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Abstract The sublimation enthalpy, , is a key thermodynamic parameter governing the phase transformation of a substance between its solid and gas phases. This transformation is at the core of many important materials' purification, deposition, and etching processes. While can be measured experimentally and estimated computationally, these approaches have their own different challenges. Here, we develop a machine learning (ML) approach to rapidly predict from data generated using density functional theory (DFT). We further demonstrate how combining ML and DFT methods with active learning can be efficient in exploring the materials space, expanding the coverage of the computed dataset, and systematically improving the ML predictive model of . With an error of kJ/mol in instantaneous predictions of , the ML model developed in this work will be useful for the community. 

Abstract A Bayesian optimization procedure is presented for calibrating a multi-mechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8-9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600 °C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework [1]. Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. Validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel. 

Abstract It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article, we show that the optimal training/testing splitting ratio is , where is the number of parameters in a linear regression model that explains the data well. 

Abstract In this work, we develop a method namedTwinningfor partitioning a dataset into statistically similar twin sets.Twinningis based onSPlit, a recently proposed model‐independent method for optimally splitting a dataset into training and testing sets.Twinningis orders of magnitude faster than theSPlitalgorithm, which makes it applicable to Big Data problems such as data compression.Twinningcan also be used for generating multiple splits of a given dataset to aid divide‐and‐conquer procedures andk‐fold cross validation. 

Vapor phase infiltration (VPI) is a vapor processing technique that converts polymers into organic–inorganic hybrid materials with modified properties. VPI of polymer membranes stabilizes against dissolution and swelling in organic liquids, opening up new opportunities for use in organic solvent reverse osmosis (OSRO) separations. However, the precise chemical structure of the infiltrated inorganic components remains poorly understood, limiting the potential to fully exploit process–structure–property relations for materials design and slowing the development of new hybrid membranes. This study explores the structural characteristics contributing to the chemical stability of PIM-1/ZnOxHy hybrid membranes through advanced spectroscopic techniques to clarify the chemistry and inorganic cluster formation in these materials that lead to enhanced stability in solvents that otherwise swell or dissolve the pure polymer. X-ray photoelectron spectroscopy (XPS) indicates a predominantly zinc hydroxide chemistry with higher proportions of oxide forming only at increasing cycle counts. Extended X-ray absorption fine structure (EXAFS) spectroscopy provides new understanding of the first and second coordination shells. These results indicate that the size of the clusters increases with prolonged VPI precursor exposure and additional VPI cycles, leading to improvements in membrane solvent stability. These findings offer a new understanding for how the physicochemical structure of these hybrid membranes can be characterized and then used to design for a desired performance. 

We explored the potential for membrane materials to reduce energy and carbon requirements for the separation of aliphatic hydrocarbon feedstocks and products. We developed a series of fluorine-rich poly(arylene amine) polymer membranes that feature rigid polymer backbones with segregated perfluoroalkyl side chains. This combination imbues the polymers with resistance to dilation induced by hydrocarbon immersion without the loss of solution-based membrane fabrication techniques. These materials exhibit good separation of liquid-phase alkane isomers at ambient temperatures. The integration of these polymeric membranes into fuel and chemical feedstock separation processes was investigated in a series of experiments. Technoeconomic analyses based on these experiments indicate that the best-performing membrane materials can substantially reduce the energy costs and associated carbon emissions of hydrocarbon separations (two to 10 times, depending on product specifications). 

Vapor phase infiltration (VPI) enables the fabrication of novel organic–inorganic hybrid materials with distinctive properties by infiltrating polymers with inorganic species through a top-down approach. However, understanding the process kinetics is challenging due to the complex interplay of sorption, diffusion and reaction processes. This study examines how polymer network flexibility affects the kinetics of diethylzinc (DEZ) infiltration into a highly crosslinked polyacrylate copolymer system composed of two monomers: trimethylolpropane triacrylate (TMPTA) and ethoxylated trimethylolpropane triacrylate (ETPTA). The findings show that increasing the ratio of ETPTA, which enhances network flexibility, facilitates precursor diffusion, resulting in deeper infiltration and faster saturation. A reaction–diffusion transport model is employed to qualitatively interpret the experimental results and gain insights into the underlying process mechanisms, thus contributing to a better understanding of VPI kinetics.