Accelerated Lignocellulosic Molecule Adsorption Structure Determination.

Here, we present a study combining Bayesian optimization structural inference with the machine learning interatomic potential Neural Equivariant Interatomic Potential (NequIP) to accelerate and enable the study of the adsorption of the conformationally flexible lignocellulosic molecules ß-d-xylose and 1,4-ß-d-xylotetraose on a copper surface. The number of structure evaluations needed to map out the relevant potential energy surfaces are reduced by Bayesian optimization, while NequIP minimizes the time spent on each evaluation, ultimately resulting in cost-efficient and reliable sampling of large systems and configurational spaces. Although the applicability of Bayesian optimization for the conformational analysis of the more flexible xylotetraose molecule is restricted by the sample complexity bottleneck, the latter can be effectively bypassed with external conformer search tools, such as the Conformer-Rotamer Ensemble Sampling Tool, facilitating the subsequent lower-dimensional global minimum adsorption structure determination. Finally, we demonstrate the applicability of the described approach to find adsorption structures practically equivalent to the density functional theory counterparts at a fraction of the computational cost.

Structure Discovery in Atomic Force Microscopy Imaging of Ice.

The interaction of water with surfaces is crucially important in a wide range of natural and technological settings. In particular, at low temperatures, unveiling the atomistic structure of adsorbed water clusters would provide valuable data for understanding the ice nucleation process. Using high-resolution atomic force microscopy (AFM) and scanning tunneling microscopy, several studies have demonstrated the presence of water pentamers, hexamers, and heptamers (and of their combinations) on a variety of metallic surfaces, as well as the initial stages of 2D ice growth on an insulating surface. However, in all of these cases, the observed structures were completely flat, providing a relatively straightforward path to interpretation. Here, we present high-resolution AFM measurements of several water clusters on Au(111) and Cu(111), whose understanding presents significant challenges due to both their highly 3D configuration and their large size. For each of them, we use a combination of machine learning, atomistic modeling with neural network potentials, and statistical sampling to propose an underlying atomic structure, finally comparing its AFM simulated images to the experimental ones. These results provide insights into the early phases of ice formation, which is a ubiquitous phenomenon ranging from biology to astrophysics.

Molecular insights on the crystalline cellulose-water interfaces via three-dimensional atomic force microscopy.

Cellulose, a renewable structural biopolymer, is ubiquitous in nature and is the basic reinforcement component of the natural hierarchical structures of living plants, bacteria, and tunicates. However, a detailed picture of the crystalline cellulose surface at the molecular level is still unavailable. Here, using atomic force microscopy (AFM) and molecular dynamics (MD) simulations, we revealed the molecular details of the cellulose chain arrangements on the surfaces of individual cellulose nanocrystals (CNCs) in water. Furthermore, we visualized the three-dimensional (3D) local arrangement of water molecules near the CNC surface using 3D AFM. AFM experiments and MD simulations showed anisotropic water structuring, as determined by the surface topologies and exposed chemical moieties. These findings provide important insights into our understanding of the interfacial interactions between CNCs and water at the molecular level. This may allow the establishment of the structure-property relationship of CNCs extracted from various biomass sources.

Probing the Structural Details of Chitin Nanocrystal-Water Interfaces by Three-Dimensional Atomic Force Microscopy.

Chitin is one of the most abundant and renewable natural biopolymers. It exists in the form of crystalline microfibrils and is the basic structural building block of many biological materials. Its surface crystalline structure is yet to be reported at the molecular level. Herein, atomic force microscopy (AFM) in combination with molecular dynamics simulations reveals the molecular-scale structural details of the chitin nanocrystal (chitin NC)-water interface. High-resolution AFM images reveal the molecular details of chitin chain arrangements at the surfaces of individual chitin NCs, showing highly ordered, stable crystalline structures almost free of structural defects or disorder. 3D-AFM measurements with submolecular spatial resolution demonstrate that chitin NC surfaces interact strongly with interfacial water molecules creating stable, well-ordered hydration layers. Inhomogeneous encapsulation of the underlying chitin substrate by these hydration layers reflects the chitin NCs' multifaceted surface character with different chain arrangements and molecular packing. These findings provide important insights into chitin NC structures at the molecular level, which is critical for developing the properties of chitin-based nanomaterials. Furthermore, these results will contribute to a better understanding of the chemical and enzymatic hydrolysis of chitin and other native polysaccharides, which is also essential for the enzymatic conversion of biomass.