechanism behind the structural change can be attributed to a vanishing energy cost in forming surface defects in high electric fields. Our results demonstrate how surface processes can be directly controlled at the atomic scale by an externally applied electric field, which promotes an effective decoupling of the topmost surface layers from the underlying bulk. This opens up opportunities for development of active nanodevices in e.g. nanophotonics and field-effect transistor technology as well as fundamental research in materials characterization and of yet unexplored dynamically-controlled low-dimensional phases of matter., Comment: The manuscript is 10 pages long and the supplemental material is 15 pages. There are six supplemental movies that can be provided upon request to LDK

We present libvdwxc, a general library for evaluating the energy and potential for the family of vdW-DF exchange--correlation functionals. libvdwxc provides an efficient implementation of the vdW-DF method and can be interfaced with various general-purpose DFT codes. Currently, the GPAW and Octopus codes implement interfaces to libvdwxc. The present implementation emphasizes scalability and parallel performance, and thereby enables \textit{ab initio} calculations of nanometer-scale complexes. The numerical accuracy is benchmarked on the S22 test set whereas parallel performance is benchmarked on ligand-protected gold nanoparticles ($\text{Au}_{144}(\text{SC}_{11}\text{NH}_{25})_{60}$) up to 9696 atoms., Comment: 19 pages, 3 figures