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Results for “predictive thermodynamics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules

The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were, however, tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys, machine-learning methods showed that the formation of solid solutions can be very accurately predicted (93%). The machine-learning results help identify the most important features, such as molar volume, bulk modulus, and melting temperature. As such a new thermodynamics-based rule was developed to predict solid–solution alloys. The new rule is nonetheless slightly less accurate (73%) but has roots in the physical nature of the problem. The new rule is employed to predict solid solutions existing in the three blocks, each of which consists of 9 elements. The predictions encompass face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal closest packed (HCP) structures in a high throughput manner. The validity of the prediction is further confirmed by CALculations of PHAse Diagram (CALPHAD) calculations with high consistency (94%). Since the new thermodynamics-based rule employs only elemental properties, applicability in screening for solid solution high-entropy alloys is straightforward and efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Computational methods in solution-based plastics purification

Plastic waste can be recycled into resins with near-virgin properties by solution-based purification processes that selectively dissolve polymers, remove contaminants, or detach printing residues. Here, in this review, we examine computational methods for predicting the behavior governing solution-based plastic purification, motivated by the vast polymer–solvent–contaminant compositional space. We discuss thermodynamic and machine learning methods for predicting polymer–solvent and polymer–contaminant interaction and review physics-based molecular dynamics simulations that resolve molecular-scale phenomena within polymer matrices inaccessible to screening methods. We highlight how these methods have informed experimental design for dissolution-based recycling and solvent-based contaminant removal. Finally, we discuss the prospective role of agentic AI in integrating these computational tools with real-time sorting data to adapt purification conditions to the compositional variability of real post-consumer feedstocks. This review charts a path toward computationally guided solution-based purification workflows that can respond to the complexity inherent in plastic waste streams.

Altamimi, Ali [Univ. of Wisconsin, Madison, WI (Un↗
DOE OSTI2020

Enzyme activities predicted by metabolite concentrations and solvent capacity in the cell

Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization–reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric. As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge. The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions. The approach is demonstrated for three pathways in the central metabolism of E. coli (gluconeogenesis, glycolysis-tricarboxylic acid (TCA) and pentose phosphate-TCA) that each require different regulation schemes. It is shown quantitatively that hexokinase, glucose 6-phosphate dehydrogenase and glyceraldehyde phosphate dehydrogenase, all branch points of pathways, play the largest roles in regulating central metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction

Background: G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies. Results: G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R 2 of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans. Conclusion: G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Influence of fixed charge concentration and water uptake on ion sorption in AMPS/PEGDA membranes

Water content and charge density strongly influence ion sorption in charged membranes. However, isolating the individual effect of each parameter on ion sorption can be difficult due to experimental challenges associated with independently controlling these two variables during membrane synthesis. This frustrates development and validation of fundamental models and structure/property relationships. In this study, three series of uncharged and negatively charged hydrogel membranes were prepared using poly(ethylene glycol) diacrylate (PEGDA) as a cross-linker and 2-acrylamido-2-methylpropanesulfonic acid (AMPS) as an ionic monomer. Here, water content and charge density were controlled by varying AMPS content and PEGDA chain length. Ion sorption in these materials was measured using aqueous NaCl solutions at concentrations ranging from 0.01 to 1.0 M. At constant charge density, salt sorption increased as water content increased. At fixed water content, salt sorption decreased as charge density increased. Salt sorption in membranes with the highest water content and charge density was predicted using the Donnan/Manning model, which accounts for solution and membrane phase thermodynamic non-idealities. However, this approach could not predict sorption in weakly charged and uncharged membranes, presumably due to additional membrane phase thermodynamic non-idealities associated with ion solvation and uncharged polymer segments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Creep Behavior and Phase Equilibria in Model Precipitate Strengthened Alumina-Forming Austenitic Alloys

Abstract Creep-rupture behavior and microstructural response in alumina-forming austenitic (AFA) alloys with two different precipitation strengthening mechanisms, “Laves-phase + M 23 C 6 carbide” and “coherent L1 2 γ′-Ni 3 (Al,Ti),” were explored as “model” cases of multi-phase, multi-scale heat-resistant AFA alloys for 650–750°C use. These alloys will be used to guide and verify computational alloy design and life-prediction modeling under an on-going eXtremeMAT project through the Office of Fossil Energy and Carbon Management, US Department of Energy. Computational thermodynamics were used to design and predict the amounts of strengthening and deteriorating secondary phases at 750°C. Creep-rupture lives of the alloys tested at 750°C and 100 MPa were in a range of 4000–9000 h, and the microstructure at the gage/grip after creep-rupture testing was compared with isothermally aged alloys for 1500 h, as well as the calculated phases. Detailed microstructure characterization includes phase identification, volume fraction measurement, and compositional analysis, which were correlated with the creep-rupture properties. High-temperature oxidation resistance was also screened and compared with commercial, chromia-forming heat-resistant steels. These model alloys also provide the basis for further design and optimization of next generation AFA alloys with improved creep resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.

Dudchenko, Alexander V↗
DOE OSTI2022

Redox Defect Thermochemistry of FeAl 2 O 4 Hercynite in Water Splitting from First-Principles Methods

Solar thermochemical hydrogen (STCH) production is a promising route to produce fuels from sunlight via high-temperature water splitting. However, efficient and technologically viable implementations of this process only allow a narrow window of thermodynamic boundary conditions that can be used to cycle the system, thus limiting the design space for suitable metal oxide redox mediators. An oxygen defect redox mechanism can contribute a favorable reduction entropy to expand this window, and computational evaluation of materials with high oxygen defect entropies could play a pivotal role in guiding the discovery and design of suitable oxides. This study employs first-principles calculations to investigate the redox mediating defect mechanism of the STCH candidate material, hercynite (FeAl 2 O 4 ). We compare the results of total energy calculations from density functional theory (DFT) with beyond-DFT approaches, including hybrid functionals and the random phase approximation, which are among the most advanced methods currently feasible for supercell defect calculations. Using the predicted formation energies, we perform thermodynamic modeling of FeAl2O4 reduction and oxidation via free energy minimization that incorporates ideal gas, configurational, and vibrational entropy contributions evaluated within the quasi-harmonic approximation. Special attention is devoted to understanding interactions among co-existing defects, such as the association of pairs and complexes of O vacancies and cation antisite defects, and the effect of mutually compensating defect charges. Our results corroborate the notion that the details of defect interactions can be decisive for the viability of hydrogen production within the desirable STCH process window.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Favorable Redox Thermodynamics of SrTi 0.5 Mn 0.5 O 3–δ in Solar Thermochemical Water Splitting

Two-step, solar thermochemical splitting of water using nonstoichiometric redox-active metal oxides has emerged as an intriguing approach for large-scale hydrogen production. Perovskites have been proposed as alternatives to state-of-the-art fluorite CeO 2–δ because of their potential for lowering reduction temperature while maintaining high fuel productivity. Guided by computational insights, we explore the thermodynamic properties and water splitting efficacy of the cubic perovskite SrTi 0.5 Mn 0.5 O 3–δ (STM55). Thermogravimetric analysis is performed under controlled oxygen partial pressures (pO 2 ) and temperatures up to 1500 °C, from which both the enthalpy and entropy of reduction as a function of oxygen nonstoichiometry are determined. STM55 provides an attractive combination of moderate enthalpy, 200–250 kJ (mol-O) –1 , and high entropy, with unusual δ dependence. Using a water splitting cycle in which the material is thermally reduced at 1350 °C (pO 2 , ~10 –5 atm) and subsequently exposed to steam at 1100 °C (steam partial pressure of pH 2 O = 0.4 atm), we demonstrate a hydrogen yield of 7.4 mL g –1 . Through both half-cycles, the material remains largely in quasi-equilibrium with the gas phase, as reflected in the agreement of the measured data with predicted profiles based on the thermodynamic data. This behavior indicates rapid surface and bulk diffusion kinetics. Cyclic operation showed the material to be free of degradation and always resulted in a 2:1 yield of H 2 /O 2 . Altogether, STM55 provides outstanding performance characteristics for thermochemical hydrogen production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning

Aqueous phase heterogeneous catalysis is important to various industrial processes, including biomass conversion, Fischer–Tropsch synthesis, and electrocatalysis. Accurate calculation of solvation thermodynamic properties is essential for modeling the performance of catalysts for these processes. Explicit solvation methods employing multiscale modeling, e.g., involving density functional theory and molecular dynamics have emerged for this purpose. Although accurate, these methods are computationally intensive. This study introduces machine learning (ML) models to predict solvation thermodynamics for adsorbates on a Pt(111) surface, aiming to enhance computational efficiency without compromising accuracy. In particular, ML models are developed using a combination of molecular descriptors and fingerprints and trained on previously published water–adsorbate interaction energies, energies of solvation, and free energies of solvation of adsorbates bound to Pt(111). These models achieve root mean square error values of 0.09 eV for interaction energies, 0.04 eV for energies of solvation, and 0.06 eV for free energies of solvation, demonstrating accuracy within the standard error of multiscale modeling. Feature importance analysis reveals that hydrogen bonding, van der Waals interactions, and solvent density, together with the properties of the adsorbate, are critical factors influencing solvation thermodynamics. Furthermore, these findings suggest that ML models can provide rapid and reliable predictions of solvation properties. This approach not only reduces computational costs but also offers insights into the solvation characteristics of adsorbates at Pt(111)–water interfaces.

Adsorption↗
DOE OSTI2026

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential

Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with datasets from on-the-fly active learning achieve quantum mechanical level accuracy when employed in molecular dynamics simulations to predict wide-ranging thermodynamic, structural, and vibrational properties. The simulated phase diagram (500~1400 K and 0~16 GPa) agrees with experimental measurements. The SGP-based MD simulations also successfully predicted that the B13 defect is critical in stabilizing β-B below 700 K. At higher temperatures, the entropy becomes the dominant factor, making β-B the more stable phase over α-B. Furthermore, this Letter demonstrates that SGP potentials based on a training set consisting of defect-free-only systems could make correct predictions of defect-related phenomena in solid-state crystals, paving the path to investigate crystal phase stability and transitions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Oxidation Lifetime Modeling of 625 and 120 Foils After Long-term Exposure in Flowing Air + 10% ${\hbox {H}_2\hbox {O}}$ at 700 and 800 $^\circ$C

A model describing oxidation kinetics considering simultaneous oxidation and volatilization of external oxide scales was integrated with a CALPHAD-based coupled thermodynamic and kinetic approach to predict the oxidation-induced lifetime of foils of alloys 625 and 120. Long-term exposures in flowing air +10% H 2 O at 700 and 800 °C under two flow velocities were used to validate the modeling results. The time to a critical Cr concentration of 10 wt% at the oxide/alloy interface, t 10 , was shown to correlate with the breakdown of the Cr 2 O 3 scale for the studied experimental conditions. Finally, the modeling approach was extended to consider time-dependent oxidation and volatilization rate constants to develop a more appropriate lifetime criterion for microturbine recuperator foils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Effect of substitutional doping and disorder on the phase stability, magnetism, and half-metallicity of Heusler alloys

Spintronics is the fast growing field that will play a key role in optimizing power consumption, memory, and processing capabilities of nanoelectronic devices. Heusler alloys are potential candidates for application in spintronics due to their room temperature (RT) half-metallicity, high Curie temperature, low lattice mismatch with most substrates, and strong control on electronic density of states at Fermi level. In this work, we investigate the effect of substitutional doping and disorder on the half-metallicity, phase stability, and magnetism of Heusler alloys using density functional theory methods. Our study shows that electronic and magnetic properties of half/full-Heusler alloys can be tuned by changing electron-count through controlled variation of chemical compositions of alloying elements. We provide a detailed discussion on the effect of substitutional doping and disorder on the tunability of half-metallic nature of Co 2 MnX and NiMnX based Heusler alloys, where X represents group 13–16 and period 3–6 elements of the periodic table. Based on the idea of electron count and disorder, we predicted a possible existence of thermodynamically stable half-metallic multicomponent bismuthides, for example, (CuNi 3 )Mn 4 Bi 4 and (ZnNi 7 )Mn 8 Bi 8 , through substitution doping at Ni site by specific Cu and Zn composition in half-Heusler NiMnBi. We believe that the design guide based on electron-counts presented for half-metals will play a key role in electronic-structure engineering of novel Heusler alloys for spintronic application, which will accelerate the development and synthesis of novel materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Experiments on a vapor compression air conditioner with liquid desiccants for efficient dehumidification

Buildings require air conditioning systems that not only cool and dehumidify supply air but also provide sufficient ventilation to ensure indoor air quality and occupant comfort. However, standard recirculation systems-which introduce about a 10 % to 20 % fraction of outdoor air-often fail to deliver air that is precisely cooled and dry, particularly because 80-90 % of the ventilation cooling load is latent. Mixing humid ventilation air with recirculated indoor air increases the energy and costs required to condition the air to comfortable levels. Dedicated outdoor air systems (DOASs) are designed to handle this latent dominated ventilation load and thus need to have efficient humidity removal. Many cooling cycles can perform this task. Here we describe a liquid desiccant DOAS, which combines a vapor compression cycle and a liquid desiccant absorber and desorber pair. We present its performance at 26 operating conditions and a thermodynamic model which can accurately predict the moisture removal efficiency. The model's performance predictions have a mean percentage error of 2.5 % and a coefficient of variation of the root mean square error of 7.5 %. We also compare the performance of this vapor-compression-coupled liquid desiccant system with a standard vapor compression system with the same components but no liquid desiccant. For the 26 conditions tested in this study, this comparison shows that adding liquid desiccants lowers the required evaporator cooling load by 21 %, allows for 25 % lower compressor volumetric capacity, and 25 % lower electricity use. Future work will leverage this model to quantify the reduction in annual electricity use across different climates, including the need for a standard vapor compression system to reheat the air during some of the year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Atomic-scale mechanism of carbon nucleation from a deep crustal fluid by replica exchange reactive molecular dynamics simulation

Here we present a mechanistic model of carbon nucleation and growth from a fluid at elevated temperature (T) and pressure conditions, typical of those found in the shallow Earth’s lithosphere. Our model uses a replica exchange reactive molecular dynamics framework in which molecular configurations are swapped between adjacent T replica at regular intervals according to underlying statistical mechanics. This framework allows predicting complex molecular structures and thermodynamics while remaining computationally efficient. Here we simulate the reactivity of an unstable mixture of CO 2 and CH 4 at 1000 K and 1 GPa. We find that the path to thermodynamic equilibrium is initially entropy-driven, producing a diversity of short-lived species, including various alcohols with intermediate carbon oxidation states. Cyclic and polycyclic radicals that are sometimes resonance-stabilized form next and set the stage for carbon nucleation. The carbon exsolution process releases abundant water, is exothermic and starts with the nucleation of a large aggregate of hydrogenated graphene flakes from covalently bonded polycyclic units. The carbon backbone of this nucleus subsequently grows into a hydrogen-depleted fullerene-like structure, before evolving toward a partially bilayered graphene layer. Overall, our results show that the mechanism of graphitic C formation is certainly not bimolecular, and that it may involve a combination of key condensation and radical chain reactions. This will help understand the isotopic, and reactive characteristics of carbon-bearing fluids during their upward transit through the Earth’s mantle and crust. Moreover, the mechanistic insights outlined here present intriguing similarities with the process of soot and interstellar dust formation, which suggests that the widespread distribution of abiotic polyaromatic and graphitic material on Earth and beyond may reflect the prevalence of a fundamental chemical pathway.

58 GEOSCIENCES↗
DOE OSTI2022

Atomistic and cluster dynamics modeling of fission gas (Xe) diffusivity in TRISO fuel kernels

TRISO fuel particles are candidates for use in next generation reactors including gas reactors, fluoride salt-cooled high temperature reactors, and micro-reactors. The UCO fuel kernel consists of a uranium dioxide (UO) and uranium carbide mixture. The addition of UC helps suppress the formation of carbon monoxide gas, which led to failures during initial TRISO development. The addition of uranium carbide alters the chemistry of the UO kernel, which is known to influence performance parameters such as fission gas diffusivity, although the impact has not been quantified and no models exist that take the change in chemistry into account. Therefore, better understanding and more accurate models of the impact of chemistry on fuel performance are of high priority. In this paper, a first-principles density functional theory (DFT) and empirical potential based multi-scale study has been carried out to model the diffusivity of fission gas xenon (Xe) in UCO TRISO fuel kernels. The focus is on the UO component in the UCO fuel kernels, as that represents the largest volume fraction of the fuel kernels. The study relies on DFT and empirical potential calculations to determine Xe and point defect properties, which are then used in thermodynamic and kinetic models to predict diffusion for intrinsic conditions. In addition, the information is utilized in cluster dynamics simulations using the Centipede code to estimate the impact of irradiation on defect transport. Additionally, the presence of UC or UC in the UCO fuel kernels is shown to have a substantial impact on the UO non-stoichiometry by inducing oxygen vacancies and driving UO sub-stoichiometric, which causes much slower Xe diffusion in UCO compared to light water reactor UO fuel. The application of this model in fuel performance simulations using the Bison code is also demonstrated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

MaterialsMap: A CALPHAD-based tool to design composition pathways through feasibility map for desired dissimilar materials, demonstrated with resistance spot welding joining of Ag-Al-Cu

Assembly of dissimilar metals can be achieved by different methods, for example, casting, welding, and additive manufacturing (AM). However, undesired phases formed in liquid-phase assembling processes due to solute segregation during solidification diminish mechanical and other properties of the processed parts. In the present work, an open-source software named MaterialsMap, has been developed based on the CALculation of Phase Diagrams (CALPHAD) approach. The primary objective of MaterialsMap is to facilitate the design of an optimal composition pathway for assembling dissimilar alloys with liquid-phases based on the formation of desired and undesired phases along the pathway. In MaterialsMap, equilibrium thermodynamic calculations are used to predict equilibrium phases formed at slow cooling rate, while Scheil-Gulliver simulations are employed to predict non-equilibrium phases formed during rapid cooling. Additionally, by combining these two simulations, MaterialsMap offers a thorough guide for understanding phase formation in various manufacturing processes, assisting users in making informed decisions during material selection and production. As a demonstration of this approach, a compositional pathway was designed from pure Al to pure Cu through Ag using MaterialsMap. The design was experimentally verified using resistance spot welding (RSW).

36 MATERIALS SCIENCE↗