Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.

An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment by design. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-COx), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from GW and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-COx structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-COx. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach.

Surface Functionalization of 2D MXenes: Trends in Distribution, Composition, and Electronic Properties.

Using a multiscale computational scheme, we study the trends in distribution and composition of the surface functional groups -O, -OH, and -F on two-dimensional (2D) transition metal carbides and nitrides (MXenes). We consider Ti2N, Ti4N3, Nb2C, Nb4C3, Ti2C, and Ti3C2 to explore MXenes with different chemistry and different number of atomic layers. Using a combination of cluster expansion, Monte Carlo, and density functional theory methods, we study the distribution and composition of functional groups at experimentally relevant conditions. We show that mixtures of functional groups are favorable on all studied MXene surfaces. The distribution of functional groups appears to be largely independent of the type of metal, carbon, or nitrogen species and/or number of atomic layers in the MXene. We further show that some properties (e.g., the work function) strongly depend on the surface composition, while others, for example, the electric conductivity, exhibit only a weak dependence.

First principles study of the stability of MXenes under an electron beam.

Interactions between two-dimensional MXene sheets and electron beams of a (scanning) transmission electron microscope are studied by first-principles calculations. We simulated the knock-on sputtering threshold for Ti3C2 MXene sheets via ab initio molecular dynamics simulations and for five other MXenes (Ti2C, Ti2N, Nb2C, Mo2TiC2, and Ti3CN) approximately from defect formation energies. We evaluated the sputtering cross section and sputtering rates and based on those evaluated the surface composition. We find that at the exit surface and for "low" TEM energies H and F sputter at equal rates, but at "high" TEM energies the F is sputtered most strongly. In the entry surface, H sputtering dominates. The results were found to be largely similar for all studied MXenes, and although the sputtering thresholds varied between the different metal atoms the thresholds were always too high to lead to significant sputtering of the metal atoms. We simulated electron microscope images at the successive stages of sputtering and found that while it is likely difficult to identify surface groups based on the spot intensities, the local contraction of the lattice around O groups should be observable. We also studied MXenes encapsulated with graphene and found them to provide efficient protection from knock-on damage for all surface group atoms except H.

Self-organized twist-heterostructures via aligned van der Waals epitaxy and solid-state transformations.

Vertical van der Waals (vdW) heterostructures of 2D crystals with defined interlayer twist are of interest for band-structure engineering via twist moiré superlattice potentials. To date, twist-heterostructures have been realized by micromechanical stacking. Direct synthesis is hindered by the tendency toward equilibrium stacking without interlayer twist. Here, we demonstrate that growing a 2D crystal with fixed azimuthal alignment to the substrate followed by transformation of this intermediate enables a potentially scalable synthesis of twisted heterostructures. Microscopy during growth of ultrathin orthorhombic SnS on trigonal SnS2 shows that vdW epitaxy yields azimuthal order even for non-isotypic 2D crystals. Excess sulfur drives a spontaneous transformation of the few-layer SnS to SnS2, whose orientation - rotated 30° against the underlying SnS2 crystal - is defined by the SnS intermediate rather than the substrate. Preferential nucleation of additional SnS on such twisted domains repeats the process, promising the realization of complex twisted stacks by bottom-up synthesis.

pH-Dependent Distribution of Functional Groups on Titanium-Based MXenes.

MXenes are a new rapidly developing class of two-dimensional materials with suitable properties for a broad range of applications. It has been shown that during synthesis of these materials the surfaces are usually functionalized by O, OH, and F and further suggested that controlling the surface allows controlling the material properties. However, a proper understanding of the surface structure is still missing, with a significant discrepancy between computational and experimental studies. Experiments consistently show formation of surfaces with mixed terminations, whereas computational studies point toward pure terminated surfaces. Here, we explain the formation of mixed functionalization on the surface of titanium-based two-dimensional carbides, Ti2C and Ti3C2, using a multiscale modeling scheme. Our scheme is based on calculating Gibbs free energy of formation by a combination of electronic structure calculations with cluster expansion and Monte Carlo simulations. Our calculations show formation of mixtures of O, OH, and F on the surface with the composition depending on pH, temperature, and the work function. On the other hand, our results also suggest a limited stable range of compositions, which challenges the paradigm of a high tunability of MXene properties.