Comparisons of CTH Simulations with Measured Wave Profiles for Simple Flyer Plate Experiments
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Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions
Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model ( SynthNN ) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.
36 MATERIALS SCIENCE↗
Localization landscape theory of disorder in semiconductors. III. Application to carrier transport and recombination in light emitting diodes
In this paper we introduce a novel method to account for quantum disorder effects into the classical drift-diffusion model of semiconductor transport through the localization landscape theory. Quantum confinement and quantum tunneling in the disordered system change dramatically the energy barriers acting on the perpendicular transport of heterostructures. In addition, they lead to percolative transport through paths of minimal energy in the two-dimensional (2D) landscape of disordered energies of multiple 2D quantum wells. This model solves the carrier dynamics with quantum effects self-consistently and provides a computationally much faster solver when compared with the Schrödinger equation resolution. The theory also provides a good approximation to the density of states for the disordered system over the full range of energies required to account for transport at room temperature. The current-voltage characteristics modeled by three-dimensional simulation of a full nitride-based light emitting diode (LED) structure with compositional material fluctuations closely match the experimental behavior of high-quality blue LEDs. The model allows also a fine analysis of the quantum effects involved in carrier transport through such complex heterostructures. Finally, details of carrier population and recombination in the different quantum wells are given.
36 MATERIALS SCIENCE↗
Metal dissolution from first principles: Potential-dependent kinetics and charge transfer
Metal dissolution and its inverse process are integral to both corrosion and electrodeposition; however, many mechanistic details regarding the dissolution process are challenging to decipher. These include how ion dissolution kinetics and charge transfer are influenced by the competition between metal and solvent interactions under an electrode potential. Here, in this work, we introduce a computational framework based on density functional theory with grand-canonical treatment of electrons to directly predict the potential energy landscape for metal dissolution at a constant potential. Using aluminum as an example, we demonstrate that dissolution kinetics is governed by competing kinetics between two physical processes associated with metal–metal bond breaking and ion-migration within the electrical double layer, respectively. We identify a kinetic transition between regimes dominated by each of these processes and show that this transition depends on the operating electrode potential, among other key factors. It is further found that kinetics and thermodynamics of these processes can be described with a simple, one-parameter Marcus-theory-type model. Beyond offering new understanding of charge transfer during dissolution, our simulation protocol provides a recipe for directly predicting other important quantities in electrochemical reactions from first principles that are difficult to measure, such as the symmetry factor.
36 MATERIALS SCIENCE↗
Investigation of a model based reconstruction technique for neutron tomography at the PULSTAR reactor
Herein, a model based neutron computed tomography (CT) reconstruction technique is developed and implemented to improve the spatial resolution of the reconstructed images. The spatial resolution of a scintillator and a charge-coupled device (CCD) based digital neutron imaging system is quantified and used as the system response function in the iterative reconstruction process. The response function is included in the forward projection model to provide better estimation of the actual projection, thus improving the spatial resolution of the reconstructed images. Experiments were performed at the neutron imaging facility of the PULSTAR research reactor to corroborate the performance of the developed algorithm with a 50 μm and 250 μa m 6 LiF:ZnS thick scintillator. The reconstruction results of the model based technique have shown an improvement of nearly 40% in achievable spatial resolution as compared to the standard filtered back projection (FBP) technique.
47 OTHER INSTRUMENTATION↗
A Joint Experimental/Computational Study of Non-Idealities in Practical Rotating Detonation Engines. Final Project Report, 10/2015 - 09/2020
The overall goal of this work is to understand non-ideal effects in practical rotating detonation engines (RDEs) that impede the realization of theoretical detonation cycle efficiencies. The main limitations in RDE operability and performance are associated with practical design considerations, where non-idealities, such as incomplete mixing, fuel leakage, secondary deflagration, instabilities and geometry-dependent effects, reduce the effectiveness of the detonation-based compression cycle. The goal of this work is to take a fundamental point of view, and use detailed experiments and simulations to understand non-ideal effects, their contribution to loss in pressure gain, and RDE operability limitations. Current research is mainly focused on design and overall operability of RDEs. Our approach is instead to look at the physics of basic processes occurring in RDEs. In particular, we consider processes associated with non-idealities and we use a combination of laser and non-laser diagnostics and large eddy simulation/direct numerical simulation computations to investigate these underlying phenomena. By advancing the fundamental science of detonation wave propagation in RDE flowfields, and subject to non-ideal conditions, will enable design to progress more rapidly. This work is a collaborative work involving experiments, simulations and external domain experts to understand the physics behind RDEs and non-idealities. Through collaboration between experimentalists and modelers, we have developed targeted experiments to investigate fundamental aspects of the operation of an RDE. Furthermore, we have developed advanced combustion models that account for non-ideal effects in the operation of an RDE and are able to capture the complexities of realistic RDE configurations. We conduct detailed physical and computational experiments to investigate the flame structure ahead and behind detonation fronts in the presence of non-uniform or incomplete mixing, leakage and secondary combustion. We use optical and laser diagnostics to identify the structure of the detonation wave. We consider different working parameters, such injection scheme, geometry, flow rates and equivalence ratios. We brought in DNS/LES capabilities to RDE design by specifically incorporating detailed kinetics and pressure-sensitive combustion physics, thus improving upon current practice based on an Euler equations description that do not take into account viscous effects, while the combustion process is introduced using an induction time based global ignition model. These current models lack the capability of capturing non-ideal mixing and fuel stratification effects — limitations that models and analysis developed in this work have been reduced. Furthermore, the significant changes in pressure within the domain due to the detonation requires combustion models with time-varying pressure effects. In this regard, using DNS with detailed chemistry, and developing LES-based combustion models for complex geometries with detonation wave has enable to understand some of the underlying phenomena controlling the operation of these devices. The main objectives developed by the project are the following ones: Objective 1. Develop canonical and operational RDE configurations, as well as imaging-based diagnostics for understanding fuel stratification, leakage, deflagration and detonation structure under non-ideal conditions in RDEs. Objective 2. Develop detailed computational tools for studying detonation wave propagation processes in RDEs. Objective 3. Develop a comprehensive picture of the fundamental physics governing non-idealities and how they impact RDE performance and operability from both experiments and simulations.
03 NATURAL GAS↗
Isolating Chemical Reaction Mechanism as a Variable with Reactive Coarse-Grained Molecular Dynamics: Step-Growth versus Chain-Growth Polymerization
Here we present a general approach to isolate chemical reaction mechanism as an independently controllable variable across chemically distinct systems. Modern approaches to reduce the computational expense of molecular dynamics simulations often group multiple atoms into a single “coarse-grained” interaction site, which leads to a loss of chemical resolution. In this work we convert this shortcoming into a feature and use identical coarse-grained models to represent molecules that share nonreactive characteristics but react by different mechanisms. As a proof of concept, we use this approach to simulate and investigate distinct, yet similar, trifunctional isocyanurate resin formulations that polymerize by either chain- or step-growth. Because the underlying molecular mechanics of these models are identical, all emergent differences are a function of the reaction mechanism only. We find that the microscopic morphologies resemble related all-atom simulations and that simulated mechanical testing reasonably agrees with experiment.
36 MATERIALS SCIENCE↗
Automated AI-driven Molecular Design for Therapeutic Discovery
In recent years, artificial intelligence and machine learning (AI/ML) approaches have revolutionized the process of designing new therapeutics, enabling scientists to rapidly respond to emerging threats from various pathogens. A prime example is the SARS-CoV-2 main protease, a key target for the development of antiviral inhibitors. In this study, we employed a novel, integrated approach that combines AI-driven iterative design of inhibitor candidates, screening based on physio-chemical properties and toxicity, physics-based computational modeling of protein-inhibitor interactions, and AI-assisted analysis of Native MS biophysical assay and characterization of designed candidates. Our deep learning 3D-scaffold model, which uses an input scaffold as a starting point, generated tens of thousands of compounds while preserving the key scaffold. To optimize these candidates, we calculated a comprehensive set of 136 descriptors, including both 2D and 3D molecular features, for compounds targeting the SARS-CoV-2 Main protease (Mpro) and a neurodegenerative disease-associated protein, cyclophilin (Cyp). The generated compounds were initially filtered based on their properties and then ranked according to their predicted binding affinity using our automated modeling and ML methods. Experimental validation of the Mpro candidates showing inhibitory activity demonstrates that our workflow can expedite the therapeutic discovery.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
MFANS 2024 - Formally Proving Characteristics of Cyber-Physical Systems
Cyber-physical systems (CPS) are engineered systems that rely on the smooth integration of computational algorithms and physical elements. This integration presents new challenges for verifying that systems will behave as expected. The goal of this presentation is to present current challenges and potential solutions for the formal verification of cyber-physical systems. For cyber systems, formal methods refer to systematically rigorous mathematical techniques employed in the specification, development, analysis, and verification of both software and hardware systems. Recent advancements in computer science have yielded sophisticated tools specifically designed to address challenges associated with formal methods in complex systems. These tools leverage various foundational concepts such as logic, formal languages, program semantics, type systems, type theory, and automata theory. A notable achievement in the application of formal methods is the seL4 microkernel, claimed to be the first general-purpose operating-system kernel to be verified. Its proof implies the absence of bugs and guarantees that the kernel meets specifications. For physical systems, dynamic and control theory has a history of using rigorous analytic techniques to prove functional correctness. Lyapunov, optimal, classical, modern, and robust control theories all provide rigorous mathematical methods both to analyze system performance and to design controller that can be guaranteed to meet certain objectives. Recent computational techniques like level set theory and reachability analysis provide assertions that a system's state will avoid unsafe regions. Even though success has been independently achieved for cyber systems and physical systems, the integration of such systems creates new challenges. In particular, there is an obvious discrepancy between finite-state machines and infinite-state systems, resulting in different approaches for modeling and analyzing these system. While it is possible to simulate hybrid systems, this provides only a demonstration of a performance and not proof. For hybrid systems, current formal methods and system analysis approaches typically require a workarounds to work on hybrid systems like CPS. This paper will outline the state of the art and limits of current practice for formally verifying CPS and will identify possible research directions that require attention.
97 MATHEMATICS AND COMPUTING↗
ECP External Review - Sandia National Laboratories ATDM (WBS X.X.X.X)(KPP-2)
Accomplishments to date are provided for several programs: SPARC for Virtual Flight Testing; EMPIRE for Electromagnetic Plasma Physics; and the Science Challenge Problem Description.
42 ENGINEERING↗
Shift-and-invert parallel spectral transformation eigensolver: Massively parallel performance for density-functional based tight-binding
The Shift-and-invert parallel spectral transformations (SIPs), a computational approach to solve sparse eigenvalue problems, is developed for massively parallel architectures with exceptional parallel scalability and robustness. The capabilities of SIPs are demonstrated by diagonalization of density-functional based tight-binding (DFTB) Hamiltonian and overlap matrices for single-wall metallic carbon nanotubes, diamond nanowires, and bulk diamond crystals. The largest (smallest) example studied is a 128,000 (2000) atom nanotube for which ~330,000 (~5600) eigenvalues and eigenfunctions are obtained in ~190 (~5) seconds when parallelized over 266,144 (16,384) Blue Gene/Q cores. Weak scaling and strong scaling of SIPs are analyzed and the performance of SIPs is compared with other novel methods. Different matrix ordering methods are investigated to reduce the cost of the factorization step, which dominates the time-to-solution at the strong scaling limit. As a result, a parallel implementation of assembling the density matrix from the distributed eigenvectors is demonstrated.
36 MATERIALS SCIENCE↗
Sequence-defined Pareto frontier of a copolymer structure
The correlations between the sequence of monomers in a macromolecule and its three-dimensional (3D) structure is a grand challenge in polymer science. The properties and functions of macromolecules depend on their 3D shape that has appeared to be dictated by their monomer sequence. However, the progress towards understanding the sequence–structure-property correlations and their utilization in materials engineering are slow because it is almost impossible to characterize an astronomically large number of possible sequences of a copolymer using traditional experimental and simulation methods. To address this problem, here, we combine evolutionary computing and coarse-grained molecular dynamics (CGMD) simulation and study the sequence-structure correlations of a model AB-type copolymer in a solution and assess the impact of sequence on the packing density in its bulk phase. The CGMD-based evolutionary algorithm (EA) screens the sequence space of a single chain copolymer efficiently and identifies a wide range of single-molecule structures including extremal radii of gyration. The data are utilized to estimate the Pareto front of the structure-space of a binary copolymer as a function of its composition. The monomer packings in single-molecule solution phase and multimolecular bulk phase are found to be identical. Finally, this work highlights the opportunities of sequence-specific control of macromolecular structure for designing target materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A reactive molecular dynamics model for uranium/hydrogen containing systems
Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. Here, we show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.
36 MATERIALS SCIENCE↗
Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.
36 MATERIALS SCIENCE↗
Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures [Slides]
Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more difficult as the detectors increase in size to reach their physics goals. In liquid argon time projection chambers (TPCs) the charged particles from neutrino interactions produce ionization electrons which drift in an electric field towards a series of collection wires, and the signal on the wires is used to reconstruct the interaction. The MicroBooNE detector currently collecting data at Fermilab has 8000 wires, and planned future experiments like DUNE will have 100 times more, which means that the time required to reconstruct an event will scale accordingly. Modernization of liquid argon TPC reconstruction code, including vectorization, parallelization and code portability to GPUs, will help to mitigate these challenges. The liquid argon TPC hit finding algorithm within the L arSoft framework used across multiple experiments has been vectorized and parallelized. This increases the speed of the algorithm on the order of ten times within a stand alone version on Intel architectures. This new version has been incorporated back into LarSoft so that it can be generally used. These methods will also be applied to other low level reconstruction of the wire signals such as the deconvolution. The applications and performance of this modernized liquid argon TPC wire reconstruction will be presented.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
A computational study of diffusion in a glass-forming metallic liquid
In this study, liquid phase diffusion plays a critical role in phase transformations (e.g. glass transformation and devitrification) observed in marginal glass forming systems such as Al-Sm. Controlling transformation pathways in such cases requires a comprehensive description of diffusivity, including the associated composition and temperature dependencies. In our computational study, we examine atomic diffusion in Al-Sm liquids using ab initio molecular dynamics (AIMD) and determine the diffusivities of Al and Sm for selected alloy compositions. Non-Arrhenius diffusion behavior is observed in the undercooled liquids with an enhanced local structural ordering. Through assessment of our AIMD result, we construct a general formulation for Al-Sm liquid, involving a diffusion mobility database that includes composition and temperature dependence. A Volmer-Fulcher-Tammann (VFT) equation is adopted for describing the non-Arrhenius behavior observed in the undercooled liquid. Furthermore, the composition dependence of diffusivity is found quite strong, even for the Al-rich region contrary to the sole previous report on this binary system. The model is used in combination with the available thermodynamic database to predict specific diffusivities and compares well with reported experimental data for 0.6 at.% and 5.6 at.% Sm in Al-Sm alloys.
36 MATERIALS SCIENCE↗
Autonomous anomaly detection of proliferation in the AGN-201 nuclear reactor digital twin
The expansion of global nuclear power necessitates advanced methods for analyzing proliferation indicators. This study introduces a novel application of the Isolation Forest Machine Learning (IFML) algorithm within a digital twin (DT) of the AGN-201 nuclear reactor to autonomously detect anomalies. Leveraging real-time operational data from the AGN-201 DT, the IFML algorithm identifies outliers without prior data labeling and operates as a lightweight, complementary approach to traditional physics-based anomaly detection methods for nuclear safeguards. In a simulated Red vs. Blue team exercise, the IFML algorithm successfully detected six significant unseen anomalies related to reactivity changes, achieving an accuracy of 99% for identifying operational deviationxs. These anomalies, caused by deliberate perturbations, were detected alongside known physics-based models, underscoring the potential of IFML to enhance real-time monitoring without displacing traditional methods. Further, this study highlights the applicability of IFML in nuclear environments by providing an additional, redundant layer of anomaly detection to improve safeguards and operational safety in complex systems.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems
This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder-decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs with important benchmark problems from computational physics.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗