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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

Idakwo, Gabriel↗
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 OSTI2021

Decontamination and Sterilization of Surfaces using Low Cost Ionic Liquids (CRADA Final Report)

The current study made a comprehensive literature review of the structure activity relationship (SAR) for microcidal and virucidal influence of ionic liquids. It summarized the key structure parameters that have close correlation to the antimicrobial and antiviral properties of the ILs. It proposed an experimental plan on developing and testing an effective ionic liquid from renewable lignocellulosic biomass. The goal is to understand the structure activity relationship (SAR) for virucidal influence of ionic liquids through comprehensive literature search and data analysis. A full experimental plan will be drafted based on the literature summary. Perform a comprehensive literature search to aid the development of an effective and easily deployable method to inactivate SARS-CoV-2 and decontaminate surfaces based on low-cost functional ionic liquids. These designer solvents have the potential to be a compelling and environmentally friendly alternative to common chemical disinfectants. Literature summary report including reported ionic liquid structures, synthesis and testing methods, and the structure activity relationship. Experimental plan on developing a low cost effective antiviral ionic liquid.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

De novo designed ice-binding proteins from twist-constrained helices

Attaining molecular-level control over solidification processes is a crucial aspect of materials science. To control ice formation, organisms have evolved bewildering arrays of ice-binding proteins (IBPs), but these have poorly understood structure–activity relationships. We propose that reverse engineering using de novo computational protein design can shed light on structure–activity relationships of IBPs. We hypothesized that the model alpha-helical winter flounder antifreeze protein uses an unusual undertwisting of its alpha-helix to align its putative ice-binding threonine residues in exactly the same direction. We test this hypothesis by designing a series of straight three-helix bundles with an ice-binding helix projecting threonines and two supporting helices constraining the twist of the ice-binding helix. Our findings show that ice-recrystallization inhibition by the designed proteins increases with the degree of designed undertwisting, thus validating our hypothesis, and opening up avenues for the computational design of IBPs.

Science & Technology - Other Topics↗
DOE OSTI2025

Rational Selection of Transition-Metal Oxide Electrocatalysts from Structure Electronic Structure-Activity Relations: The Role of Defects, Strain, and Sub-Surface Layering

This BES research investigates the physicochemical properties of metal oxides and how they affect electrocatalytic functionalities. The goal is to develop a predictive framework for realizing top-performing electrocatalytic materials for energy-critical reactions. The hypothesis is that well-defined thin films allow detailed mapping of structural-activity relationships because flat surfaces are more straightforward to characterize. Furthermore, the well-defined nature of thin-film metal oxides allows precise tuning of structural and chemical variables for structure-activity-relationship mapping. The proposed research has two technical objectives. The first is to experimentally assess whether the binding energies of surface oxygen can serve as an activity descriptor for oxygen electrocatalysis on metal oxides and then how to control them by tuning the structure and chemical variables. The second is to find the rate-limiting process in oxygen electrocatalysis and other small-molecule reactions. Similar mechanistic insights have been developed on well-defined platinum surfaces but not yet on oxides. This research addresses this gap and uses electrochemistry and X-ray photoemission spectroscopy to study oxide surface chemistry. The obtained insights are collectively analyzed to reveal how the oxides’ structural and chemical variables affect kinetics and can be used to design more active electrocatalysts for energy-critical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Unraveling Fundamental Activity–Stability Relationships in Rutile Oxides

The oxygen evolution reaction (OER) is a key anodic half-cell reaction that accompanies several critical electrochemical reduction reactions of interest to a variety of applications. Despite steady advances in understanding and qualitatively predicting OER activity and selectivity trends, a comprehensive description or prediction of material aqueous (in)stability and degradation mechanisms remains elusive, even though these processes critically influence device lifetime and economic feasibility. In this work, we investigate the interplay, or lack thereof, between OER activity and material aqueous stability across rutile oxides, with a particular focus on iridium oxide (IrO 2 ). By applying a Born–Haber cycle, we calculate the thermodynamic driving force for metal dissolution as a function of the applied bias and electrolyte conditions. We apply interpretable machine learning techniques, including principal component analysis and symbolic regression, to analyze trends across rutile oxides and find that key thermodynamic descriptors for OER activity and surface stability are only very weakly correlated. Instead, the local atomic environment─especially electronic structure signatures for interactions between the active site and its neighbors─plays a more important role in predicting material stability. Leveraging these insights, we investigate the impact of doping IrO 2 with a range of transition metals and show that the stability of Ir active sites can be tuned largely independently of its predicted OER activity. These insights lay the foundation for material design to improve stability with respect to corrosion, with the ultimate aim to enhance long-term stability without sacrificing catalytic performance in the OER.

evolution reactions↗
DOE OSTI2020

Engineering Active Fe Sites on Nickel–Iron Layered Double Hydroxide through Component Segregation for Oxygen Evolution Reaction

Nickel-iron layered double hydroxide (NiFe LDH) is a promising oxygen evolution reaction (OER) electrocatalyst under alkaline conditions. Much research has been performed to understand the structure-activity relationship of NiFe LDH under OER conditions. However, the specific role of the Fe species remains unclear and under debate. Herein, based on DFT calculations, it was discovered that the edge Fe sites show higher activity towards OER than either the edge Ni sites or lattice sites. Therefore, a facile acid-etching method was proposed to controllably induce the formation of edge Fe sites in NiFe LDH, and the obtained sample exhibited higher OER activity. X-ray absorption near edge structure and extended X-ray absorption fine structure analyses further revealed that the interaction of the edge Fe species with Ni is believed to contribute to the enhancement of the OER performance. This work provides a new understanding of the structure-activity relationship in NiFe LDH and offers a facile method for the design of efficient electrocatalysts in an alkaline environment.

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

Acidic Oxygen Evolution Reaction Activity–Stability Relationships in Ru-Based Pyrochlores

Ru-based oxygen evolution reaction (OER) catalysts show significant promise for efficient water electrolysis, but rapid degradation poses a major challenge for commercial applications. In this work, we explore several Ru-based pyrochlores (A 2 Ru 2 O 7 , A = Y, Nd, Gd, Bi) as OER catalysts and demonstrate improved activity and stability of catalytic Ru sites relative to RuO 2 . Furthermore, we combine complementary experimental and theoretical analysis to understand how the A-site element impacts activity and stability under acidic OER conditions. Among the A 2 Ru 2 O 7 studied herein, we find that a longer Ru–O bond and a weaker interaction of the Ru 4d and O 2p orbitals compared with RuO 2 results in enhanced initial activity. We observe that the OER activity of the catalysts changes over time and is accompanied by both A-site and Ru dissolution at different relative rates depending on the identity of the A-site. Pourbaix diagrams constructed using density functional theory (DFT) calculations reveal a driving force for this experimentally observed dissolution, indicating that all compositions studied herein are thermodynamically unstable in acidic OER conditions. Theoretical activity predictions show consistent trends between A-site cation leaching and OER activity. These trends coupled with Bader charge analysis suggest that dissolution exposes highly oxidized Ru sites that exhibit enhanced activity. Overall, using the stability number (molO2 evolved/molRu dissolved) as a comparative metric, the A 2 Ru 2 O 7 materials studied in this work show substantially greater stability than a standard RuO2 and commensurate stability to some Ir mixed metal oxides. Finally, the insights described herein provide a pathway to enhanced Ru catalyst activity and durability, ultimately improving the efficiency of water electrolyzers.

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

Engineering Second Sphere Interactions in a Host–Guest Multicomponent Catalyst System for the Hydrogenation of Carbon Dioxide to Methanol

Many enzymes utilize interactions extending beyond the primary coordination sphere to enhance catalyst activity and/or selectivity. Such interactions could improve the efficacy of synthetic catalyst systems, but the supramolecular assemblies employed by biology to incorporate second sphere interactions are challenging to replicate in synthetic catalysts. Herein, a strategy is reported for efficiently manipulating outer sphere influence on catalyst reactivity by modulating host–guest interactions between a noncovalently encapsulated transitionmetal-based catalyst guest and a metal–organic framework (MOF) host. This composite consists of a ruthenium PNP pincer complex encapsulated in the MOF UiO-66 that is used in tandem with the zirconium oxide nodes of UiO-66 and a ruthenium PNN pincer complex to hydrogenate carbon dioxide to methanol. Due to the method used to incorporate the complexes in UiO-66, structure–activity relationships could be efficiently determined using a variety of functionalized UiO-66-X hosts. These investigations uncovered the beneficial effects of the ammonium functional group (i.e., UiO-66-NH 3 + ). Mechanistic experiments revealed that the ammonium functionality improved efficiency in the hydrogenation of carbon dioxide to formic acid, the first step in the cascade. Isotope effects and structure–activity relationships suggested that the primary role of the ammonium functionality is to serve as a general Brønsted acid. Importantly, the cooperative influence from the host was effective only with the functional group in close proximity to the encapsulated catalyst. Here, reactions carried out in the presence of molecular sieves to remove water highlighted the beneficial effects of the ammonium functional group in UiO-66-NH 3 + and resulted in a 4-fold increase in activity. As a result of the modular nature of the catalyst system, the highest reported turnover number (TON) (19 000) and turnover frequency (TOF) (9100 h –1 ) for the hydrogenation of carbon dioxide to methanol are obtained. Moreover, the reaction was readily recyclable, leading to a cumulative TON of 100 000 after 10 reaction cycles.

08 HYDROGEN↗
DOE OSTI2023

Anti-fungal bioactive terpenoids in the bioenergy crop switchgrass (Panicum virgatum) may contribute to ecotype-specific microbiome composition

Abstract Plant derived bioactive small molecules have attracted attention of scientists across fundamental and applied scientific disciplines. We seek to understand the influence of these phytochemicals on rhizosphere and root-associated fungi. We hypothesize that – consistent with accumulating evidence that switchgrass genotype impacts microbiome assembly – differential terpenoid accumulation contributes to switchgrass ecotype-specific microbiome composition. An initial in vitro Petri plate-based disc diffusion screen of 18 switchgrass root derived fungal isolates revealed differential responses to upland- and lowland-isolated metabolites. To identify specific fungal growth-modulating metabolites, we tested fractions from root extracts on three ecologically important fungal isolates – Linnemania elongata, Trichoderma sp. and Fusarium sp. Saponins and diterpenoids were identified as the most prominent antifungal metabolites. Finally, analysis of liquid chromatography-purified terpenoids revealed fungal inhibition structure – activity relationships (SAR). Saponin antifungal activity was primarily determined by the number of sugar moieties – saponins glycosylated at a single core position were inhibitory whereas saponins glycosylated at two core positions were inactive. Saponin core hydroxylation and acetylation were also associated with reduced activity. Diterpenoid activity required the presence of an intact furan ring for strong fungal growth inhibition. These results inform future breeding and biotechnology strategies for crop protection with reduced pesticide application.

09 BIOMASS FUELS↗
DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗