Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

Transition metal dichalcogenides (TMDC) like MoS are promising candidates for next-generation electric and optoelectronic devices. These TMDC monolayers are typically synthesized by chemical vapor deposition (CVD). However, despite significant amount of empirical work on this CVD growth of monolayered crystals, neither experiment nor theory has been able to decipher mechanisms of selection rules for different growth scenarios, or make predictions of optimized environmental parameters and growth factors. Here, we present an atomic-scale mechanistic analysis of the initial sulfidation process on MoO surfaces using first-principles-informed ReaxFF reactive molecular dynamics (RMD) simulations. We identify a three-step reaction process associated with synthesis of the MoS samples from MoO and S precursors: O evolution and self-reduction of the MoO surface; SO/SO formation and S-assisted reduction; and sulfidation of the reduced surface and Mo-S bond formation. These atomic processes occurring during early stage MoS synthesis, which are consistent with experimental observations and existing theoretical literature, provide valuable input for guided rational synthesis of MoS and other TMDC crystals by the CVD process.

A paradigm-shifting design strategy is demonstrated that unifies the treatment of electronic and conformational properties of polymer dielectrics for concurrent high electric field and elevated temperature harsh conditions.

Fission of a spin-singlet exciton into two triplet excitons, if realized in disordered organic solid, could revolutionize low-cost fabrication of efficient solar cells. Here, a divide-conquer-recombine approach involving nonadiabatic quantum molecular dynamics and kinetic Monte Carlo simulations identifies the key molecular geometry and exciton-flow-network topology for singlet-fission “hot spots” in amorphous diphenyl tetracene, where fission occurs preferentially. The simulation reveals the molecular origin of experimentally observed two time scales in exciton population dynamics and may pave a way to nanostructural design of efficient solar cells from first principles.

Atomically thin MoS2 layer, a promising transition metal dichalcogenide (TMDC) material, has great potential for application in next-generation electronic and optoelectronic devices. Chemical vapor deposition (CVD) is the most effective technique for the synthesis of high-quality MoS2 layers. During CVD synthesis, monolayered MoS2 is generally synthesized by sulfidation of MoO3. Although qualitative reaction mechanisms for the sulfidation of MoO3 have been investigated by previous studies, the detailed reaction processes, including atomic-scale reaction pathways and growth kinetics, have yet to be fully understood. Here, we present quantum-mechanically informed and validated reactive molecular dynamics simulations of the direct sulfidation of MoO3 surfaces using S2 gas precursors. Our work clarifies the reaction mechanisms and kinetics of the sulfidation of MoO3 surfaces as follows: the reduction and sulfidation of MoO3 surfaces occur primarily at O-termination sites, followed by unsaturated Mo sites; these local reaction processes lead to nonuniform MoO x S y surface structures during the CVD process. After annealing the MoO x S y samples, the crystallized surface structures contain voids, and three different types of local surface complexes (MoO x , MoO x S y , and MoS2-like surface regions), depending on the fraction of S ingredients on the MoO x S y surface. These results, which have been validated by our reactive quantum molecular dynamics simulations and previous experimental results, provide valuable chemical insights into the CVD synthesis of large-scale and defect-free MoS2 layers and other layered TMDC materials.

We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 10(6)-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of techniques are employed for efficiently calculating the long-range exact exchange correction and excited-state forces. The NAQMD trajectories are analyzed to extract the rates of various excitonic processes, which are then used in KMC simulation to study the dynamics of the global exciton flow network. This has allowed the study of large-scale photoexcitation dynamics in 6400-atom amorphous molecular solid, reaching the experimental time scales.

Two-dimensional (2D) materials exhibit different mechanical properties from their bulk counterparts owing to their monolayer atomic thickness. Here, we have examined the mechanical behavior of 2D molybdenum tungsten diselenide (MoWSe) precipitation alloy grown using chemical vapor deposition and composed of numerous nanoscopic MoSe and WSe regions. Applying a bending strain blue-shifted the MoSe and WSe A Raman modes with the stress concentrated near the precipitate interfaces predominantly affecting the WSe modes. In situ local Raman measurements suggested that the crack propagated primarily thorough MoSe-rich regions in the monolayer alloy. Molecular dynamics (MD) simulations were performed to study crack propagation in an MoSe monolayer containing nanoscopic WSe regions akin to the experiment. Raman spectra calculated from MD trajectories of crack propagation confirmed the emergence of intermediate peaks in the strained monolayer alloy, mirroring experimental results. The simulations revealed that the stress buildup around the crack tip caused an irreversible structural transformation from the 2H to 1T phase both in the MoSe matrix and WSe patches. This was corroborated by high-angle annular dark-field images. Crack branching and subsequent healing of a crack branch were also observed in WSe, indicating the increased toughness and crack propagation resistance of the alloyed 2D MoWSe over the unalloyed counterparts.

The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes. ReaxFF parameters are commonly trained to fit a predefined set of quantummechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions. Here, we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest. ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly, where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification. Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications. The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO 3 by H 2 S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS 2 layers. Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes, which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.