LAN: A Materials Notation for Two-Dimensional Layered Assemblies.

Two-dimensional (2D) layered materials offer intriguing possibilities for novel physics and applications. Before any attempt at exploring the materials space in a systematic fashion, or combining insights from theory, computation, and experiment, a formal description of information about an assembly of arbitrary composition is required. Here, we introduce a domain-generic notation that is used to describe the space of 2D layered materials from monolayers to twisted assemblies of arbitrary composition, existent or not yet fabricated. The notation corresponds to a theoretical materials concept of stepwise assembly of layered structures using a sequence of rotation, vertical stacking, and other operations on individual 2D layers. Its scope is demonstrated with a number of example structures using common single-layer materials as building blocks. This work overall aims to contribute to the systematic codification, capture, and transfer of materials knowledge in the area of 2D layered materials.

Machine Learning Prediction of H Adsorption Energies on Ag Alloys.

Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.

Stress effects on the initial lithiation of crystalline silicon nanowires: reactive molecular dynamics simulations using ReaxFF.

Silicon (Si) has been recognized as a promising anode material for the next-generation high-capacity lithium (Li)-ion batteries because of its high theoretical energy density. Recent in situ transmission electron microscopy (TEM) revealed that the electrochemical lithiation of crystalline Si nanowires (c-SiNWs) proceeds by the migration of the interface between the lithiated Si (LixSi) shell and the pristine unlithiated core, accompanied by solid-state amorphization. The underlying atomic mechanisms of Li insertion into c-Si remain poorly understood. Herein, we perform molecular dynamics (MD) simulations using the reactive force field (ReaxFF) to characterize the lithiation process of c-SiNWs. Our calculations show that ReaxFF can accurately reproduce the energy barriers of Li migration from DFT calculations in both crystalline (c-Si) and amorphous Si (a-Si). The ReaxFF-based MD simulations reveal that Li insertion into interlayer spacing between two adjacent (111) planes results in the peeling-off of the (111) facets and subsequent amorphization, in agreement with experimental observations. We find that breaking of the Si-Si bonds between (111)-bilayers requires a rather high local Li concentration, which explains the atomically sharp amorphous-crystalline interface (ACI). Our stress analysis shows that lithiation induces compressive stress at the ACI layer, causing retardation or even the stagnation of the reaction front, also in good agreement with TEM observations. Lithiation at high temperatures (e.g. 1200 K) shows that Li insertion into c-SiNW results in an amorphous to crystalline phase transformation at Li : Si composition of ∼4.2 : 1. Our modeling results provide a comprehensive picture of the effects of reaction and diffusion-induced stress on the interfacial dynamics and mechanical degradation of SiNW anodes under chemo-mechanical lithiation.

Adsorption and diffusion of lithium on layered silicon for Li-ion storage.

The energy density of Li-ion batteries depends critically on the specific charge capacity of the constituent electrodes. Silicene, the silicon analogue to graphene, being of atomic thickness could serve as high-capacity host of Li in Li-ion secondary batteries. In this work, we employ first-principles calculations to investigate the interaction of Li with Si in model electrodes of free-standing single-layer and double-layer silicene. More specifically, we identify strong binding sites for Li, calculate the energy barriers accompanying Li diffusion, and present our findings in the context of previous theoretical work related to Li-ion storage in other structural forms of silicon: the bulk and nanowires. The binding energy of Li is ~2.2 eV per Li atom and shows small variation with respect to Li content and silicene thickness (one or two layers) while the barriers for Li diffusion are relatively low, typically less than 0.6 eV. We use our theoretical findings to assess the suitability of two-dimensional silicon in the form of silicene layers for Li-ion storage.

Reactive flow in silicon electrodes assisted by the insertion of lithium.

In the search for high-energy density materials for Li-ion batteries, silicon has emerged as a promising candidate for anodes due to its ability to absorb a large number of Li atoms. Lithiation of Si leads to large deformation and concurrent changes in its mechanical properties, from a brittle material in its pure form to a material that can sustain large inelastic deformation in the lithiated form. These remarkable changes in behavior pose a challenge to theoretical treatment of the material properties. Here, we provide a detailed picture of the origin of changes in the mechanical properties, based on first-principles calculations of the atomic-scale structural and electronic properties in a model amorphous silicon (a-Si) structure. We regard the reactive flow of lithiated silicon as a nonequilibrium process consisting of concurrent Li insertion driven by unbalanced chemical potential and flow driven by deviatoric stress. The reaction enables the material to flow at a lower level of stress. Our theoretical model is in excellent quantitative agreement with experimental measurements of lithiation-induced stress on a Si thin film.

Data-Driven Studies of the Magnetic Anisotropy of Two-Dimensional Magnetic Materials.

A key issue in layered materials is the dependence of their properties on their chemical composition and crystal structure in addition to the dimensionality. For instance, atomically thin magnetic structures exhibit novel spin properties that do not exist in the bulk. We use first-principles calculations, based on density functional theory, and machine learning to study the magnetocrystalline anisotropy of a set of single-layer two-dimensional structures that are derived from changing the chemical composition of the ferromagnetic semiconductor Cr2Ge2Te6. We discuss trends and identify descriptors for the magnetocrystalline anisotropy in monolayers with the chemical formula A2B2X6. Our data-driven study aims to provide physical insights into the microscopic origins of magnetic anisotropy in two dimensions. For instance, we demonstrate that hybridization plays a key role in determining the magnetic anisotropy of the materials investigated in this study. In addition, we demonstrate that first-principles calculations can be combined with machine learning to create a high-throughput computational approach for the targeted design of quantum materials with potential applications in areas ranging from sensing to data storage.

Two-dimensional ordered porous patterns by molecular design.

We demonstrate by computer experiments that the spontaneous formation of two-dimensional regularly patterned molecular networks containing voids may be an entirely entropy-driven process. On the basis of a simple model of core-(soft) shell half-disk-shaped particles, we show that, even without the mediation of any attractive interparticle forces, such particles self-organize to stable and macroscopically ordered patterns with regularly distributed voids. The morphology of these supramolecular porous motifs depends critically on the size of the core relative to the coronal halo. The reverse engineering analysis of these precise two-dimensional supramolecular porous templates suggests molecular-shape complementarity and polyphilicity as key design parameters for the bottom-up engineering of such functional substrates.

Anatomy of the Photochemical Reaction: Excited-State Dynamics Reveals the C-H Acidity Mechanism of Methoxy Photo-oxidation on Titania.

Light-driven chemical reactions on semiconductor surfaces have potential for addressing energy and pollution needs through efficient chemical synthesis; however, little is known about the time evolution of excited states that determine reaction pathways. Here, we study the photo-oxidation of methoxy (CH3O) derived from methanol on the rutile TiO2(110) surface using ab initio simulations to create a molecular movie of the process. The movie sequence reveals a wealth of information on the reaction intermediates, time scales, and energetics. The reaction is broken in three stages, described by Lewis structures directly derived from the "hole" wave functions that lead to the concept of "photoinduced C-H acidity". The insights gained from this work can be generalized to a set of simple rules that can predict the efficiency of photo-oxidation reactions in reactant-catalyst pairs.