Effects of carbon concentration on the local atomic structure of amorphous GST.

Ge-Sb-Te (GST) alloys are leading phase-change materials for data storage due to the fast phase transition between amorphous and crystalline states. Ongoing research aims at improving the stability of the amorphous phase to improve retention. This can be accomplished by the introduction of carbon as a dopant to Ge2Sb2Te5, which is known to alter the short- and mid-range structure of the amorphous phase and form covalently bonded C clusters, both of which hinder crystallization. The relative importance of these processes as a function of C concentration is not known. We used molecular dynamics simulation based on density functional theory to study how carbon doping affects the atomic structure of GST-C. Carbon doping results in an increase in tetrahedral coordination, especially of Ge atoms, and this is known to stabilize the amorphous phase. We observe an unexpected, non-monotonous trend in the number of tetrahedral bonded Ge with the amount of carbon doping. Our simulations show an increase in the number of tetrahedral bonded Ge up to 5 at.% C, after which the number saturates and begins to decrease above 14 at.% C. The carbon atoms aggregate into clusters, mostly in the form of chains and graphene flakes, leaving less carbon to disrupt the GST matrix at higher carbon concentrations. Different degrees of carbon clustering can explain divergent experimental results for recrystallization temperature for carbon doped GST.

Interpretable Performance Models for Energetic Materials using Parsimonious Neural Networks.

Predictive models for the performance of explosives and propellants are important for their design, optimization, and safety. Thermochemical codes can predict some of these properties from fundamental quantities such as density and formation energies that can be obtained from first principles. Models that are simpler to evaluate are desirable for efficient, rapid screening of material screening. In addition, interpretable models can provide insight into the physics and chemistry of these materials that could be useful to direct new synthesis. Current state-of-the-art performance models are based on either the parametrization of physics-based expressions or data-driven approaches with minimal interpretability. We use parsimonious neural networks (PNNs) to discover interpretable models for the specific impulse of propellants and detonation velocity and pressure for explosives using data collected from the open literature. A combination of evolutionary optimization with custom neural networks explores and trains models with objective functions that balance accuracy and complexity. For all three properties of interest, we find interpretable models that are Pareto optimal in the accuracy and simplicity space.

High-throughput density functional theory screening of double transition metal MXene precursors.

MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their chemical tunability has been significantly expanded with the successful synthesis of double transition metal MXenes. While knowledge of the structure and energetics of double transition metal MAX phases is critical to designing and optimizing new MXenes, only a small subset of these materials been explored. We present a comprehensive dataset of key properties of MAX phases obtained using density functional theory within the generalized gradient approximation exchange-correlation functionals. Energetics and structure of 8,712 MAX phases have been calculated and stored in a queryable, open database hosted at nanoHUB.

High-pressure and temperature neural network reactive force field for energetic materials.

Reactive force fields for molecular dynamics have enabled a wide range of studies in numerous material classes. These force fields are computationally inexpensive compared with electronic structure calculations and allow for simulations of millions of atoms. However, the accuracy of traditional force fields is limited by their functional forms, preventing continual refinement and improvement. Therefore, we develop a neural network-based reactive interatomic potential for the prediction of the mechanical, thermal, and chemical responses of energetic materials at extreme conditions. The training set is expanded in an automatic iterative approach and consists of various CHNO materials and their reactions under ambient and shock-loading conditions. This new potential shows improved accuracy over the current state-of-the-art force fields for a wide range of properties such as detonation performance, decomposition product formation, and vibrational spectra under ambient and shock-loading conditions.

A coarse-grain reactive model of RDX: Molecular resolution at the µm scale.

Predictive models for the thermal, chemical, and mechanical response of high explosives at extreme conditions are important for investigating their performance and safety. We introduce a particle-based, reactive model of 1,3,5-trinitro-1,3,5-triazinane (RDX) with molecular resolution utilizing generalized energy-conserving dissipative particle dynamics with reactions. The model is parameterized with respect to the data from atomistic molecular dynamics simulations as well as from quantum mechanical calculations, thus bridging atomic processes to the mesoscales, including microstructures and defects. It accurately captures the response of RDX under a range of thermal loading conditions compared to atomistic simulations. In addition, the Hugoniot response of the CG model in the overdriven regime reasonably matches atomistic simulations and experiments. Exploiting the model's high computational efficiency, we investigate mesoscale systems involving millions of molecules and characterize size-dependent criticality of hotspots in RDX. The combination of accuracy and computational efficiency of our reactive model provides a tool for investigation of mesoscale phenomena, such as the role of microstructures and defects in the shock-to-deflagration transition, through particle-based simulation.

Design of Atomic Ordering in Mo2Nb2C3Tx MXenes for Hydrogen Evolution Electrocatalysis.

The need for novel materials for energy storage and generation calls for chemical control at the atomic scale in nanomaterials. Ordered double-transition-metal MXenes expanded the chemical diversity of the family of atomically layered 2D materials since their discovery in 2015. However, atomistic tunability of ordered MXenes to achieve ideal composition-property relationships has not been yet possible. In this study, we demonstrate the synthesis of Mo2+αNb2-αAlC3 MAX phases (0 ≤ α ≤ 0.3) and confirm the preferential ordering behavior of Mo and Nb in the outer and inner M layers, respectively, using density functional theory, Rietveld refinement, and electron microscopy methods. We also synthesize their 2D derivative Mo2+αNb2-αC3Tx MXenes and exemplify the effect of preferential ordering on their hydrogen evolution reaction electrocatalytic behavior. This study seeks to inspire further exploration of the ordered double-transition-metal MXene family and contribute composition-behavior tools toward application-driven design of 2D materials.

Extemporaneous Mechanochemistry: Shock-Wave-Induced Ultrafast Chemical Reactions Due to Intramolecular Strain Energy.

Regions of energy localization referred to as hotspots are known to govern shock initiation and the run-to-detonation in energetic materials. Mounting computational evidence points to accelerated chemistry in hotspots from large intramolecular strains induced via the interactions between the shock wave and microstructure. However, definite evidence mapping intramolecular strain to accelerated or altered chemical reactions has so far been elusive. From a large-scale reactive molecular dynamics simulation of the energetic material 1,3,5-triamino-2,4,6-trinitrobenzene, we map decomposition kinetics to molecular temperature and intramolecular strain energy prior to reaction. Both temperature and intramolecular strain are shown to accelerate chemical kinetics. A detailed analysis of the atomistic trajectory shows that intramolecular strain can induce a mechanochemical alteration of decomposition mechanisms. The results in this paper could inform continuum-level chemistry models to account for a wide range of mechanochemical effects.

Sim2Ls: FAIR simulation workflows and data.

Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data.

Modeling environment-dependent atomic-level properties in complex-concentrated alloys.

Complex-concentrated-alloys (CCAs) are of interest for a range of applications due to a host of desirable properties, including high-temperature strength and tolerance to radiation damage. Their multi-principal component nature results in a vast number of possible atomic environments with the associated variability in chemistry and structure. This atomic-level variability is central to the unique properties of these alloys but makes their modeling challenging. We combine atomistic simulations using many body potentials with machine learning to develop predictive models of various atomic properties of CrFeCoNiCu-based CCAs: relaxed vacancy formation energy, atomic-level cohesive energy, pressure, and volume. A fingerprint of the local atomic environments is obtained combining invariants associated with the local atomic geometry and periodic-table information of the atoms involved. Importantly, all descriptors are based on the unrelaxed atomic structure; thus, they are computationally inexpensive to compute. This enables the incorporation of these models into macroscopic simulations. The models show good accuracy and we explore their ability to extrapolate to compositions and elements not used during training.

Atomistic Insights on the Full Operation Cycle of a HfO2-Based Resistive Random Access Memory Cell from Molecular Dynamics.

We characterize the atomic processes that underlie forming, reset, and set in HfO2-based resistive random access memory (RRAM) cells through molecular dynamics (MD) simulations, using an extended charge equilibration method to describe external electric fields. By tracking the migration of oxygen ions and the change in coordination of Hf atoms in the dielectric, we characterize the formation and dissolution of conductive filaments (CFs) during the operation of the device with atomic detail. Simulations of the forming process show that the CFs form through an oxygen exchange mechanism, induced by a cascade of oxygen displacements from the oxide to the active electrode, as opposed to aggregation of pre-existing oxygen vacancies. However, the filament breakup is dominated by lateral, rather than vertical (along the filament), motion of vacancies. In addition, depending on the temperature of the system, the reset can be achieved through a redox effect (bipolar switch), where oxygen diffusion is governed by the applied bias, or by a thermochemical process (unipolar switch), where the diffusion is driven by temperature. Unlike forming and similar to reset, the set process involves lateral oxygen atoms as well. This is driven by field localization associated with conductive paths.

Hybrid Polymer-Garnet Materials for All-Solid-State Energy Storage Devices.

Hybrid electrolyte materials comprising polymer-ionic salt matrixes embedded with garnet particles constitute a promising class of materials for the realization of all-solid-state batteries. In addition to providing solutions to the safety issues inherent to current liquid electrolytes, hybrid polymer electrolytes offer advantages over other solid-state electrolytes. This is because their functional properties such as ionic conductivity, electrochemical stability, and mechanical and thermal properties can be tailored to a particular application by independently optimizing the properties of the constituent materials. This independent optimization permits the rational design of solid-state electrolytes, thereby solving the current bottlenecks that prevent their practical implementation into battery devices. This Mini-Review starts with a survey of solid-state electrolytes, focusing on their materials and ion transport limitations. Next, we summarize the current understanding of transport mechanisms in composite polymer electrolytes (CPEs) with the purpose of identifying materials' solutions for further improving their properties. The overall goal of the Mini-Review is to foster heightened research interest in these hybrid structures to rapidly advance development of future all-solid-state battery devices.

Parsimonious neural networks learn interpretable physical laws.

Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models. Less explored is its use to discover interpretable physical laws from data. We propose parsimonious neural networks (PNNs) that combine neural networks with evolutionary optimization to find models that balance accuracy with parsimony. The power and versatility of the approach is demonstrated by developing models for classical mechanics and to predict the melting temperature of materials from fundamental properties. In the first example, the resulting PNNs are easily interpretable as Newton's second law, expressed as a non-trivial time integrator that exhibits time-reversibility and conserves energy, where the parsimony is critical to extract underlying symmetries from the data. In the second case, the PNNs not only find the celebrated Lindemann melting law, but also new relationships that outperform it in the pareto sense of parsimony vs. accuracy.

A Hotspot's Better Half: Non-Equilibrium Intra-Molecular Strain in Shock Physics.

Shockwave interactions with a material's microstructure localizes energy into hotspots, which act as nucleation sites for complex processes such as phase transformations and chemical reactions. To date, hotspots have been described via their temperature fields. Nonreactive, all-atom molecular dynamics simulations of shock-induced pore collapse in a molecular crystal show that more energy is localized as potential energy (PE) than can be inferred from the temperature field and that PE localization persists beyond thermal diffusion. The origin of the PE hotspot is traced to large intramolecular strains, storing energy in modes readily available for chemical decomposition.

Predicted Reaction Mechanisms, Product Speciation, Kinetics, and Detonation Properties of the Insensitive Explosive 2,6-Diamino-3,5-dinitropyrazine-1-oxide (LLM-105).

2,6-Diamino-3,5-dinitropyrazine-1-oxide (LLM-105) is a relatively new and promising insensitive high-explosive (IHE) material that remains only partially characterized. IHEs are of interest for a range of applications and from a fundamental science standpoint, as the root causes behind insensitivity are poorly understood. We adopt a multitheory approach based on reactive molecular dynamic simulations performed with density functional theory, density functional tight-binding, and reactive force fields to characterize the reaction pathways, product speciation, reaction kinetics, and detonation performance of LLM-105. We compare and contrast these predictions to 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a prototypical IHE, and 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (HMX), a more sensitive and higher performance material. The combination of different predictive models allows access to processes operative on progressively longer timescales while providing benchmarks for assessing uncertainties in the predictions. We find that the early reaction pathways of LLM-105 decomposition are extremely similar to TATB; they involve intra- and intermolecular hydrogen transfer. Additionally, the detonation performance of LLM-105 falls between that of TATB and HMX. We find agreement between predictive models for first-step reaction pathways but significant differences in final product formations. Predictions of detonation performance result in a wide range of values, and one-step kinetic parameters show the similar reaction rates at high temperatures for three out of four models considered.

Nitromethane Decomposition via Automated Reaction Discovery and an Ab Initio Corrected Kinetic Model.

We explore the systematic construction of kinetic models from in silico reaction data for the decomposition of nitromethane. Our models are constructed in a computationally affordable manner by using reactions discovered through accelerated molecular dynamics simulations using the ReaxFF reactive force field. The reaction paths are then optimized to determine reaction rate parameters. We introduce a reaction barrier correction scheme that combines accurate thermochemical data from density functional theory with ReaxFF minimal energy paths. We validate our models across different thermodynamic regimes, showing predictions of gas phase CO and NO concentrations and high-pressure induction times that are similar to experimental data. The kinetic models are analyzed to find fundamental decomposition reactions in different thermodynamic regimes.

Unsupervised Learning-Based Multiscale Model of Thermochemistry in 1,3,5-Trinitro-1,3,5-triazinane (RDX).

The response of high-energy-density materials to thermal or mechanical insults involves coupled thermal, mechanical, and chemical processes with disparate temporal and spatial scales that no single model can capture. Therefore, we developed a multiscale model for 1,3,5-trinitro-1,3,5-triazinane, RDX, where a continuum description is informed by reactive and nonreactive molecular dynamics (MD) simulations to describe chemical reactions and thermal transport. Reactive MD simulations under homogeneous isothermal and adiabatic conditions are used to develop a reduced-order chemical kinetics model. Coarse graining is done using unsupervised learning via non-negative matrix factorization. Importantly, the components resulting from the analysis can be interpreted as reactants, intermediates, and products, which allows us to write kinetics equations for their evolution. The kinetics parameters are obtained from isothermal MD simulations over a wide temperature range, 1200-3000 K, and the heat evolved is calibrated from adiabatic simulations. We validate the continuum model against MD simulations by comparing the evolution of a cylindrical hotspot 10 nm in diameter. We find excellent agreement in the time evolution of the hotspot temperature fields both in cases where quenching is observed and at higher temperatures for which the hotspot transitions into a deflagration wave. The validated continuum model is then used to assess the criticality of hotspots involving scales beyond the reach of atomistic simulations that are relevant to detonation initiation.

Novel Mode of Noncrystallographic Branching in the Initial Stages of Polymer Fibril Growth.

Spherulites are the most ubiquitous of polycrystalline microstructure of polymers; they develop under a wide range of conditions by the subsequent branching of crystalline lamella that results in an overall spherical shape. Despite significant efforts over decades, the mechanisms behind branching remain unclear. Molecular dynamics simulations in polyethylene reveal the molecular-level origin of noncrystallographic branching and the initial formation of fibrils. We find that the growth of crystalline lamella by reeling in and folding of polymer chains causes surprisingly large local deformation which, in turn, aligns the chains in the neighboring undercooled liquid. Thus, subsidiary grains nucleate with preferred orientations resulting in fibril growth with branching at small angles, consistent with those observed experimentally.

Investigation of structural ordering in network forming ionic liquids: A molecular dynamics study.

Molecular dynamics simulations reveal anomalous short- and medium-range ordering with increasing temperature in network-forming ionic liquids (NIL) consisting of alkyl-diammonium cations with long side chains of 6 carbon atoms and citrate anions (NIL 5-6). This effect is weaker, and only a short-range order is observed in equivalent systems with side chains shortened to 3 C atoms (NIL 5-3). The short-range ordering can be attributed to volume expansion during heating, but the intermediate range order requires volume expansion as well as an increase in temperature. We find that the cross (cation-anion) interactions are the major contributors to the observed trend and the development of complex 3D correlations in the two-particle correlation functions. The simulations suggest that the above phenomenon can be correlated to local trapping of cation molecules in a variety of configurations at lower temperatures where molecular shape distributions show great variability; as temperature increases, the distribution of molecular radii of gyration becomes narrower, enabling the increased ordering.

Pulse Dynamics of Electric Double Layer Formation on All-Solid-State Graphene Field-Effect Transistors.

Electric double layer (EDL) dynamics in graphene field-effect transistors (FETs) gated with polyethylene oxide (PEO)-based electrolytes are studied by molecular dynamics (MD) simulations from picoseconds to nanoseconds and experimentally from microseconds to milliseconds. Under an applied field of approximately mV/nm, EDL formation on graphene FETs gated with PEO:CsClO4 occurs on the timescale of microseconds at room temperature and strengthens within 1 ms to a sheet carrier density of nS ≈ 1013 cm-2. Stronger EDLs (i.e., larger nS) are induced experimentally by pulsing with applied voltages exceeding the electrochemical window of the electrolyte; electrochemistry is avoided using short pulses of a few milliseconds. Dynamics on picosecond to nanosecond timescales are accessed using MD simulations of PEO:LiClO4 between graphene electrodes with field strengths of hundreds of mV/nm which is 100× larger than experiment. At 100 mV/nm, EDL formation initiates in sub-nanoseconds achieving charge densities up to 6 × 1013 cm-2 within 3 nanoseconds. The modeling shows that under sufficiently high electric fields, EDLs with densities ∼1013 cm-2 can form within a nanosecond, which is a timescale relevant for high-performance electronics such as EDL transistors (EDLTs). Moreover, the combination of experiment and modeling shows that the timescale for EDL formation ( nS = 1013 to 1014 cm-2) can be tuned by 9 orders of magnitude by adjusting the field strength by only 3 orders of magnitude.