The remarkable mechanical stability of wood is primarily attributed to the hierarchical fibrous arrangement of the polymeric components. While the mechanisms by which fibrous cell structure and cellulose microfibril arrangements lend stiffness and strength to wood have been intensively studied, the structural origins of the relatively high splitting fracture toughness remain unclear. This study relates cellulose microfibril arrangements to splitting fracture toughness in pine wood cell walls using in situ electron microscopy and reveals a previously unknown toughening mechanism: the specific arrangement of cellulose microfibrils in the cell wall deflects cracks from the S2 layer to the S1/S2 interface, and, once there, causes the crack to be repetitively arrested and shunted along the interface in a zig‐zag path. It is suggested that this natural adaptation of wood to achieve tough interfaces and then deflect and trap cracks at them can be generalized to provide design guidelines to improve toughness of high‐performance and renewable engineering materials.

Many positive electrode materials in lithium ion batteries include transition metals, which are difficult to describe by electronic structure methods like density functional theory (DFT) due to the presence of multiple oxidation states. A prominent example is the lithium manganese oxide spinel ${\mathrm{Li}}_x{\mathrm{Mn}}_2{\mathrm{O}}_4$ with $0\le x\le 2$. While DFT, employing the local hybrid functional PBE0r, provides a reliable description, the need for extended computer simulations of large structural models remains a significant challenge. Here, we close this gap by constructing a DFT-based high-dimensional neural network potential (HDNNP) providing accurate energies and forces at a fraction of the computational costs. As different oxidation states and the resulting Jahn-Teller distortions represent a new level of complexity for HDNNPs, the potential is carefully validated by performing x-ray diffraction experiments. We demonstrate that the HDNNP provides atomic level details and is able to predict a series of properties like the lattice parameters and expansion with increasing Li content or temperature, the orthorhombic to cubic transition, the lithium diffusion barrier, and the phonon frequencies. We show that for understanding these properties access to large time and length scales as enabled by the HDNNP is essential to close the gap between theory and experiment.

A statistical analysis of the rate-dependent pop-in behavior during spherical nanoindentation of a metallic glass is presented. Since the first pop-in of a test corresponds to the initiation of plasticity, this method provides insights into the fundamentals of shear-banding behavior. In this study, test series with different applied loading rates reveal that the initiation of a shear band is thermally activated with a rate- and stress-dependent activation volume. We further find a spatial inhomogeneity of the elastic modulus at the micrometer scale. This long-range structural heterogeneity does not strongly correlate with the shear-band initiation stress.