The class of 9-12% Cr ferritic-martensitic alloys (FMA) and austenitic stainless steels have received considerable attention due to their numerous applications in high temperature power generation ...industries. To design high strength steels with prolonged service life requires a thorough understanding of the long-term properties, e.g., creep rupture strength, rupture life, etc., as a function of the chemical composition and processing parameters that govern the microstructural characteristics. In this article, the creep rupture strength of both 9-12% Cr FMA and austenitic stainless steel has been parameterized using curated experimental datasets with a gradient boosting machine. The trained model has been cross validated against unseen test data and achieved high predictive performance in terms of correlation coefficient (Formula: see text for 9-12% Cr FMA and Formula: see text for austenitic stainless steel) thus bypassing the need for additional comprehensive tensile test campaigns or physical theoretical calculations. Furthermore, the feature importance has been computed using the Shapley value analysis to understand the complex interplay of different features.
We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid ...synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young's modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young's modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.
Three probabilistic methodologies are developed for predicting the long-term creep rupture life of 9-12 wt%Cr ferritic-martensitic steels using their chemical and processing parameters. The framework ...developed in this research strives to simultaneously make efficient inference along with associated risk, i.e., the uncertainty of estimation. The study highlights the limitations of applying probabilistic machine learning to model creep life and provides suggestions as to how this might be alleviated to make an efficient and accurate model with the evaluation of epistemic uncertainty of each prediction. Based on extensive experimentation, Gaussian Process Regression yielded more accurate inference (Formula: see text for the holdout test set) in addition to meaningful uncertainty estimate (i.e., coverage ranges from 94 to 98% for the test set) as compared to quantile regression and natural gradient boosting algorithm. Furthermore, the possibility of an active learning framework to iteratively explore the material space intelligently was demonstrated by simulating the experimental data collection process. This framework can be subsequently deployed to improve model performance or to explore new alloy domains with minimal experimental effort.
The effects of hydration level and temperature on the nanostructure of an atomistic model of a Nafion (DuPont) membrane and the vehicular transport of hydronium ions and water molecules were examined ...using classical molecular dynamics simulations. Through the determination and analysis of structural and dynamical parameters such as density, radial distribution functions, coordination numbers, mean square deviations, and diffusion coefficients, we identify that hydronium ions play an important role in modifying the hydration structure near the sulfonate groups. In the regime of low level of hydration, short hydrogen bonded linkages made of water molecules and sometimes hydronium ions alone give a more constrained structure among the sulfonate side chains. The diffusion coefficient for water was found to be in good accord with experimental data. The diffusion coefficient for the hydronium ions was determined to be much smaller (6−10 times) than that for water. Temperature was found to have a significant effect on the absolute value of the diffusion coefficients for both water and hydronium ions.
Abstract
The Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability ...and dependence on the constant
C
often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.
Abstract
More than $270 billion is spent on combatting corrosion annually in the USA alone. As such, we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our focus ...is on a non-traditional class of alloys called multi-principal element alloys (MPEAs). Given the vast search space due to the variety of compositions and descriptors to be considered, and based upon existing corrosion data for MPEAs, we demonstrate descriptor optimization to predict corrosion resistance of any given MPEA. Our ML model with descriptor optimization predicts the corrosion resistance of a given MPEA in the presence of an aqueous environment by down selecting two environmental descriptors (pH of the medium and halide concentration), one chemical composition descriptor (atomic % of element with minimum reduction potential), and two atomic descriptors (difference in lattice constant (
$$\Delta {{{\mathrm{a}}}$$
Δ
a
) and average reduction potential). Our findings show that, while it is possible to down select corrosion-resistant MPEAs by using ML from a large search space, a larger dataset and higher quality data are needed to accurately predict the corrosion rate of MPEAs. This study shows both the promise and the perils of ML when applied to a complex chemical phenomenon like corrosion of alloys.
Proton exchange membranes (PEMs) that operate at temperatures above 120 degreeC are needed to avoid catalyst poisoning, enhance electrochemical reactions, simplify the design and reduce the cost of ...fuel cells. This review summarizes developments in PEMs over the last five years. In order to design new membranes for elevated temperature operation, one must understand the chemistry, morphology and dynamics of protons and water molecules in existing membranes. The integration of experiment with modelling and simulation can shed light on the hierarchical structure of the membrane and dynamical processes associated with molecular transport. Based on such a fundamental understanding, membranes can be modified by controlling the polymer chemistry and architecture or adding inorganic fillers that can retain water under low relative humidity conditions. The development of anhydrous membranes based on phosphoric acid doped polymers, ionic liquid-infused polymer gels and solid acids can enable fuel cell operation above 150 degreeC. Considerable work remains to be done to identify proton transport mechanisms in novel membranes and evaluate membrane durability under real world operating conditions.
A polyethersulfone (PES)-supported graphene oxide (GO) membrane has been developed by a simple casting approach. This stable membrane is applied for ethanol/water separation at different ...temperatures. The 5.0 µm thick GO film coated on PES support membrane showed a long-term stability over a testing period of one month and excellent water/ethanol selectivity at elevated temperatures. The water/ethanol selectivity is dependent on ethanol weight percentage in water/ethanol feed mixtures and on operating temperature. The water/ethanol selectivity was enhanced with an increase of ethanol weight percentage in water/ethanol mixtures, from below 100 at RT to close to 874 at a 90 °C for 90% ethanol/10% water mixture. Molecular dynamics simulation of water-ethanol mixtures in graphene bilayers, that are considered to play a key role in transport, revealed that molecular transport is negligible for layer spacing below 1 nm. The differences in the diffusion of ethanol and water in the bilayer are not consistent with the large selectivity value experimentally observed. The entry of water and ethanol into the interlayer space may be the crucial step controlling the selectivity.
Here, more than a billion people do not have access to clean water globally and millions of people die every year from water borne diseases. Human activity has resulted in depletion of groundwater, ...seawater intrusion in coastal aquifers, pollution of water resources, ecological damage, and resultant threats to the world’s freshwater, food supply, security, and prosperity. To address this challenge, there is a pressing need to produce clean water from seawater, brackish groundwater, and waste water. Current desalination methods are energy intensive and produce adverse environmental impact. At the same time, energy production consumes large quantities of water and creates waste water that needs to be treated with further energy input. Water treatment with membranes that separate water molecules from ions, pathogens and pollutants has been proposed as an energy-efficient solution to the fresh water crisis. Recently, membranes based on carbon nanotubes, graphene and graphene oxide (GO) have garnered considerable interest for their potential in desalination. Of these, GO membranes hold the promise of inexpensive production on a large scale but swell when immersed in water. The swollen membrane allows not only water molecules but also ions, such as Na+ and Mg2+, to pass through. Abraham and coworkers show that the interlayer spacing in a GO laminar membrane can be tuned to a certain value and then fixed by physically restraining the membrane from swelling. When the authors reduced the spacing systematically in steps from 9.8 Å to 7.4 Å, the ion permeation rate was reduced by two orders of magnitude while the water permeation rate was only halved.
We have performed a detailed analysis of water clustering and percolation in hydrated Nafion configurations generated by classical molecular dynamics simulations. Our results show that at low ...hydration levels H2O molecules are isolated and a continuous hydrogen-bonded network forms as the hydration level is increased. Our quantitative analysis has established a hydration level (λ) between 5 and 6 H2O/SO3 − as the percolation threshold of Nafion. We have also examined the effect of such a network on proton transport by studying the structural diffusion of protons using the quantum hopping molecular dynamics method. The mean residence time of the proton on a water molecule decreases by 2 orders of magnitude when the λ value is increased from 5 to 15. The proton diffusion coefficient in Nafion at a λ value of 15 is about 1.1 × 10−5 cm2/s in agreement with experiment. The results provide quantitative atomic-level evidence of water network percolation in Nafion and its effect on proton conductivity.
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