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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.

A remarkable property of certain covalent glasses and their melts is intermediate range order, manifested as the first sharp diffraction peak (FSDP) in neutron-scattering experiments, as was exhaustively investigated by Price, Saboungi, and collaborators. Atomistic simulations thus far have relied on either quantum molecular dynamics (QMD), with systems too small to resolve FSDP, or classical molecular dynamics, without quantum-mechanical accuracy. We investigate prototypical FSDP in GeSe2 glass and melt using neural-network quantum molecular dynamics (NNQMD) based on machine learning, which allows large simulation sizes with validated quantum mechanical accuracy to make quantitative comparisons with neutron data. The system-size dependence of the FSDP height is determined by comparing QMD and NNQMD simulations with experimental data. Partial pair distribution functions, bond-angle distributions, partial and neutron structure factors, and ring-size distributions are presented. Calculated FSDP heights agree quantitatively with neutron scattering data for GeSe2 glass at 10 K and melt at 1100 K.

Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP- optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures.

Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales where these topologies emerge, we have performed multiscale excited-state neural network quantum molecular dynamics simulations that integrate quantum-mechanical description of electronic excitation and billion-atom machine learning molecular dynamics to describe ultrafast polarization control in an archetypal ferroelectric oxide, lead titanate. Far-from-equilibrium quantum simulations reveal a marked photo-induced change in the electronic energy landscape and resulting cross-over from ferroelectric to octahedral tilting topological dynamics within picoseconds. The coupling and frustration of these dynamics, in turn, create topological defects in the form of polar strings. The demonstrated nexus of multiscale quantum simulation and machine learning will boost not only the emerging field of ferroelectric topotronics but also broader optoelectronic applications.

Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS2. The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

Mechanical behavior of 2D materials such as MoS2 can be tuned by the ancient art of kirigami. Experiments and atomistic simulations show that 2D materials can be stretched more than 50% by strategic insertion of cuts. However, designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS2 kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS2 kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.

Dynamic fracture of a two-dimensional MoWSe2 membrane is studied with molecular dynamics (MD) simulation. The system consists of a random distribution of WSe2 patches in a pre-cracked matrix of MoSe2. Under strain, the system shows toughening due to crack branching, crack closure and strain-induced structural phase transformation from 2H to 1T crystal structures. Different structures generated during MD simulation are analyzed using a three-layer, feed-forward neural network (NN) model. A training data set of 36,000 atoms is created where each atom is represented by a 50-dimension feature vector consisting of radial and angular symmetry functions. Hyper parameters of the symmetry functions and network architecture are tuned to minimize model complexity with high predictive power using feature learning, which shows an increase in model accuracy from 67% to 95%. The NN model classifies each atom in one of the six phases which are either as transition metal or chalcogen atoms in 2H phase, 1T phase and defects. Further t-SNE analyses of learned representation of these phases in the hidden layers of the NN model show that separation of all phases become clearer in the third layer than in layers 1 and 2.

A remarkable property of certain covalent glasses and their melts is intermediate range order, manifested as the first sharp diffraction peak (FSDP) in neutron-scattering experiments, as was exhaustively investigated by Price, Saboungi, and collaborators. Atomistic simulations thus far have relied on either quantum molecular dynamics (QMD), with systems too small to resolve FSDP, or classical molecular dynamics, without quantum-mechanical accuracy. We investigate prototypical FSDP in GeSe

Deep learning is revolutionizing countless scientific and engineering fields. In particular, SC20 Gordon Bell award represented a breakthrough in molecular simulation, i.e., 100-million-atom simulation with quantum-mechanical accuracy on the Summit supercomputer at ORNL, using deep potential molecular dynamics (MD). Moving forward, while these simulations were performed only in gentle equilibrium conditions, far-from-equilibrium MD simulation involving light-induced electronic excited states finds numerous scientific and engineering applications. However, it remains a challenge to perform such far-from-equilibrium simulations at larger spatiotemporal scales, where growing number of unphysical predictions of interatomic force prohibits simulations involving larger numbers of atoms for longer times. In this paper, we propose a physically-based inductive bias, maximally-preserved Maxwell-Boltzmann (MPMB), to overcome this fidelity-scaling problem. Along with hybrid divide-and-conquer parallelization and single-node level optimization using multithreading and data parallel SIMD, the resulting Ex-NNQMD (extreme-scale neural network quantum molecular dynamics) algorithm has achieved unprecedented scales of far-from-equilibrium simulations: 1) 5.1-billion atom system with a parallel efficiency of 0.94, and 2) a sustained performance of 6.4 nanoseconds/day for 10-million atom system both on 262,144 cores of the Theta supercomputer at Argonne Leadership Computing Facility. Extended fidelity scaling and efficient parallelization have allowed us for the first time to study light-induced ferroelectric switching under extreme electronic excitation at experimentally relevant spatiotemporal scales with accuracy.

The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.

Informatics-driven computational design of advanced dielectric polymers (i.e., dielectric polymer genome) has remained a challenge. We have developed a computational framework for (i) high-throughput computational synthesis of polymer structures, (ii) evaluation of their dielectric properties using reactive molecular dynamics (RMD) simulations based on a new valence-aware polarizable reactive force field (ReaxPQ-v), and (iii) learning polymer structure–property relationships using machine-learning (ML) models. The resulting large size of simulated training dataset provides an unprecedented opportunity to uncover hitherto-unknown structure–property relationships purely computationally, thereby predicting new polymers with desired dielectric properties. Employing a large dataset of structurally diverse 1276 polymers, multilayer perceptron (MLP) and random forest models achieved good accuracy for predicting the dielectric constants of these polymers, while a recurrent neural network (RNN) model is being developed. Such ML prediction models are indispensable for further enlarging the search space for superior dielectric polymers by orders-of-magnitude.

Neural network molecular dynamics (NNMD) simulations could revolutionize atomistic modeling of materials with quantum-mechanical accuracy at a fraction of computational cost. However, popular NNMD frameworks are generally implemented for a single computing node, and conventional energy-based NN models still suffer from large time-to-solution (T2S), prohibiting the application of NNMD to challenging materials simulations encompassing large spatiotemporal scales. Consequently, no leadership-scale NNMD simulation has thus far been reported. Here, we present a scalable parallel NNMD software (RXMD-NN) based on our scalable reactive molecular dynamics simulation engine named RXMD. RXMD-NN has achieved high scalability up to 786,432 IBM BlueGene/Q cores involving 1.7 billion atoms. Furthermore, we have achieved 4.6-fold reduction of T2S by using a novel network that directly predicts atomic forces from feature vectors. Reduced T2S has for the first time allowed the study of large-scale off-stoichiometry effect in a widely used phase change material, Ge 2 Se 2 Te 5 , thereby resolving its “firstsharp diffraction peak mystery”.

Confluence of extreme-scale quantum dynamics simulations (i.e. quantum@scale) and cutting-edge x-ray free-electron laser experiments are revolutionizing materials science. An archetypal example is the exciting concept of using picosecond light pulses to control emergent material properties on demand in atomically-thin layered materials. This paper describes efforts to scale our quantum molecular dynamics engine toward the United States' first exaflop/s computer, under an Aurora Early Science Program project named "Metascalable layered material genome". Key algorithmic and computing techniques incorporated are: (1) globally-scalable and locally-fast solvers within a linear-scaling divide-conquer-recombine algorithmic framework; (2) algebraic 'BLASification' of computational kernels; and (3) data alignment and loop restructuring, along with register and cache blocking, for enhanced vectorization and efficient memory access. The resulting weak-scaling parallel efficiency was 0.93 on 131,072 Intel Xeon Phi cores for a 56.6 million atom (or 169 million valence-electron) system, whereas the various code transformations achieved 5-fold speedup. The optimized simulation engine allowed us for the first time to establish a significant effect of substrate on the dynamics of layered material upon electronic excitation.

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.

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; the...

The extreme heat resistance of dormant bacterial spores strongly depends on the extent of protoplast dehydration and the concentration of dipicolinic acid (DPA) and its associated calcium salts (Ca-DPA) in the spore core. Recent experiments have suggested that this heat resistance depends on the properties of confined water molecules in the hydrated Ca-DPA-rich protoplasm, but atomistic details have not been elucidated. In this study, we used reactive molecular dynamics (RMD) simulations to study the dynamics of water in hydrated DPA and Ca-DPA as a function of temperature. The RMD simulations indicate two distinct solid-liquid and liquid-gel transitions for the spore core. Simulation results reveal monotonically decreasing solid-gel-liquid transition temperatures with increasing hydration. Additional calculations on the specific heat and free energy of water molecules in the spore core further support the higher heat resistance of dehydrated spores. These results provide an insight into the experimental trend of moist-heat resistance of bacterial spores and reconciles previous conflicting experimental findings on the state of water in bacterial spores.

Vertical hetero-structures made from stacked monolayers of transition metal dichalcogenides (TMDC) are promising candidates for next-generation optoelectronic and thermoelectric devices. Identification of optimal layered materials for these applications requires the calculation of several physical properties, including electronic band structure and thermal transport coefficients. However, exhaustive screening of the material structure space using ab initio calculations is currently outside the bounds of existing computational resources. Furthermore, the functional form of how the physical properties relate to the structure is unknown, making gradient-based optimization unsuitable. Here, we present a model based on the Bayesian optimization technique to optimize layered TMDC hetero-structures, performing a minimal number of structure calculations. We use the electronic band gap and thermoelectric figure of merit as representative physical properties for optimization. The electronic band structure calculations were performed within the Materials Project framework, while thermoelectric properties were computed with BoltzTraP. With high probability, the Bayesian optimization process is able to discover the optimal hetero-structure after evaluation of only ∼20% of all possible 3-layered structures. In addition, we have used a Gaussian regression model to predict not only the band gap but also the valence band maximum and conduction band minimum energies as a function of the momentum.

Transition metal dichalcogenides (TMDC) like MoS2 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 MoO3 surfaces using first-principles-informed ReaxFF reactive molecular dynamics (RMD) simulations. We identify a three-step reaction process associated with synthesis of the MoS2 samples from MoO3 and S2 precursors: O2 evolution and self-reduction of the MoO3 surface; SO/SO2 formation and S2-assisted reduction; and sulfidation of the reduced surface and Mo–S bond formation. These atomic processes occurring during ...

Electrical and optoelectronic properties of two-dimensional (2D) transition metal dichalcogenides (TMDCs) can be tuned by exploiting their structural phase transitions. Here semiconducting (2H) to metallic (1T) phase transition is investigated in a strained ${\mathrm{MoWSe}}_{2}$ monolayer using molecular dynamics (MD) simulations. Novel intermediate structures called $\ensuremath{\alpha}$ and $\ensuremath{\beta}$ are found between the 2H and 1T phases. These intermediate structures are similar to those observed in a 2D $\mathrm{Mo}{\mathrm{S}}_{2}$ by scanning transmission electron microscopy. A deep generative model, namely the variational autoencoder (VAE) trained by MD data, is used to generate novel heterostructures with $\ensuremath{\alpha}$ and $\ensuremath{\beta}$ interfaces. Quantum simulations based on density functional theory show that these heterostructures are stable and suitable for novel nanoelectronics applications.

QXMD is a scalable, parallel program for Quantum Molecular Dynamics simulations with various eXtensions. Its simulation engine is based on (time-dependent) density functional theory using pseudopotentials and a plane-wave basis set, while extensions include nonadiabatic electron–nuclei dynamics and multiscale shock technique. QXMD serves as a community-development platform for new methods and algorithms, a research platform on high-end parallel supercomputers, and an educational platform for hands-on training. Keywords: Nonadiabatic quantum molecular dynamics, Parallel computing, Hands-on training

Monolayer MoS

The mechanical response of ultralight kagome structures consisting of hollow nickel (Ni) nanotubes and solid Ni nanorods to compression is studied using molecular dynamics simulations. In both kagome architectures, 1 6 [ 112 ] Shockley partial dislocations and twin formation are observed under compression. The structure made from solid nanorods shows deformation near both the nodes and beams of the kagome lattice. The hollow kagome architecture has a higher yield point than the solid kagome structure. The deformation in the hollow nanotube structure is mostly localized in the nodal region for strains less than 11%. At higher strains, the deformation sets in all the struts and nodes of the hollow kagome lattice. Owing to this two-stage deformation mechanism, the hollow Ni nanotube kagome structure shows less bending and greater toughness than the solid Ni nanorod kagome architecture.

This study uses ab initio quantum molecular dynamics (QMD) simulations to validate multimillion-atom reactive molecular dynamics (RMD) simulations, and predicts unexpected condensation of carbon atoms during high-temperature oxidation of silicon-carbide nanoparticles (nSiC). For the validation process, a small nSiC in oxygen environment is chosen to perform QMD simulation. The QMD results provide the number of Si-O and C-O bonds as a function of time. RMD simulation is then performed under the identical condition. The time evolutions of different bonds are compared between the QMD and RMD simulations. We observe the condensation of large number of C-cluster nuclei into larger C clusters in both simulations, thereby validating RMD. Furthermore, we use the QMD simulation results as an input to a multi-objective genetic algorithm to train the RMD force-field parameters. The resulting force field far better reproduces the ground-truth QMD simulation results.

Machine learning (ML) is revolutionizing scientific and engineering disciplines owing to its ability to capture hidden patterns in large amounts of data. The recent success of ML can be attributed to increasing amount of data, simulation resources, and improving understanding of statistical inference. For these reasons computational materials science is undergoing a paradigm shift. The main reason is that trial-and-error approach to materials design is inefficient: laboratory trials require a lot of time, and the results of previous trials are not utilized in a systematic fashion. A data-driven approach, which draws upon all relevant data from experiments, and reactive and quantum molecular dynamics simulations, can address these issues. The MAGICS (Materials Genome Innovation for Computational Software) Center develops to aid the synthesis of stacked layered materials by chemical vapor deposition, exfoliation, and intercalation. The identification of different phases is a classification problem and can be solved using ML techniques. We have used feed-forward neural network with three hidden layers to identify the different phases present during computational synthesis of MoSe2. Our goal is to carry out exascale MD simulations using forces for multimillion-atom quantum dynamics simulations from neural networks and deep learning using small QMD simulations.

RXMD is a parallel reactive molecular dynamics (RMD) simulator designed to perform large-scale RMD simulations on commodity laptop computers to supercomputer platforms. With extensive Time-to-Solution (TtoS) optimization techniques and scalable algorithms implemented, RXMD enables researchers to explorer materials design space that requires vast spatial-extent and long-time atomistic events with highly accurate chemistry. Extensible and modular software architecture allows to implement innovative optimization techniques. RXMD is one of core simulation engines at Material Genome Innovation for Computational Software (MAGICS) center, and is freely distributed as an open-source software from MAGICS website and Github. RXMD is also used as computational materials courseware to provide basic training of parallel RMD simulations for computational researchers and community.

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. High accuracy predictions of materials properties can be obtained using Bayesian optimization (BO). A team led by Priya Vashishta at University of Southern California developed a Gaussian regression model capable of predicting the band gap value and thermoelectric properties of three-layered van der Waals heterostructures of transition metal dichalcogenides. A BO model further allowed identification of optimal heterostructures using a minimal number of ab initio calculations. BO models were computed to find either heterostructures with maximum band gap or heterostructures with a band gap value closest to 1.1 eV, relevant for optoelectronic and thermoelectric applications. BO was found to identify nearly optimal materials configurations with high probability, whilst significantly reducing the computational cost of discovering ideal structures using regression models.

Reactive molecular dynamics is a powerful simulation method for describing chemical reactions. Here, we introduce a new generalized polarizable reactive force-field (ReaxPQ+) model to significantly improve the accuracy by accommodating the reorganization of surrounding media. The increased computation is accelerated by (1) extended Lagrangian approach to eliminate the speed-limiting charge iteration, (2) shift-collapse computation of many-body renormalized n-tuples, which provably minimizes data transfer, (3) multithreading with round-robin data privatization, and (4) data reordering to reduce computation and allow vectorization. The new code achieves (1) weak-scaling parallel efficiency of 0.989 for 131,072 cores, and (2) eight-fold reduction of time-to-solution (T2S) compared with the original code, on an Intel Knights Landing-based computer. The reduced T2S has for the first time allowed purely computational synthesis of atomically-thin transition metal dichalcogenide layers assisted by machine learning to discover a novel synthetic pathway.

Reactive molecular dynamics simulations are performed to study self-healing of cracks in Al2O3 containing core/shell SiC/SiO2 nanoparticles. These simulations are carried out in a precracked Al2O3 under mode 1 strain at 1426 °C. The nanoparticles are embedded ahead of the precrack in the Al2O3 matrix. When the crack begins to propagate at a strain of 2%, the nanoparticles closest to the advancing crack distort to create nanochannels through which silica flows toward the crack and stops its growth. At this strain, the Al2O3 matrix at the interface of SiC/SiO2 nanoparticles forms facets along the prismatic (A) ⟨2110⟩ and prismatic (M) ⟨1010⟩ planes. These facets act as nucleation sites for the growth of multiple secondary amorphous grains in the Al2O3 matrix. These grains grow with an increase in the applied strain. Voids and nanocracks form in the grain boundaries but are again healed by diffusion of silica from the nanoparticles.

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 quantum-mechanical 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 MoO3 by H2S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS2 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. Multi-objective genetic algorithms allow training and uncertainty quantification of force-field parameters with minimal modifications of molecular dynamics codes. A team led by Aiichiro Nakano at University of Southern California used an algorithm based on reactive molecular dynamics (RMD) simulations for the training of reactive force-field parameters and uncertainty quantification of simulated quantities of interest. Chemical vapor deposition synthesis of MoS2 monolayer was set as a specific example, and force-field parameters were trained against quantum molecular dynamics (QMD) simulations. Starting from a 128-atom MoO3–H2S system, the reaction dynamics were investigated by estimating the numbers of H–S, Mo–O, and Mo–S bonds as a function of time during the QMD simulations. By comparing these quantities of interest with those obtained in the RMD simulations, it was found that RMD can quantitatively reproduce QMD within an error bar.

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 (MoWSe2) precipitation alloy grown using chemical vapor deposition and composed of numerous nanoscopic MoSe2 and WSe2 regions. Applying a bending strain blue-shifted the MoSe2 and WSe2 A1g Raman modes with the stress concentrated near the precipitate interfaces predominantly affecting the WSe2 modes. In situ local Raman measurements suggested that the crack propagated primarily thorough MoSe2-rich regions in the monolayer alloy. Molecular dynamics (MD) simulations were performed to study crack propagation in an MoSe2 monolayer containing nanoscopic WSe2 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 MoSe2 matrix and WSe2 patches. This was corroborated by high-angle annular dark-field images. Crack branching and subsequent healing of a crack branch were also observed in WSe2, indicating the increased toughness and crack propagation resistance of the alloyed 2D MoWSe2 over the unalloyed counterparts., by Amey Apte, Vidya Kochat, Pankaj Rajak, Aravind Krishnamoorthy , Praveena Manimunda , Jordan A. Hachtel, Juan Carlos Idrobo, Syed Asif Syed Amanulla, Priya Vashishta , Aiichiro Nakano , Rajiv K. Kalia, Chandra Sekhar Tiwary and Pulickel M. Ajayan

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

Transition metal dichalcogenides (TMDC) like MoS

Quantification of lattice thermal conductivity of two-dimensional semiconductors like MoS2 is necessary for the design of electronic and thermoelectric devices, but direct experimental measurements on free-standing samples is challenging. Molecular dynamics simulations, with appropriate corrections, can provide a reference value for thermal conductivity for these material systems. Here, we construct a new empirical forcefield of the Stillinger-Weber form, parameterized to phonon dispersion relations, lattice constants and elastic moduli and we use it to compute a material-intrinsic thermal conductivity of 38.1 W/m-K at room temperature and estimate a maximum thermal conductivity of 85.4 W/m-K at T = 200 K. We also identify that phonon scattering by the large isotopic mass distribution of Mo and S contributes a significant correction (>45%) to the thermal conductivity at low temperatures.

To design a coating that will absorb maximum energy prior to failure with minimum deformation, the shearing process of polycrystalline Zn coated Fe is simulated in the presence of dislocations, using molecular dynamics. The results fed to an Evolutionary Neural Network generated the meta-models of objective functions required in the subsequent Pareto-optimization task using a Multi-objective Genetic Algorithm. Similar calculations conducted for single crystals, and also in the absence of dislocations, are compared and analyzed.

Various interfaces in Zn coated steel are examined for their stability on the basis of two conflicting requirements of minimum deformation at a maximum absorption of shear energy. The shearing process is simulated using a Molecular Dynamics technique and meta-models of both energy and strain are constructed using an Evolutionary Neural Network that itself evolved through a multi-objective Genetic Algorithm. Simultaneous optimization of deformation and energy absorption is conducted with a Predator–prey Genetic Algorithm and the resulting Pareto frontiers are analyzed and discussed. The findings show good correspondence with existing experimental observations.

Wet heat treatments are widely used sterilization techniques for inactivating dangerous and resistant sporulating bacteria. The effectiveness of such treatments depends upon the thermodynamics of water uptake by the spore as well as the kinetics of phase transformations in the hydrated spore core. The mechanism behind these chemical and physical processes remains unknown because the thermodynamic properties of the spore core constituents are not well understood. Here, we use reactive molecular dynamics simulations to calculate the vibrational density of states and specific heat of hydrated calcium dipicolinate as well as the free energy of hydration based on Jarzynski's inequality. These two quantities are used to construct a phase diagram of hydrated calcium dipicolinate, indicating the extent of hydration at different pressures and temperatures, which can be used to identify potential regimes for wet-heat sterilization of bacterial spores.Wet heat treatments are widely used sterilization techniques for inactivating dangerous and resistant sporulating bacteria. The effectiveness of such treatments depends upon the thermodynamics of water uptake by the spore as well as the kinetics of phase transformations in the hydrated spore core. The mechanism behind these chemical and physical processes remains unknown because the thermodynamic properties of the spore core constituents are not well understood. Here, we use reactive molecular dynamics simulations to calculate the vibrational density of states and specific heat of hydrated calcium dipicolinate as well as the free energy of hydration based on Jarzynski's inequality. These two quantities are used to construct a phase diagram of hydrated calcium dipicolinate, indicating the extent of hydration at different pressures and temperatures, which can be used to identify potential regimes for wet-heat sterilization of bacterial spores.