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Results for “high throughput screening”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Unraveling H2 chemisorption and physisorption on metal decorated graphene using quantum Monte Carlo

Molecular hydrogen has the potential to significantly reduce the use of carbon dioxide emitting energy processes. However, hydrogen gas storage is a major bottleneck for its large-scale use as current storage methods are energy intensive. Among different storage methods, physisorbing molecular hydrogen at ambient pressure and temperatures is a promising alternative—particularly in light of the advancements in tunable lightweight nanomaterials and high throughput screening methods. Nonetheless, understanding hydrogen adsorption in well-defined nanomaterials remains experimentally challenging and reference information is scarce despite the proliferation of works predicting hydrogen adsorption. We focus on Li, Na, Ca, and K, decorated graphene sheets as substrates for molecular hydrogen adsorption, and compute the most accurate adsorption energies available to date using quantum diffusion Monte Carlo (DMC). Building on our previous insights at the density functional theory (DFT) level, we find that a weak covalent chemisorption of molecular hydrogen, known as Kubas interaction, is feasible on Ca decorated graphene according to DMC, in agreement with DFT. This finding is in contrast to previous DMC predictions of the 4H2/Ca+ gas cluster (without graphene) where chemisorption is not favored. However, we find that the adsorption energy of hydrogen on metal decorated graphene according to a widely used DFT method is not fully consistent with DMC. The reference adsorption energies reported herein can be used to find better work-horse methods for application in large-scale modeling of hydrogen adsorption. Furthermore, the implications of this work affect strategies for finding suitable hydrogen storage materials and high-throughput methods.

Chemistry↗
DOE OSTI2020

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Prediction of ambient pressure superconductivity in cubic ternary hydrides with MH 6 octahedra

Exploring high-temperature superconducting (high-T c ) material at ambient pressure holds immense significance for physics, chemistry, and materials science. In this study, we perform a high-throughput screening of strong electron-phonon interactions in X 2 MH 6 compounds (X = Li, Na, Mg, Al, K, Ca, Ga, Rb, Sr, and In; M are 3d, 4d, and 5d transition metals). These compounds have a cubic structure featuring an MH 6 octahedron motif. Our screening calculations suggest that 26 compounds exhibit dynamic stability and strong electron-phonon coupling. Among them, Mg 2 RhH 6 , Mg 2 IrH 6 , Al 2 MnH 6 , and Li 2 CuH 6 show promising energetic stability and T c of more than 50 K at ambient pressure. This study underscores promising high-T c compounds at ambient pressure with distinctive MH 6 motifs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Design of Twisted Two-Dimensional Heterostructures and Performance Regulation Descriptor for Electrocatalytic Ammonia Production from Nitric Oxide

The electrocatalytic reduction of nitric oxide (NO) to ammonia (NH 3 ) represents an attractive alternative for valorizing waste NO streams (NORR). However, discovering efficient catalysts for NO-to-NH 3 conversion remains challenging. We have designed metal-intercalated twisted graphene-BN heterostructures, in which metal atoms act as electron-transfer bridges. The twisted configuration facilitates cross-interface charge transfer, redistributing electrons from the graphene–metal interface to the metal–BN interface and BN surface. This electronic modulation enables boron atom adjacent to the metal center in BN to serve as active sites, promoting strong chemisorption and enhanced activation of NO. After high-throughput screening of the stability and NO capture ability of various transition metal-intercalated twisted heterostructures, we have investigated systematically the NORR pathways across 30 candidates. The results show that the rBN-Ti-Gθ and rBN-V-Gθ heterostructures exhibit exceptional NO-to-NH 3 catalytic performance under optimized twisting conditions. Additionally, using sure independence screening and sparsifying operator (SISSO) for model training, we propose a descriptor and establish a relationship between the twist angle and catalytic activity. This study bridges the gap in applying twisted heterostructures to NORR electrocatalysis and provides new insights and strategies for designing high-performance NORR catalysts.

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

Gene co-expression network analysis in zebrafish reveals chemical class specific modules

Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response. Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure. Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib’s mechanism of M pro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His 163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1–M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships. Here, this review provides recent progress in the ML-aided rational design of heterogeneous catalysts with the focus on SACs in terms of structure–activity relationships, feature importance analysis, high-throughput screening, stability, and metal–support interactions for electrochemistry. Support vector machine (SVM), random forest regression (RFR), and deep neural networks (DNN) along with atomic properties are mainly used for the design of SACs. The ML results have shown that the number of electrons in the d orbital, oxide formation enthalpy, ionization energy, Bader charge, d-band center, and enthalpy of vaporization are mainly the most important parameters for the defining of the structure–activity relationships for electrochemistry. However, the black-box nature of ML techniques occasionally makes a physical interpretation of descriptors, such as the Bader charge, d-band center, and enthalpy of vaporization, non-trivial. At the current stage, ML application is limited by the lack of a large and high-quality database. Future prospects for the development of a large database and a generalized ML algorithm for SAC design are discussed to give insights for further studies in this field.

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

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

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

Stacking Fault Energy Based Alloy Screening for Hydrogen Compatibility

The selection of austenitic stainless steels for hydrogen service is challenging since there are few intrinsic metrics that relate alloy composition to hydrogen degradation. One such metric, explored here, is intrinsic stacking fault energy. Stacking fault energy has an influence on the character and structure of dislocations and on the formation of secondary crystalline phases created during mechanical deformation in austenitic alloys. In this work, a data-driven model for the intrinsic stacking fault energy of common austenitic stainless steel alloys is applied to compare the relative degradation of tensile performance in the presence of hydrogen. A transition in the tensile reduction of area of both 300-series and manganese stabilized stainless steels is observed at a calculated stacking fault energy of approximately 43 mJ m -2 , below which pronounced hydrogen degradation on tensile ductility is observed. The model is also applied to suggest alloying strategies for low nickel austenitic stainless steels for hydrogen service. Lastly, through this investigation, we find that calculated intrinsic stacking fault energy is a high-throughput screening metric that enables the ranking of the performance of a diverse range of austenitic stainless steel compositions, as well as the identification of new alloys, with regard to hydrogen compatibility.

08 HYDROGEN↗
DOE OSTI2020

Design principles for the ultimate gas deliverable capacity material: nonporous to porous deformations without volume change

Understanding the fundamental limits of gas deliverable capacity in porous materials is of critical importance as it informs whether technical targets (e.g., for on-board vehicular storage) are feasible. High-throughput screening studies of rigid materials, for example, have shown they are not able to achieve the original ARPA-E methane storage targets, yet an interesting question remains: what is the upper limit of deliverable capacity in flexible materials? In this work we develop a statistical adsorption model that specifically probes the limit of deliverable capacity in intrinsically flexible materials. The resulting adsorption thermodynamics indicate that a perfectly designed, intrinsically flexible nanoporous material could achieve higher methane deliverable capacity than the best benchmark systems known to date with little to no total volume change. Density functional theory and grand canonical Monte Carlo simulations identify a known metal–organic framework (MOF) that validates key features of the model. Therefore, this work (1) motivates a continued, extensive effort to rationally design a porous material analogous to the adsorption model and (2) calls for continued discovery of additional high deliverable capacity materials that remain hidden from rigid structure screening studies due to nominal non-porosity.

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

Biosynthesis of bioprivileged, linear molecules via novel carboligase reactions

Over the award period, we made progress on the three aims. We screened twenty-five carboligases for activity coupling twenty-one possible -keto acids (Aim 1). The carboligases were selected across a diverse set of protein sequences. Using Q-Exactive UHPLC-MS, we tested a total of 210 coupled products per enzyme and generated a dataset of 5250 enzyme-substrate activity relationships. We identified multiple enzymes that had activity for synthesizing suberic acid and heptanoic acid (Aim 2). We built a random forest model for predicting the activity of each enzyme toward substrates on which it was not tested using the data from Aim 1. Finally, we evaluated growth defects that occurred due to expression of different carboligases in E. coli (Aim 3). We were able to identify specific metabolites and putative pathways that, when supplemented in the media, recovered the growth defect associated with the presence of specific carboligases. We are in the process of publishing two manuscript describing the methods for high-throughput screening of enzyme promiscuity, using machine learning to predict activity on untested substrates, and enzyme activity data we collected. This project has produced enabling data for biosynthesis of a range of new-to-nature compounds to support biomanufacturing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Synergistic double-atom catalysts of metal-boron anchored on g-C 2 N for electrochemical nitrogen reduction: Mechanistic insight and catalyst screening

The rational design of a novel catalytic center with a sound basis remains both challenging and rewarding for the electrochemical reduction of N 2 (eNRR), which has provided a feasible route for achieving clean and sustainable NH 3 production under ambient conditions. Herein, using density functional theory calculations, we demonstrate that hybrid metal (M)-boron (B) double-atom catalysts (DACs) embedded in g-C 2 N substrate (M-B@C 2 N, M = 3d, 4d and 5d transition metals) can achieve both high catalytic activity and high selectivity in eNRR. The proposed M-B@C 2 N DACs have exhibited impressive feasibility and stability thanks to the resilient and robust C 2 N substrate with abundant pyridinic N atoms distributed among right-sized pore structures. Our results reveal that like the metal center, the embedded B atom can actively involve in Ntriple bondN bond activation via π*-backdonation mechanism concomitant with the substantial charge transfer to adsorbed *N 2 , leading to sizable Nsingle bondN bond elongation. Accordingly, both adsorption energy and Nsingle bondN bond length of *N 2 can be employed as catalytic descriptors for predicting eNRR activity in terms of the limiting potentials (UL). Using high-throughput screening method, we found that six M-B@C 2 N candidates have stood out as the outstanding electrocatalysts for driving eNRR, namely, M = Ti (U L = 0 V), Mo (U L = 0 V), Nb (U L = -0.04 V), W (U L = -0.23 V), Zr (U L = -0.26 V), V (U L = -0.28 V). The underlying origin is attributed to the balanced and constrained N-affinity of M-B dual site working in synergy, which can thus be used as one important guide of catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exploring the Structural, Dynamic, and Functional Properties of Metal–Organic Frameworks through Molecular Modeling

This review spotlights the role of atomic-level modeling in research on metal-organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster-based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high-throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scalable, two–stage, autoinduction of recombinant protein expression in E. coli utilizing phosphate depletion

We report the scalable production of recombinant proteins in Escherichia coli, reliant on tightly controlled autoinduction, triggered by phosphate depletion in the stationary phase. The method, reliant on engineered strains and plasmids, enables improved protein expression across scales. Expression levels using this approach have reached as high as 55% of the total cellular protein. The initial use of the method in instrumented fed–batch fermentations enables cell densities of ~30 gCDW/L and protein titers up to 8.1 ± 0.7 g/L (~270 mg/gCDW). The process has also been adapted to an optimized autoinduction media, enabling routine batch production at culture volumes of 20 μl (384–well plates), 100 μl (96–well plates), 20 ml, and 100 ml. In batch cultures, cell densities routinely reach ~5–7 gCDW/L, offering protein titers above 2 g/L. Here, the methodology has been validated with a set of diverse heterologous proteins and is of general use for the facile optimization of routine protein expression from high throughput screens to fed–batch fermentation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Transforming layered 2D mats into multiphasic 3D nanofiber scaffolds with tailored gradient features for tissue regeneration

Abstract Multiphasic scaffolds with tailored gradient features hold significant promise for tissue regeneration applications. Herein, this work reports the transformation of two‐dimensional (2D) layered fiber mats into three‐dimensional (3D) multiphasic scaffolds using a ‘solids‐of‐revolution’ inspired gas‐foaming expansion technology. These scaffolds feature precise control over fiber alignment, pore size, and regional structure. Manipulating nanofiber mat layers and Pluronic F127 concentrations allows further customization of pore size and fiber alignment within different scaffold regions. The cellular response to multiphasic scaffolds demonstrates that the number of cells migrated and proliferated onto the scaffolds is mainly dependent on the pore size rather than fiber alignment. In vivo subcutaneous implantation of multiphasic scaffolds to rats reveals substantial cell infiltration, neo tissue formation, collagen deposition, and new vessel formation within scaffolds, greatly surpassing the capabilities of traditional nanofiber mats. Histological examination indicates the importance of optimizing pore size and fiber alignment for the promotion of cell infiltration and tissue regeneration. Overall, these scaffolds have potential applications in tissue modeling, studying tissue‐tissue interactions, interface tissue engineering, and high‐throughput screening for optimized tissue regeneration.

Shahriar, S. M. Shatil↗
DOE OSTI2024

Cellular Phosphate Sensing and Anion Binding by an Azacrown‐Calixpyrrole Hybrid

A hybrid receptor-sensor for anions originating from the merging of positively charged ammonium moieties for electrostatic attraction/stronger binding of azacrowns with directionality of calixpyrrole hydrogen bond donors for selectivity is investigated. As demonstrated this hybrid receptor-sensor shows a remarkable selectivity for orthophosphate even in the presence of other phosphates and anions found in cellular materials (K assoc H 2 PO 4 − >H 2 P 2 O 7 2− >AMP − ≫ADP 2− or ATP 3− over halides, nitrate, or hydrogen sulfate; all Na + salts in water) but also cellular polyphosphate or phospholipids. This selectivity is harnessed in a real-time monitoring of cell lysis by lysozyme, which releases orthophosphate and other phosphates and anions from the cells. This sensitive (LOD 0.4 μM) fluorescence-based microscale method compares favorably with the state-of-the-art techniques but can easily be practiced in a high-throughput screening (HTS) manner. The anion binding and selectivity in aqueous solutions were investigated by NMR and put in context with phosphate binding of the parent calix[4]pyrrole. The microscopic understanding of anion binding by the hybrid receptor was then obtained from a combination of density functional theory (DFT), classical molecular dynamics (MD) with explicit water solvation, and ab initio MD (AIMD) simulations. Correlating the NMR and fluorescence binding data with studies of solvation of the receptor, phosphate anion, and the resulting complex confirms the binding is largely driven by entropic component (TΔS) associated with receptor and anion desolvation.

Anions↗
DOE OSTI2023

Crystal Structures of Diaryl Hydrazone and Sulfone Stabilizers in Complex with an Amyloidogenic Light Chain Reveal an Alternate Ligand‐Binding Cavity

Abstract Stabilization of amyloidogenic immunoglobulin light chains (LCs) by binding of small molecule “kinetic stabilizers” is under development as a novel treatment for light chain amyloidosis. From a high‐throughput screen, we previously identified 16 full‐length (FL) LC stabilizers from five distinct chemotypes. We then obtained structural biological information on two classes of hits, coumarins and hydantoins, revealing that both chemotypes bind to a pocket at the V L −V L interface of the FL LC dimer. Here, we report crystal structures of three screening hits from two other chemotypes, diaryl hydrazones and sulfones, in complex with an amyloidogenic FL LC. While two of these hits bind to the previously identified pocket, one diaryl hydrazone binds to a different pocket bisected by the C 2 symmetry axis of the dimer. These data further expand on the FL LC stabilizer‐binding surface that could be used in design of more potent FL LC aggregation inhibitors.

Chemistry↗