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John W. Lawson

Publications and source records attributed to John W. Lawson.

At least 19 records
All sourcesNASA NTRSDOE OSTI
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 y' 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 Nb and Cr alloying additions on these processes. The simulation shows that Nb increases the creep resistance which is mostly associated with impeding the reordering of the high energy double complex stacking fault. Cr, on the other hand, promotes twin growth, degrading the high temperature creep properties. These results can help to understand the effects of elemental composition of the alloy on creep resistance.

Ni-based superalloys↗
NASA NTRS2023

A 3D Printable Alloy Designed for Extreme Environments

Multiprincipal-element alloys are an enabling class of materials owing to their impressive mechanical and oxidation-resistant properties, especially in extreme environments. Here we develop a new oxide-dispersion-strengthened NiCoCr-based alloy using a model-driven alloy design approach and laser-based additive manufacturing. This oxide-dispersion-strengthened alloy, called GRX-810, uses laser powder bed fusion to disperse nanoscale Y 2 O 3 particles throughout the microstructure without the use of resource-intensive processing steps such as mechanical or in situ alloying. We show the successful incorporation and dispersion of nanoscale oxides throughout the GRX-810 build volume via high-resolution characterization of its microstructure. The mechanical results of GRX-810 show a twofold improvement in strength, over 1,000-fold better creep performance and twofold improvement in oxidation resistance compared with the traditional polycrystalline wrought Ni-based alloys used extensively in additive manufacturing at 1,093 °C. The success of this alloy highlights how model-driven alloy designs can provide superior compositions using far fewer resources compared with the ‘trial-and-error’ methods of the past. These results showcase how future alloy development that leverages dispersion strengthening combined with additive manufacturing processing can accelerate the discovery of revolutionary materials.

Timothy M. Smith↗
NASA NTRS2022

High-precision predictions of properties of chemically disordered crystals

Multiple scattering theory (MST) combined with density functional theory (DFT) allows to predict properties of chemically disordered materials from the first principles. However, such predictions often suffer from the systematic errors, which depend on crystal geometry. Each computed property of a particular crystal structure typically has a relatively small random error and a larger systematic error. Cancellation of systematic errors allows more accurate predictions. We propose a computational methodology based on the subtraction of the systematic errors in MST. To exemplify it, we apply it to the precipitated alloys. Considering precipitation strengthening in Ni superalloys, we compute the relative enthalpies of the competing Ni_3(Al_{1-x}Ti_x)_1 crystal structures with a chemical disorder on the Al+Ti sublattice. Such predicted composition-structure-property dependencies are useful for the guided design of the next-generation alloys with improved strength. Our predictions are validated by comparison with the results of other DFT methods (having a higher computational cost) and with experiment.

density functional theory↗
NASA NTRS2021

Utilizing local phase transformation strengthening for nickel-based superalloys

Almost 75 years of research has been devoted to producing superalloys capable of higher operating temperatures in jet turbine engines, and there is an ongoing need to increase operating temperature further. Here, a new disk Nickel-base superalloy is designed to take advantage of strengthening atomic-scale dynamic complexions. This local phase transformation strengthening provides the alloy with a three times improvement in creep strength over similar disk superalloys and comparable strength to a single crystal blade alloy at 760 °C. Ultra-high-resolution chemical mapping reveals that the improvement in creep strength is a result of atomic-scale η (D024) and χ (D019) formation along superlattice stacking faults. To understand these results, the energy differences between the L12 and competing D024 and D019 stacking fault structures and their dependence on composition are computed by density functional theory. This study can help guide researchers to further optimize local phase transformation strengthening mechanisms for alloy development.

Timothy M. Smith↗
NASA NTRS2021

Molecular Dynamics Investigation of the Structural and Mechanical Properties of Off-Stoichiometric Epoxy Resins

Molecular dynamic (MD) simulations were performed to compute the mechanical properties of off-stoichiometric epoxy resins as a function of hardener/epoxy mixture ratio (r). Properties were characterized in relation to their microscopic structures. Such resins have been used recently for adhesive-free bonding of large-scale composite structures using the co-curing-ply method. In this process, two partially precured composite panels with hardener-poor (HP) off-stoichiometric resins are coupled with ply(ies) of complementary hardener-rich (HR) formulations and then cured simultaneously. This bonding process has the potential to produce reliable and certifiable composite joints without the need for additional fasteners, which are often required for many conventional bonding methods because even small amounts of contamination can cause a weak bond. The reflow and mixing of the HP/HR resin in this bonding process result in a joint with no discernable interface that should not be susceptible to surface contamination. However, incomplete mixing of the two offset resins may result in chemical heterogeneity of the cured polymeric joint. Thus, different r values may be obtained across the joint. Classical MD simulations were performed to compute the Young’s modulus of polymers with different r values and correlate their properties to network structures. High stiffness was associated with molecular packing due to chemical crosslinking, leading to a single network structure. Moreover, the networks became denser as the ratio approached the stoichiometric value r = 1. Thus, the r = 1 systems were single clusters, with high stiffness, high molecular weight, and a high degree of crosslinking. Structural properties such as radius of gyration and mean square displacement were determined to investigate the variation in the stiffness with respect to r. This MD simulation study was validated with experimental measurements.

Chang Woon Jang↗
NASA NTRSDate not supplied

Ab Initio Simulations of Martensitic Phase Transformations in NiTi-based High Temperature Ternary Shape Memory Alloys: NiTiHf and NiTiZr

Ab initio simulations of phase stability and martensitic phase transitions are performed for NiTi-based ternary shape memory alloys (SMAs). Specifically, we considered NiTiHf and NiTiZr, which are highly studied for high temperature SMA applications. Previously, we performed investigations of ordered NiTi and related binaries [1,2]. However, similar approaches for chemically disordered compounds present additional difficulties. In this work, special quasi-random structures (SQS) were generated for various compositions, x∈[0,0.5], of Ni0.5Ti(0.5-x)Hfx and Ni0.5Ti(0.5-x)Zrx to capture chemical disorder of off-stoichiometric compounds. Phase stability was evaluated through analysis of finite temperature phonon spectra using temperature dependent effective potential (TDEP) method. Free energies for the cubic B2 phase of NiTiHf and NiTiZr were computed using ab initio thermodynamic integration (AITI) developed previously [1,2]. Free energies for monoclinic B19’ and orthorhombic B33 phases were evaluated via quasi harmonic approximations (QHA). Our results show a critical composition (xc) where the three phases of B2, B19’ and B33 meet, i.e. there is a tri-critical point. For x xc, the transition is between B33 and B2, i.e. it is not a shape memory transition. The approach presented here opens the door to ab initio based predictions of MTT for arbitrary ternary SMAs.

Hessam Malmir↗
NASA NTRSDate not supplied

Towards Accurate and Efficient Predictions of Martensitic Transition Temperatures for Shape Memory Alloys from First Principles

Recent rapid progresses in physics theory and computational power have made it possible to predict the martensitic transition temperatures (MTTs) in shape memory alloys (SMAs) from first principles [1-3]. In particular, rigorous while time-consuming thermodynamic integration has been employed to compute the anharmonic phonon free energies, which play a crucial role in determining martensitic phase transitions in SMAs. However, this approach has only been applied to simple binaries, and its accuracy is unsatisfying for certain SMAs such as the most commonly used NiTi. In this work, we report on several new developments to our method that bring first-principles theory and experiment much closer into agreement including the MTT of NiTi, and that improve the computational efficiency significantly. We have applied our refined approach to investigate the Ni0.5Ti0.5-xHfx and PdxNi0.5-xTi0.5 ternaries, and the predicted MTT for each composition is within 100K compared with experiment. We will address various techniques to overcome the difficulty encountered in studying ternaries. Our theoretical approach is expected to be a broadly applicable and predictive theory for designing complex SMAs with desirable properties. [1] J.B. Haskins, A.E. Thompson,and J.W. Lawson, Phys. Rev B 94, 214110 (2016). [2] J.B. HaskinsandJ.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).

Zhigang Wu↗
NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗
NASA NTRSDate not supplied

Mixed-Domain Charge Transport in the S-Se System from First Principles

Lithium-sulfur (Li-S) batteries are emerging systems heralded for the inexpensive cathode component (in S)and higher energy density than that of typical Li-ion batteries [1–5]. For the latter reason in particular, they are drawing high levels of interest for long-range electric air-craft applications where high gravimetric energy density is of utmost importance. Unfortunately, S by itself has a much too low electrical conductivity to be useful asa stand-alone cathode material. One effort to alleviate these shortcomings is to alloy sulfur with selenium [6–13],which is expected to improve the paltry conductivity of S as Se, depending on the phase, is 10∼30 times more conductive. This, however, comes at the cost of the heavier mass of Se that works against the gravimetric energy density. The charge transport behavior in the S1−xSex alloy system, not yet fully known or understood, is necessary for one to navigate the trade-offs between conductivity and other variables such as energy density en route to ultimately determining the optimum Se content, on demand. Charting carrier mobilities and conductivities in the S-Se system throughout the compositional spectrum from first principles at a predictive level is therefore critical.

Junsoo Park↗
NASA NTRSDate not supplied

Towards Accurate and Efficient Predictions of Martensitic Transition Temperatures for Shape Memory Alloys from First Principles

Shape memory alloys (SMAs) can remember and recover their original shapes upon heating due to the existence of a reversible martensitic transition (MT) between the high-temperature austenite (A) and low-temperature martensite (M) phases. The martensitic transition temperature (MTT) is a crucial characteristic of an SMA. SMAs have a wide range of potential applications in aerospace, civil engineering, bioengineering, etc., but their operating temperatures are limited by the available SMAs. MTT can be tuned by alloying a binary with other metals, and the multicomponent NiTi-based SMAs have attracted tremendous research efforts recently. It is not efficient to employ the trial-and-error method alone due to the dramatically increased complexity and possibilities in compositions, and thus reliable theory and accurate computations play an indispensable role in creating SMAs with desirable properties.

Zhigang Wu↗
NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely small errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery of novel SMAs with targeted properties.

Shape Memory Alloys↗
NASA NTRSDate not supplied

Energy landscape in Ni-Co-Cr and related alloys

Among multi-principal element alloys, the NiCoCr middle-entropy alloy has an outstanding combination of strength and ductility at both low and elevated temperatures. Equiatomic NiCoCr is a single-phase alloy with the face centered cubic (fcc) crystal structure. A low stacking fault energy in the fcc matrix is a cause of a relatively low creep in this alloy. The hexagonal close-packed (hcp) structure differs from the fcc by a stacking of atomic layers. The energy difference between the hcp and fcc structures is known to correlate with the stacking fault energy in the fcc phase. We compute formation and relative structural energies versus composition in the Ni-Co-Cr ternary and related quaternary systems, discuss possibilities of compositional adjustments, and compare theoretical predictions with experiment. We acknowledge funding by NASA’s Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.

Multiscale↗
NASA NTRSDate not supplied

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery↗
NASA NTRSDate not supplied

Molecular dynamics simulation of effects of solutes on dislocation propagation in Ni-based superalloys

Ni-based superalloys are used in the hot sections of jet turbine engines because of their high strength, stability and resistance to oxidation. The properties of these alloys can be further optimized by adding alloying elements. Therefore, a fundamental knowledge on the effect of different elements on properties of Ni-based superalloys is required. Molecular dynamics simulation could shed light here but its application is hindered by the absence of reliable and computationally cheap semi-empirical potential of the interatomic interaction for 4 and more element alloys. We will present a new Ni-Al-Cr-Nb Finnis-Sinclair (FS) potential specially designed to simulate the dislocation propagation from to  phase. In order to construct this potential, we designed a special algorithm to incorporate the data on element partitioning in the potential development procedure. For example, it is known from experiment, that Cr is mostly present in the gamma phase. Figure 1 shows a snapshot obtained after equilibration of the model of the Ni68Al17Cr15 alloy at T=1000 K using the hybrid Monte-Carlo (MC)/molecular dynamics (MD) simulation with the developed semi-empirical potential. One can clearly see that the Cr partitioning is in agreement with the experimental data. We will discuss the developed algorithm to incorporate the solute partition data in details. Using the developed semi-empirical potential, we first investigated the effect of anti-site defects in the  phase on the single dislocation propagation. It was found that the dislocation velocity increases with the increasing of the anti-site defect concentration. This effect was attributed to smaller number of Al-Al pairs forming during the dislocation migration in the presence of the anti-site defects. Next, we investigated the effect of Nb on the dislocation pair propagation in the Kolbe mechanism. It was found that the addition of Nb leads to considerable decrease in the dislocation propagation rate. This is in agreement with the experimental data on the effect of Nb on the creep resistance of the Ni-based superalloys. We will discuss the origin of this effect.

Mikhail I. Mendelev↗
NASA NTRSDate not supplied

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery↗
NASA NTRSDate not supplied

Influence of Microstructural Features on Austenite-Martensite Interfaces in NiTi Shape Memory Alloys

Shape memory alloys (SMAs) exhibit several unique thermomechanical properties due to a reversible martensitic phase transformation. A key aspect of this transformation is the austenite-martensite interface. Here we present a molecular dynamics (MD) simulation methodology to study the atomic-scale features of austenite-martensite interface migration under near-equilibrium conditions. In single crystals, the interfaces migrate rapidly with only a small thermodynamic driving force. In polycrystals, however, interface migration is significantly impeded due to the change in orientation relationship at grain boundaries and the stored elastic energy resulting from microstructural constraints. This behavior can be linked to several mechanisms associated with transformation width and hysteresis in SMAs, properties of great importance for applications involving actuation. Additionally, we will present preliminary MD simulation results on the influence of precipitates on the formation and migration of austenite-martensite interfaces.

Gabriel Plummer↗
NASA NTRSDate not supplied

Ab-Initio Study of Stacking Fault Segregation Behavior in Ni-based Superalloys at High Temperatures

Ni-based superalloys demonstrate extraordinary creep properties compared to traditional metallic systems due to the formation of L12 (γ’) precipitates. Despite experimental evidence of alloying element segregation to the stacking faults, prior ab-initio efforts indicated repulsion of Nb from superlattice intrinsic stacking faults (SISFs) . Due to the importance of this element in key strengthening mechanisms, we developed a new technique to account for multi-component segregation behavior at temperature combining ab-initio molecular dynamics (AIMD) and Monte-Carlo (MC) simulations. Using this technique, we replicated experimentally observed segregation behavior of Nb, Co, and Ti to the SISF in L12 (Figure 1). Furthermore, we investigated the co-segregation behaviors of individual pairs of alloying elements to determine the interaction effects which lead to this segregation behavior in the full alloy system. By simulating the reduced-chemistry models using our approach, we reconciled previous ab-initio calculations demonstrating repulsion of Nb from the SISF and the experimentally observed segregation behavior.

Molecular Dynamics↗