Engineering topics

John W Lawson

Publications and source records attributed to John W Lawson.

At least 19 records

NASA NTRS2026

Diffusion Quantum Monte Carlo Calculation of the Austenite and Martensite Phases of NiTi

NiTi is a promising material for smart and active technologies due to its exhibition of the shape memory effect, superelasticity, and biocompatibility. The shape memory effect is tied to the reversible transition between the austenite and martensite phases. A major research direction is to alloy NiTi with Zr, Hf, Pd, Pt, etc., in order to tune the martensitic transition temperature (MTT). Modeling the MTT from first principles is challenging because the lattice dynamics is complicated by anharmonicity and various low-energy structures. Using density functional theory, the energy difference between the austenite and martensite phases of NiTi varies by up to 100 meV/atom depending on the choice of density functional, which is of the same order of the energy difference itself. Consequently, free energy calculations with different functionals can result in estimates of the MTT that vary by several hundred K. Using diffusion quantum Monte Carlo, we calculated the energy difference between the B2 and B19' structures of NiTi to be 70.9 +- 2.5 meV/atom.

Kevin K Ly↗

NASA NTRS2024

Effects of Alloying Elements on Twinning in Ni-Based Superalloys

Micro-twinning is the major creep deformation mechanism in Ni-based superalloys at temperatures above 700 C. Recent experiments suggest that superlattice stacking faults in   phase may serve as the precursors to twin formation. Segregation of alloying elements to these precursors may have a significant effect on formation and extension of micro-twins. Using atomistic modeling we investigate and explain the effects of Cr and Nb alloying additions on these processes. Our results help to rationalize the experimentally observed puzzling effects of elemental composition of the alloy on creep resistance.

Ni-based superalloys↗

NASA NTRS2024

Molecular Dynamics Simulations of Microstructural Effects on Austenite-Martensite Interfaces in NiTi

Formation and migration of austenite-martensite interfaces plays the key role in reversible martensitic transformations of shape memory alloys (SMAs). How these interfaces interact with the SMA microstructure is a primary determining factor in important functional properties such as hysteresis and transformation span. As such, successful microstructural engineering of SMAs requires in-depth knowledge of interface behavior. The rapid nature of martensitic transformations makes experimental observations of moving austenite-martensite interfaces challenging. Molecular dynamics (MD) simulation is a unique tool which can probe the atomic-scale details of austenite-martensite interfaces as they migrate through different microstructures. However, in focusing on the entire transformation process, including the nucleation of new phases, MD studies are usually performed so far from equilibrium that their relevance to experiment is questionable. Here, we demonstrate new MD techniques to generate energetically preferred austenite-martensite interfaces in NiTi under near-equilibrium conditions. The interfaces are semi-coherent, exhibiting a series of structural disconnections, and they can migrate rapidly through single crystals under only small thermodynamic driving forces. In contrast, when interfaces migrate in polycrystals, their motion is impeded by thermoelastic effects as well as changes in orientation relationships at grain boundaries. Microstructures which accumulate large amounts of elastic energy tend to release some fraction through irreversible, hysteresis-inducing mechanisms. We demonstrate that engineering microstructures with less constraints is a viable strategy to produce SMAs with reduced hysteresis and transformation span. Similar thermoelastic and hysteresis-inducing mechanisms also arise when austenite-martensite interfaces encounter precipitates and can be controlled by tuning characteristics of the precipitate distribution.

Gabriel Plummer↗

NASA NTRS2024

Martensitic Phase Transition in Complex NiTi-Based Shape Memory Alloys from First-Principles Calculations

Recent rapid progresses in physics theory and computational power have made it possible to accurately predict the phase transitions and martensitic transition temperatures (MTTs) in shape memory alloys (SMAs) from first principles. However, previously theory and calculations [1-4] were applied only to study highly ordered stoichiometric binary alloys such as NiTi, PdTi and NiHf. Here we report on our recent first-principles investigations [5,6] on Ni 0.5 Ti 0.5-x Hf x and Pd x Ni 0.5-x Ti 0.5 ternaries and off-stoichiometric NiTi, and the predicted martensitic phase transitions in these complex SMAs are in good agreement with experimental findings. In particular, the calculated MTTs for all these compositions are within 100 K compared with the corresponding measured data, and our results also reveal the origin of the striking asymmetry in MTT of the off-stoichiometric NiTi near equiatomic compositions. We will address various techniques to overcome the difficulty encountered in studying ternaries and off-stoichiometic binaries associated with disorder and/or much lowered symmetry. Our theoretical approach is expected to be a broadly applicable and predictive theory for designing complex SMAs with desirable properties. References: [1] J. B. Haskins, A. E. Thompson, and J. W. Lawson, Phys. Rev B 94 , 214110 (2016). [2] J. B. Haskins and J. W. Lawson, J. App. Phys. 121 , 205103 (2017). [3] J. B. Haskins, H. Malmir, S. J. Honrao, L. A. Sandoval, and J. W. Lawson, Acta Materialia 212 , 116872 (2017). [4] Z. Wu, J. W. Lawson, and O. Benafan, Phys. Rev. B 106 , L140102 (2022). [5] Z. Wu, H. Malmir, O. Benafan, and J. W. Lawson, Acta Materialia 261 , 119362 (2023). [6] Z. Wu, J. W. Lawson, and O. Benafan, Phys. Rev. B 108 , L140103 (2023).

Zhigang Wu↗

NASA NTRS2023

Molecular Dynamics Simulations of Austenite-Martensite Interfaces in NiTi Shape Memory Alloys

The unique properties of shape memory alloys (SMAs) arise from a reversible martensitic transformation. The nucleation and migration of austenite-martensite interfaces are the key to understanding the SMA properties. Molecular dynamics (MD) simulations can provide important atomic-scale information about these aspects of the transformation, but their time scales prevent the interface formation under near-equilibrium conditions relevant to experiment. We present a new MD methodology which allows for the natural formation of energetically preferred austenite-martensite interfaces under near-equilibrium conditions. Our simulation demonstrates that the interfaces in NiTi are semi-coherent, composed of a series of terrace planes and structural disconnections, and they migrate rapidly through single crystals with only a small thermodynamic driving force. In bi-crystals and polycrystals, the migration of these same interfaces is significantly impeded by grain boundaries and stored elastic energy. This behavior can result in SMA hysteresis via several mechanisms associated with nucleation and non-elastic strain accommodation.

Gabriel Plummer↗

NASA NTRS2022

Free Energy Landscapes for Elucidating the Structural Consequences of Exon-20 mutations on the ErbB Family of Protein Kinases

The ErbB family of protein kinases plays an important role in major cellular functions and consequently mutations in the functional regions of these proteins are implicated in several types of cancer growths. To envision rational design of small molecule drugs that target the diseased proteins it is important to quantify the structural effects of the mutations, as some of these mutants render the protein resistant to tyrosine kinase inhibitors (TKIs). Herein we use advanced sampling techniques and long-timescale molecular dynamics simulations to predict the effect of major exon 20 mutations on the ErbB family, specifically EGFR and HER2 proteins. Exon 20 mutations have been clinically known to induce TKI resistance, though the mechanisms of such an effect is poorly understood. By mapping out the free energy landscape of the mutants and comparing them against the wild-type, we elucidate the structural differences in the binding pocket region that alter the nature of drug-protein interactions. We believe that these insights will play a pivotal role in developing small molecule drugs that overcome the TKI resistance.

Ashwin Ravichandran↗

NASA NTRS2021

Predicting the Functional State of Protein Kinases Using Interpretable Graph Neural Networks

Kinases are a family of proteins that function as molecular switches, regulating several essential cellular activities such as cell proliferation. Dysfunctional kinases are implicated in several types of cancers and hence they are actively pursued as drug targets. Given the vast number of complex kinase structures that are available in the protein data bank (PDB), there is a necessity to develop methodologies that can identify structurally important moieties of the kinases in an automated fashion, for such techniques can be instrumental in identifying novel drug targets. In this work, we develop a graph neural network (GNN) based deep learning framework for classifying the functionally active and inactive states of a large set of eukaryotic protein kinases, making use of their 3D structure from the PDB. We show that GNN based machine learning models can classify protein states with an accuracy greater than 97%. We further use the GNN models to automatically identify regions of the kinases that are important for its function. For this purpose, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented on the protein graphs. Remarkably, Grad-CAM consistently identifies the highly conserved DFG motif as the most important part of the protein across the entire kinome, without any prior input. Other regions of the hydrophobic core such as the HRD motif were also identified by the interpretable GNN framework, consistent with the literature. We discuss the significance of each of these regions in detail.

Ashwin Ravichandran↗

NASA NTRS2021

Molecular dynamics simulations of ultrafast radiation induced melting at metal–semiconductor interfaces

Metal–semiconductor contacts in silicon carbide (SiC) diodes endure damages at the interface when exposed to harsh radiation environments. Due to the rapid rise in temperature and ultrafast cooling that follows the radiation impact, the structural properties of the materials can be altered through melting, recrystallization, and amorphization. A detailed understanding of the material failure modes at the interface is lacking, specifically at the nanoscale. We use molecular simulations to investigate the ultrafast melting at tungsten (W)–SiC interfaces following radiation damage and apply deep learning techniques to track the transient evolution of the local molecular structures. We show that W near the radiation track undergoes melting and, eventually, most of it recrystallizes with a noticeable degree of undercooling, while SiC is rendered permanently amorphous. The observation of local undercooling in W films is important as it can affect the device performance even before the bulk melting temperature of the material is reached. We also show that at high temperatures, the interface undergoes a fracture-like failure. The results presented here are significant in understating the different failure modes of SiC diode materials.

Ashwin Ravichandran↗

NASA NTRS2021

Anion-Assisted Delivery of Multivalent Cations to Inert Electrodes

To understand and control key electrochemical processes - metal plating, corrosion, intercalation, etc. requires molecular-scale details of the active species at electrochemical interfaces and their mechanisms for de-solvation from the electrolyte. Using free energy sampling techniques we reveal the interfacial speciation of divalent cations in ether-based electrolytes and mechanisms for their delivery to an inert graphene electrode interface. Surprisingly, we find that anion solvophobicity drives a high population of anion-containing species to the interface that facilitate the delivery of divalent cations, even to negatively charged electrodes. Our simulations indicate that cation desolvation is greatly facilitated by cation-anion coupling. We propose anion solvophobicity as a molecular-level descriptor for rational design of electrolytes with increased efficiency for electrochemical processes limited by multivalent cation desolvation.

Electrochemical interfaces↗

NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗

NASA NTRS2020

Toward Realizing the Potential of Practical Li-O2 Batteries for Electric Aircraft

Electrochemical power sources based on the Li-O2 couple have potential to achieve more than two-times the specific energy that is achievable with state-of-the-art Li-ion technologies. Despite considerable research, significant practical limitations continue to impede the successful realization of the Li-O2 electrochemistry as a power source in electric and hybrid-electric aviation. In practice, power capability and cycle life present formidable challenges. In this work, projections are presented for Li-O2prismatic cells with lightweight construction and the effect of cathode-limited capacity and discharge rate on specific energy and power are presented.

William R Bennett↗

NASA NTRS2020

Computational Design of Eutectic Molten Salt Mixtures: What Can Thermodynamic Models Do?

In the search for efficient energy storage battery technologies, designing stable electrolytes has been a long-standing challenge. Electrolytes based on molten salt eutectics are known for their stability with minimum parasitic reactions when compared to their widely used organic counterparts. However, the operating temperatures of these molten salt electrolyte-based batteries are dictated by the melting point of the eutectic mixtures. Design and high throughput screening of low melting temperature eutectic molten salt mixtures have been hindered by the lack of computational models. In this work, we develop thermodynamic models to predict the eutectic points of several molten salt mixtures. The framework of the COSMO-SAC model is used for the predictions and is compared with experimental data and other thermodynamic approaches. Rapid thermodynamics-based approaches, as shown in this study, can accelerate the discovery of new materials, complementing experimental techniques.

Ashwin Ravichandran↗

NASA NTRS2020

Ultrafast Melting at Metal-Semiconductor Interfaces: A Molecular Dynamics Study

Quantifying the thermodynamics of interfacial melting is important for several applications such as laser machining and designing radiation hardened electronic devices. Due to the short time and length scales that are involved in the process, molecular simulations provide an ideal path for understating the interfacial melting phenomena. In this work we investigate tungsten (W) – silicon carbide (SiC) interfaces which are relevant in modern high-power electronic devices. Classical molecular dynamics simulations along with the two-temperature model (TTM) were used to study the radiation induced melting of the interface while a neural network-based machine learning framework was used to track the transient re-crystallization process. The damage endured by the semiconductor at the interface following the radiation event is discussed in detail while the re-crystallization of the metal is described in terms of the classical nucleation theory.

Ashwin Ravichandran↗

NASA NTRSDate not supplied

Fracture Prediction of Epoxy Resin using Morse Bond Potential Embedded in GAFF using Molecular Dynamics Simulations

A generic method is developed to investigate the fracture behavior of an epoxy resin using atomistic molecular (MD) dynamics simulations. The epoxy system consisted of the stoichiometric mixture of tetra-/tri-functional epoxies and di-functional hardener molecules and was cured with a step-growth crosslinking algorithm. A hybrid force field (hFF) in which Morse bond function were added in second generation-general Amber Force Field (GAFF2) was used to capture bond breaking of the crosslinked epoxy system under uniaxial deformation. The Morse bond parameters for covalent bonds in the crosslinked backbone were fit to dissociation curves computed by CASPT2/6-311+G**. After the systems in various crosslink densities were obtained and equilibrated, the full length of stress-strain (𝜎-𝜀) curve was produced including initial elastic regime, yielding, plastic flow, strain hardening, and progressive failure. We found that the hFF was effective in studying the dissociation of crosslinked polymers and understanding the failure mechanism. In the results, the hFF delivered the same elastic property obtained from the unmodified force field GAFF. The stress-strain curve, however, began to be distinct in the plastic regime due to broken covalent bonds capable of altering the tensile behavior. Once the stress reached maximum, it progressively decreased until the ultimate failure (𝜎=0). Since hFF employed the existing GAFF functions with including a minimal change of its bond potential, this approach is easier to perform and computationally more efficient for unveiling the fracture behavior of polymer materials at the molecular level than other approaches with reactive force fields. We expect this approach will be utilized to accelerate the material-by-design process for thermosets by incorporate data from molecular models.

Changwoon Jang↗

NASA NTRSDate not supplied

Computational Tools to Design Molten Salt Electrolytes for High-Temperature Environments

Current Li-ion batteries are designed for a small operating window of 5 °C to 55 °C. Modifications to the electrolyte for operation in Mars atmosphere extended this range on the lower bound to ~ -40 °C. These operating-temperature ranges are far from those presented in high-temperature environments, such as the Venus surface, where temperatures are around 450 °C. Protecting the state-of-the-art Li-ion batteries require insulations that decreases the volumetric capacity and limiting the operational time-window. These unique challenges require a paradigm shift in materials used for designing high-temperature batteries. Molten salt electrolyte-based batteries offer a plausible route to designing high-temperature batteries. ZEBRA batteries are known to be one of the safest energy storage devices operating at 270-350 °C. Nitrate based eutectics, operating at 150 °C have also been used in Li-O2 batteries. In this study, we will examine molten-salt electrolyte transport and electrochemical properties using first-principles computations and benchmark against experiments. Further, we will present thermodynamics-based models for designing and predicting melting point of molten salt mixtures. Based on these computational tools, new molten salt mixtures designed with desired operating temperatures and electrochemical windows will be presented. Implications of these new eutectics in the context of high-temperature environment exploration will also be discussed.

Balachandran Radhakrishnan↗

NASA NTRSDate not supplied

Elucidation of Electrolyte Decomposition Pathways in Li--O2 Batteries using Ab Initio Computations

Li-O2 batteries offer exciting possibilities for energy-storage applications requiring higher energy content than current Li-ion battery technologies. However, development of commercial Li-O2 batteries faces many challenges including electrolyte decomposition in order to realize the full potential of this emerging technology. In this work, we present ab initio calculations that probe the role of reactive oxygen species including singlet oxygen, superoxide, and peroxides on electrolyte decomposition for potential organic electrolytes for use in Li-O2 batteries. We investigate the influence that the harsh electrochemical environment near the cathode plays in promoting electrolyte decomposition by using increasingly detailed computational models. Our results highlight existing problems with organic electrolytes for Li-O2 battery development and suggest potential design strategies for reducing electrolyte decomposition.

Li-O2 Batteries↗

NASA NTRSDate not supplied

Computational Approaches for Li-O2 Battery Design

Threshold energy densities for general aviation electric aircraft are 400 Wh/kg with more ambitious air vehicles having significantly higher requirements. Li-O2 batteries, with the highest theoretical capacity, are one of the few “beyond Li-ion” chemistries that might satisfy the extraordinary specific capacity as well as specific power requirements of electric aircraft. However, side reactions at interfaces, in particular at the cathode, over the charge-discharge cycles result in very short cycle-life and dramatic reduction of capacity. Addressing these issues, in addition to others, are crucial for realizing practical, high performance Li-O2 batteries. In this talk, we discuss atomistic computational work to understand and mitigate some of the issues, including those at interfaces, that affect the Li-O2 electrochemistry. To start, we discuss the deposition mechanisms, both surface and solution based, of Li2O2 and their dependence on external potential. Next, we explore molten salt electrolytes as a stable alternative to organic electrolytes and approaches taken to develop new practical molten salt eutectic mixtures. Finally, we address the issue of reactive carbon-cathodes and possible cathode-candidates that were identified throughput high-throughput computations.

Li-Air battery↗

NASA NTRSDate not supplied

Molten Salt Electrolytes: Computational Analysis of Transport, Electrochemical Properties and Designing New Mixtures

Electric aircraft propulsion has gained a traction over the last decade due to possible high-energy density electrochemistries with reasonable cycle life and identification of non-flammable chemistries that can guarantee much better safety. Commercial Li-ion batteries cannot reach such high capacities because of weight limitations that arise from the widely used intercalation electrodes. Also, use of organic electrolytes make them highly susceptible to fire on exposure to air and humidity. Currently, use of Lithium metal anode along with conversion electrochemistries such as Li-O2 and Li-S are being pursued to achieve such high energy densities. Safer inorganic solid-state electrolytes, recently discovered Water-in-Salt electrolytes, molten salt electrolytes are some of the alternatives being pursued for a safer/non-flammable battery. Of these safer alternatives, molten-salt electrolytes offer some attractive properties: liquid at operating temperatures resulting in better electrode-wetting, stable interface with Li-metal anodes and good ionic conductivity; their only drawback being high operating temperatures. In this talk, we will discuss some of the computational studies, driven via atomistic simulations, of the properties of molten-salt electrolytes including transport mechanism and interface stability. We will also discuss predicting electrochemical properties of these high-temperature electrolytes. Further, we will discuss a thermodynamic approach to predicting new molten-salt eutectics with lower melting points.

Molten Salt Electrolytes↗