Deep learning of CVD growth and phase-transition pathways in layered materials

Electrical and optoelectronic properties of two-dimensional (2D) transition metal dichalcogenides (TMDCs) can be tuned by exploiting their polymorphism. Here, polymorphism in TMDCs and their growth by chemical vapor deposition (CVD) are examined using deep generative models namely, the variational autoencoder (VAE) and Restricted Boltzmann Machine (RBM), trained with molecular dynamics (MD) simulation data. The VAE correctly identifies pathways connecting the semiconducting (2H) and metallic (1T) phases via novel intermediate structures called a and bin MoWSe2 alloy. These intermediate structures are similar to those observed in a 2D MoS2 by scanning transmission electron microscopy. Structures generated solely by VAE are combined to form novel MoWSe2 devices with a and b interfaces. Quantum simulations based on density functional theory show that interfaces synthesized by VAE are stable and the devices with those interfaces are suitable for novel nanoelectronics applications. A novel deterministic RBM algorithm is used to identify 2H and 1T phases and defects in reactive MD simulations of the synthesis of a MoS2 monolayer by CVD. Applications of the deterministic RBM approach to modeling of experimental data will also be presented. * This work was supported as a part of the Computational Materials Sciences Program funded by the U.S Department of Energy, Office of Science, Basic Energy Sciences, under Award Number DE-SC0014607

Molecular Simulation of MoS2 Exfoliation.

A wide variety of two-dimensional layered materials are synthesized by liquid-phase exfoliation. Here we examine exfoliation of MoS2 into nanosheets in a mixture of water and isopropanol (IPA) containing cavitation bubbles. Using force fields optimized with experimental data on interfacial energies between MoS2 and the solvent, multimillion-atom molecular dynamics simulations are performed in conjunction with experiments to examine shock-induced collapse of cavitation bubbles and the resulting exfoliation of MoS2. The collapse of cavitation bubbles generates high-speed nanojets and shock waves in the solvent. Large shear stresses due to the nanojet impact on MoS2 surfaces initiate exfoliation, and shock waves reflected from MoS2 surfaces enhance exfoliation. Structural correlations in the solvent indicate that shock induces an ice VII like motif in the first solvation shell of water

Dynamic Transition in the Structure of an Energetic Crystal during Chemical Reactions at Shock Front Prior to Detonation

Mechanical stimuli in energetic materials initiate chemical reactions at shock fronts prior to detonation. Shock sensitivity measurements provide widely varying results, and quantum-mechanical calculations are unable to handle systems large enough to describe shock structure. Recent developments in reactive force-field molecular dynamics (ReaxFF-MD) combined with advances in parallel computing have paved the way to accurately simulate reaction pathways along with the structure of shock fronts. Our multimillion-atom ReaxFF-MD simulations of l,3,5-trinitro-l,3,5-triazine (RDX) reveal that detonation is preceded by a transition from a diffuse shock front with well-ordered molecular dipoles behind it to a disordered dipole distribution behind a sharp front

An extended-Lagrangian scheme for charge equilibration in reactive molecular dynamics simulations

a b s t r a c t Reactive molecular dynamics (RMD) simulations describe chemical reactions at orders-of-magnitude faster computing speed compared with quantum molecular dynamics (QMD) simulations. A major computational bottleneck of RMD is charge-equilibration (QEq) calculation to describe charge transfer between atoms. Here, we eliminate the speed-limiting iterative minimization of the Coulombic energy in QEq calculation by adapting an extended-Lagrangian scheme that was recently proposed in the context of QMD simulations, Souvatzis and Niklasson (2014). The resulting XRMD simulation code drastically improves energy conservation compared with our previous RMD code, , while substantially reducing the time-to-solution. The XRMD code has been implemented on parallel computers based on spatial decomposition, achieving a weak-scaling parallel efficiency of 0.977 on 786,432 IBM Blue Gene/Q cores for a 67.6 billion-atom system