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Results for “molecular descriptors”

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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167 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

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

Learning from metastable symmetric-tilt grain boundaries using physics-based descriptors

Grain boundaries (GBs) govern critical properties of polycrystalline materials. Although significant advancements have been made in characterizing minimum energy and ordered GBs, real GBs are seldom found in such well-defined states. This diversity of atomic arrangements in metastable states makes it challenging to establish structure-property relationships with physical insights. Here, to address this challenge, we use data-driven methods to explore these relationships and examine the underlying physics. In this study, we utilize a large atomistic database (~5000) of minimum energy and metastable states of symmetric-tilt copper GBs, combined with physically motivated local atomic environment (LAE) descriptors [strain functional descriptors (SFDs)], to predict GB properties and gain physical insights. Our regression models exhibit robust predictive capabilities using only 19 descriptors, generalizing to atomic environments in nanocrystals. A significant highlight of our work is the integration of an unsupervised method with SFDs to elucidate LAEs at GBs and their role in determining properties. The model, trained on these minimum energy and metastable GBs using SFDs, predicts the properties of unseen nanocrystals with good accuracy. Our research underscores the role of a physics-based representation of LAEs and the efficacy of data-driven methods in establishing GB structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2022

[Fe 4 S 4 ] cubane in sulfite reductases: new insights into bonding properties and reactivity

The dissimilatory sulfite reductase enzyme has very characteristic active site where the substrate binds to an iron site, ligated by a siroheme macrocycle and a thiol directly connected to a [Fe 4 S 4 ] cluster. This arrangement gives the enzyme remarkable efficiency in reducing sulfite and nitrite all the way to hydrogen sulfide and ammonia. For the first time we present a theoretical study where substrate binding modalities and activation are elucidated using active site models containing proton supply side chains and the [Fe 4 S 4 ] cluster. Density functional theory (DFT) was deployed in conjunction with the energy decomposition scheme (as implemented in AMS), the quantum theory of atoms in molecules (QTAIM), and conceptual DFT (cDFT) descriptors. We quantified the role of the electrostatic interactions inside the active site created by the side chains as well as the influence of the [Fe4S4] cluster on the substrate binding. Furthermore, using conceptual DFT results we shed light of the activation process, thus, laying foundation for further mechanistic studies. We found that the bonding of the ligands to the iron complex is dominated by electrostatic interactions, but the presence of the [Fe 4 S 4 ] cubane leads to substantial changes in electronic interaction. The spin state of the cubane, however, affects the binding energy only marginally. The conceptual DFT results show that the presence of the [Fe 4 S 4 ] cubane affects the reactivity of the active site as it is involved in electron transfer. This is corroborated by an increase in the electrophilicity index, thus making the active site more prone to react with the ligands. The interaction energies between the ligand and the siroheme group are also increased upon the presence of the cubane group, thus, suggesting that the siroheme group is not an innocent spectator but plays an active role in the reactivity of the dSIR active site.

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

Systematic computational study of oxide adsorption properties for applications in photocatalytic CO 2 reduction

While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO 2 reduction reaction: TiO 2 (rutile and anatase), SrTiO 3 , NaTaO 3 , and CeO 2 . Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors.

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

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗