Alejandro Strachan

Graphical abstract

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 Ge 2 Sb 2 Te 5, 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 …

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

Interfacial properties and interactions between crystals and binders are thought to play a critical role in the initiation of high explosive (HE) formulations but are poorly understood. Using a model HE-binder system, we explore the physical properties of these interfaces through molecular dynamics (MD) simulations with a nonreactive force field. A workflow is developed for preparing MD simulation cells with arbitrarily oriented molecular crystal interfaces that enables rapid exploration of interfacial properties needed to inform structure-property relationships. Interconnections between surface/interfacial energetics, structure, rheology, phase transformations, and multicomponent mixing are examined as a function of crystal orientation and thermodynamic loading path.

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 …

The area-normalized change of mass (Δm/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we developed and published an open, online, computational workflow that fits the data to various models of oxidation kinetics, uses Bayesian statistics for model selection, and makes the raw data and model parameters available via a queryable database. The tool, Refractory Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB’s Sim2Ls to make the workflow and data (including metadata) findable, accessible, interoperable, and reusable (FAIR). We find that the models selected by the original …

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 …

Data science and artificial intelligence are playing an increasingly important role in the physical sciences. However, many fields, including energetic materials, suffer from scarce data, and the available data is not organized in a way conducive to machine learning. To address this gap, we identified rich sources of experimental and calculated data and collected this data into an electronic format that is tailored for efficient querying, filtering, and extracting. We will present new predictive models that use multi-task learning to learn multiple properties and address data scarcity.

Mechanical forces acting on atoms or molecular groups can alter chemical kinetics and decomposition paths. So called mechanochemistry has been proposed to influence a variety of processes, from the formation of prebiotic compounds during planetary collisions to the shock-induced initiation of explosives. It has also been harnessed in various engineering applications such as mechanophores and ball milling in industrial applications. Experimental and computational tools designed to characterize the effect of mechanics on chemistry have focused exclusively on simple linear forces between pairs of atoms or molecular groups. However, the mechanical loading in condensed matter systems is significantly more complex and involves many-body deformations. Therefore, we propose a methodology to characterize the effect of many-body intra-molecular strains on decomposition kinetics and reaction pathways. We combine four-body external potentials with reactive molecular dynamics and show that many body strains that mimic those observed in condensed matter encourage bond rupture in a spiropyran mechanophore and accelerate thermal decomposition of condensed TATB, an energetic material. The approach is generalizable to a variety of systems and can be used in conjunction with ab initio molecular dynamics, and the two examples utilized here illustrates both the versatility of the method and the importance of many-body mechanochemistry.

The crystallization of complex, concentrated alloys can result in atomic-level short-range order, composition gradients, and phase separation. These features govern the properties of the resulting alloy. While nucleation and growth in single-element metals are well understood, several open questions remain regarding the crystallization of multi-principal component alloys. We use MD to model the crystallization of a five-element, equiatomic alloy modeled after CoCrCuFeNi upon cooling from the melt. Stochastic, homogeneous nucleation results in nuclei with a biased composition distribution, rich in Fe and Co. This deviation from the random sampling of the overall composition is driven by the internal energy and affects nuclei of a wide range of sizes, from tens of atoms all the way to super-critical sizes. This results in short range order and compositional gradients at nanometer scales.

High entropy oxides (HEO) are promising materials for novel applications as they are known to exhibit ultra-high dielectric constants as well as high ionic conductivity when doped with lithium. The combination of these attributes renders HEOs attractive as nano-fillers in composite polymer electrolytes (CPE). We report on the synthesis of (MgCoCuZn) O,(MgCoNiCuZn) O, and Li x (MgCoNiCuZn) 1-x O utilizing a sol-gel method in combination with high temperature annealing followed by fast quenching to attain single rock salt crystalline order of these HEOs. Nanoparticles are fabricated employing ball milling and different weight loads are incorporated into polymer-salt matrixes. In this talk we will report on ongoing measurements and results on the structural properties of the HEOs and the CPEs as well as ion transport measurements.

DD01. 00006: HMX/TNT Interfacial Properties from Molecular Dynamics Simulations: Energetics, Rheology, and Molecular Structure*

The research landscape has become increasingly interdisciplinary and complex with novel hardware, software, data, and lab instruments. The reproducibility of research results, usability of tools, and sharing of methods are all crucial for timely collaboration for research and teaching. Hubzero is a widely used science gateway framework designed to support online communities with efficient sharing and publication processes. This article discusses the growth of communities for the four science gateways nanoHUB, MyGeoHub, QUBEShub, and HubICL using the Hubzero platform to foster open science and tackling education with a diverse set of approaches and target communities.

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 …

Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in applications like code generation are well-documented. We explore the ability of the GPT-4 LLM to ameliorate two major challenges in computational materials science: i) the high barriers for adoption of scientific software associated with the use of custom input languages, and ii) the poor reproducibility of published results due to insufficient details in the description of simulation methods. We focus on a widely used software for molecular dynamics simulations, the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), and quantify the usefulness of input files generated by GPT-4 from task descriptions in English and its ability to generate detailed descriptions of computational tasks from input files. We find that GPT-4 can generate correct and ready-to-use input files for relatively simple tasks and useful starting points for more complex, multi-step simulations. In addition, GPT-4's description of computational tasks from input files can be tuned from a detailed set of step-by-step instructions to a summary description appropriate for publications. Our results show that GPT-4 can reduce the number of routine tasks performed by researchers, accelerate the training of new users, and enhance reproducibility.

Data science and artificial intelligence have become an indispensable part of scientific research. While such methods rely on high-quality and large quantities of machine-readable scientific data, the current scientific data infrastructure faces significant challenges that limit effective data curation and sharing. These challenges include insufficient return on investment for researchers to share quality data, logistical difficulties in maintaining long-term data repositories, and the absence of standardized methods for evaluating the relative importance of various datasets. To address these issues, this paper presents the Lennard Jones Token, a blockchain-based proof-of-concept solution implemented on the Ethereum network. The token system incentivizes users to submit optimized structures of Lennard Jones particles by offering token rewards, while also charging for access to these valuable structures. Utilizing smart contracts, the system automates the evaluation of submitted data, ensuring that only structures with energies lower than those in the existing database for a given cluster size are rewarded. The paper explores the details of the Lennard Jones Token as a proof of concept and proposes future blockchain-based tokens aimed at enhancing the curation and sharing of scientific data.

Y04. 00001: Hotspot formation in polymer-bonded energetic materials by large scale atomistic simulations*

When formulating a new solid propellant, one of the most important aspects of its performance is the burning rate's response to a change in pressure. For this reason, it is useful to be able to predict the burning rate response of a given propellant before the propellant formulation is created such that experimental trade studies are minimized or reduced in scale. While many theoretical and phenomenological models have been proposed to explain various aspects of energetic material combustion, little work has been made publicly available in the application of machine learning models to predicting solid propellant burning rates. To facilitate model creation, the material formulation and burning rate parameters for over 600 publicly available propellant formulations have been collected into a coherent data set. This work utilizes the large amount of publicly available data to inform a random forest machine learning (ML …

Calorimetry and mechanical tests on polyacrylonitrile PAN show two glass transitions (Tg). The lower Tg (370 K) has been attributed to its ordered phase with the amorphous regions showing a glass transition at 420 K. An understanding of these processes at the molecular level is lacking as is the counterintuitive lower Tg for the ordered phase as compared to the amorphous one. Molecular dynamics (MD) simulations of the amorphous and ordered PAN phases result in Tg in the range 445–455 K and 450–465 K, respectively. The amorphous value is in good agreement with the experimental result once rate effects are considered. However, MD predicts a slightly higher Tg for the ordered phase; this is as expected but in disagreement with the experimental observation. To explain this discrepancy, we built a large-scale amorphous PAN sample and mechanically strained it above its Tg to achieve a structure …

The response of materials to shock loading is important to planetary science, aerospace engineering, and energetic materials. Thermally activated processes, including chemical reactions and phase transitions, are significantly accelerated by energy localization into hotspots. These result from the interaction of the shockwave with the materials’ microstructure and are governed by complex, coupled processes, including the collapse of porosity, interfacial friction, and localized plastic deformation. These mechanisms are not fully understood and the lack of models limits our ability to predict shock to detonation transition from chemistry and microstructure alone. We demonstrate that deep learning can be used to predict the resulting shock-induced temperature fields in composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input. The accuracy of the …