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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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High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning
Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.
Bioinformatic Prediction and High Throughput In Vivo Screening to Identify Cis-Regulatory Elements for the Development of Algal Synthetic Promoters
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Multi-functional genome-wide CRISPR system for high throughput genotype–phenotype mapping
Abstract Genome-scale engineering is an indispensable tool to understand genome functions due to our limited knowledge of cellular networks. Unfortunately, most existing methods for genome-wide genotype–phenotype mapping are limited to a single mode of genomic alteration, i.e. overexpression, repression, or deletion. Here we report a multi-functional genome-wide CRISPR (MAGIC) system to precisely control the expression level of defined genes to desired levels throughout the whole genome. By combining the tri-functional CRISPR system and array-synthesized oligo pools, MAGIC is used to create, to the best of our knowledge, one of the most comprehensive and diversified genomic libraries in yeast ever reported. The power of MAGIC is demonstrated by the identification of previously uncharacterized genetic determinants of complex phenotypes, particularly those having synergistic interactions when perturbed to different expression levels. MAGIC represents a powerful synthetic biology tool to investigate fundamental biological questions as well as engineer complex phenotypes for biotechnological applications.
A universal all-solid synthesis for high throughput production of halide perovskite
Abstract Halide perovskites show ubiquitous presences in growing fields at both fundamental and applied levels. Discovery, investigation, and application of innovative perovskites are heavily dependent on the synthetic methodology in terms of time-/yield-/effort-/energy- efficiency. Conventional wet chemistry method provides the easiness for growing thin film samples, but represents as an inefficient way for bulk crystal synthesis. To overcome these, here we report a universal solid state-based route for synthesizing high-quality perovskites, by means of simultaneously applying both electric and mechanical stress fields during the synthesis, i.e., the electrical and mechanical field-assisted sintering technique. We employ various perovskite compositions and arbitrary geometric designs for demonstration in this report, and establish such synthetic route with uniqueness of ultrahigh yield, fast processing and solvent-free nature, along with bulk products of exceptional quality approaching to single crystals. We exemplify the applications of the as-synthesized perovskites in photodetection and thermoelectric as well as other potentials to open extra chapters for future technical development.
High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity
Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivity k in MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm -3 ), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m -1 K -1 ), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (>10 Wm -1 K -1 ), the structures of which we describe in additional detail.
pyGWBSE: a high throughput workflow package for GW-BSE calculations
Abstract We develop an open-source python workflow package, py GWBSE to perform automated first-principles calculations within the GW-BSE (Bethe-Salpeter) framework. GW-BSE is a many body perturbation theory based approach to explore the quasiparticle (QP) and excitonic properties of materials. GW approximation accurately predicts bandgaps of materials by overcoming the bandgap underestimation issue of the more widely used density functional theory (DFT). BSE formalism produces absorption spectra directly comparable with experimental observations. py GWBSE package achieves complete automation of the entire multi-step GW-BSE computation, including the convergence tests of several parameters that are crucial for the accuracy of these calculations. py GWBSE is integrated with Wannier90 , to generate QP bandstructures, interpolated using the maximally-localized wannier functions. py GWBSE also enables the automated creation of databases of metadata and data, including QP and excitonic properties, which can be extremely useful for future material discovery studies in the field of ultra-wide bandgap semiconductors, electronics, photovoltaics, and photocatalysis.
Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys
Not Available
A high-throughput and low-waste viability assay for microbes
Abstract Counting viable cells is a universal practice in microbiology. The colony-forming unit (CFU) assay has remained the gold standard to measure viability across disciplines, but it is time-intensive and resource-consuming. Here we describe the geometric viability assay (GVA) that replicates CFU measurements over 6 orders of magnitude while reducing over 10-fold the time and consumables required. GVA computes a sample’s viable cell count on the basis of the distribution of embedded colonies growing inside a pipette tip. GVA is compatible with Gram-positive and Gram-negative planktonic bacteria ( Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis ), biofilms and fungi ( Saccharomyces cerevisiae ). Laborious CFU experiments such as checkerboard assays, treatment time-courses and drug screens against slow-growing cells are simplified by GVA. The ease and low cost of GVA evinces that it can replace existing viability assays and enable viability measurements at previously impractical scales.
High-throughput calculations of magnetic topological materials
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High throughput calculations for a dataset of bilayer materials
Bilayer materials made of 2D monolayers are emerging as new systems creating diverse opportunities for basic research and applications in optoelectronics, thermoelectrics, and topological science among others. Herein, we present a computational bilayer materials dataset containing 760 structures with their structural, electronic, and transport properties. Different stacking patterns of each bilayer have been framed by analyzing their monolayer symmetries. Density functional theory calculations including van der Waals interactions are carried out for each stacking pattern to evaluate the corresponding ground states, which are correctly identified for experimentally synthesized transition metal dichalcogenides, graphene, boron nitride, and silicene. Binding energies and interlayer charge transfer are evaluated to analyze the interlayer coupling strength. Our dataset can be used for materials screening and data-assisted modeling for desired thermoelectric or optoelectronic applications.
Second-harmonic generation tensors from high-throughput density-functional perturbation theory
Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density-functional perturbation theory for a set of 579 inorganic semiconductors. The computational methodology is discussed and the provided database is described with respect to both its data distribution and its format. Several comparisons with both experimental and ab initio results from literature allow to confirm the reliability of our data. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.
Crystallography companion agent for high-throughput materials discovery
The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.
Experimental validation of high thermoelectric performance in RECuZnP 2 predicted by high-throughput DFT calculations
Predictions of high thermoelectric performance in RECuZnP 2 were verified by elastic, electrical, and thermal measurements. Low thermal conductivities result from strong anharmonicity, with electron transport limited by polar optical phonons.
High-throughput investigation of the formation of double spinels
Spinel compounds, with the general chemical formula AB 2 O 4 , are a wide class of materials, where A and B can be a variety of cations, providing this structure with a great deal of functional flexibility and giving rise to its considerable scientific interest. Recently, a spinel with the general formula ABB'O 4 has been predicted, increasing the possible usability of the spinels due to the higher cation diversity in the so-called double spinel structure. In this study, we use density functional theory calculations to predict if double spinels can be formed between experimentally synthesized normal and inverse single spinels. Our computations reveal that 49 double spinels have negative mixing enthalpies and are thus thermodynamically stable, with most of the stable compounds being formed from one of two distinct cation orderings. We show that the 17 different cations that form the different double spinels have a preferred site, tetrahedral or octahedral, except for Mn, Fe and Co which can occupy both sites interchangeably. We also study the relation between mixing enthalpies and cation-specific properties, as well as ways to classify the double spinels into distinct types and spinel groups depending on the cation ordering and cation oxidation states, respectively. Furthermore, by applying the Sure Independence Screening and Sparsifying Operator (SISSO) approach on the coordination-dependent ionic radii of the elemental constituents, we show that an interplay of local strain and electrostatic dominated terms can be used to separate the double spinels into distinct structural types depending on the cation order and their oxidation states.