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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗
DOE OSTI2023

Coupled cluster theory on modern heterogeneous supercomputers

This study examines the computational challenges in elucidating intricate chemical systems, particularly through ab-initio methodologies. This work highlights the Divide-Expand-Consolidate (DEC) approach for coupled cluster (CC) theory—a linear-scaling, massively parallel framework—as a viable solution. Detailed scrutiny of the DEC framework reveals its extensive applicability for large chemical systems, yet it also acknowledges inherent limitations. To mitigate these constraints, the cluster perturbation theory is presented as an effective remedy. Attention is then directed towards the CPS (D-3) model, explicitly derived from a CC singles parent and a doubles auxiliary excitation space, for computing excitation energies. The reviewed new algorithms for the CPS (D-3) method efficiently capitalize on multiple nodes and graphical processing units, expediting heavy tensor contractions. As a result, CPS (D-3) emerges as a scalable, rapid, and precise solution for computing molecular properties in large molecular systems, marking it an efficient contender to conventional CC models.

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

Advanced Methodology and Preliminary Measurements of Molecular and Mechanical Properties of Heart Valves under Dynamic Strain

Mammalian heart valves are soft tissue assemblies with multi-scale material properties. This is because they are constructs comprising both muscle and non-contractile extracellular matrix proteins (such as collagens and proteoglycans) and transition regions where one form of tissue structure becomes another, significantly different form. The leaflets of the mitral and tricuspid valves are connected to chordae tendinae which, in turn, bind through papillary muscles to the cardiac wall of the ventricle. The transition regions between these tissue subsets are complex and diffuse. Their material composition and mechanical properties have not been previously described with both micro and nanoscopic data recorded simultaneously, as reported here. Annotating the mechanical characteristics of these tissue transitions will be of great value in developing novel implants, improving the state of the surgical simulators and advancing robot-assisted surgery. We present here developments in multi-scale methodology that produce data that can relate mechanical properties to molecular structure using scanning X-ray diffraction. We correlate these data to corresponding tissue level (macro and microscopic) stress and strain, with particular emphasis on the transition regions and present analyses to indicate points of possible failure in these tissues.

valve failure↗
DOE OSTI2025

pnnl/idpp_rtp

IdPP is a Python package that provides a framework for the curation and analysis of molecular properties from multidimensional mass spectrometry (MS) measurements. The main components of the package are organized around a molecular property database and identification probability analysis

Ross, Dylan [Pacific Northwest National Laboratory↗
DOE OSTI2020

Using metacommunity ecology to understand environmental metabolomes

Abstract Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Tuning the Solvation and Solubility Properties of Molecularly Heterogeneous Nonionic Deep Eutectic Solvents via Interface Organization

Common separation techniques, such as liquid− liquid extraction, are usually used for extractions and purifications due to their industrial scalability and affordability. However, these well-established practices are hindered by low selectivity and challenges in recovering solutes and solvents. Deep eutectic solvents (DES), a fairly new type of solvent, have the potential to overcome these issues. DESs are binary mixtures whose physical properties can be tuned by selecting the appropriate precursors to facilitate and/or enhance processes such as extraction. A promising DES for selective separations is formed when lauric acid (LA) is mixed with N-methylacetamide (NMA). This LA-NMA DES has a heterogeneous microscopic structure that can solvate compounds with completely different polarities. This study explores how forming an organized structure on the mesoscale affects the solubility of nonpolar solutes in nonionic DESs. To this end, the molecular and mesoscale structures and their effect on the solubility and solvation properties are evaluated for the LA-NMA DES and two new DESs with slight chemical variations in their precursors. It is observed that the organization of the nonpolar DES domains and, consequently, of their interfaces directly relates to the solubility of nonpolar compounds. Specifically, correctly selecting the DES precursors that form organized nonpolar domains leads to an organized interface in which the nonpolar solutes are solvated, thereby increasing the solubility. Additionally, enhanced dissolution power was observed in a completely different DES with mesoscale order in its molecular structure and composed of menthol and lauric acid. The latter result further validates the proposed tunability of the DES dissolution power through organized interfaces, extending it beyond a specific DES family and opening the possibility of new extraction-tailored designer solvents.

Extraction↗
DOE OSTI2024

Electric field sensitivity of molecular color centers

Molecular color centers with S=1 ground states are promising candidates for quantum sensing of electric fields. These molecules have an electronic structure similar to solid state color centers, but they allow for processing modalities that permit direct interfacing with an analyte. Currently, it is unknown how sensitive these molecules are to electric fields and what molecular properties affect their sensitivity. We perform density functional theory calculations to understand the impact of electric fields on the electronic structure of five nominally tetrahedral molecular color centers exhibiting variable transition metal chemistry and ligand densities. We then extract the Stark parameters from each of these molecules and compare them to molecular properties such as the dipole moment and inner shell stiffness and find that the dipole moment of the molecule largely governs sensitivity. We predict that polar heteroleptic molecules may have electric field sensitivities comparable to solid state color centers such as nitrogen-vacancy centers in diamond.

Physics↗
DOE OSTI2020

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Nunez, Jamie↗
DOE OSTI2020

Thermodynamic Properties and Molecular Packing Explain Performance and Processing Procedures of Three D18:NFA Organic Solar Cells

Abstract Organic solar cells (OSCs) based on D18:Y6 have recently exhibited a record power conversion efficiency of over 18%. The initial work is extended and the device performance of D18‐based OSCs is compared with three non‐fullerene acceptors, Y6, IT‐4F, and IEICO‐4Cl, and their molecular packing characteristics and miscibility are studied. The D18 polymer shows unusually strong chain extension and excellent backbone ordering in all films, which likely contributes to the excellent hole‐transporting properties. Thermodynamic characterization indicates a room‐temperature miscibility for D18:Y6 and D18:IT‐4F near the percolation threshold. This corresponds to an ideal quench depth and explains the use of solvent vapor annealing rather than thermal annealing. In contrast, D18:IEICO‐4Cl is a low‐miscibility system with a deep quench depth during casting and poor morphology control and low performance. A failure of ternary blends with PC 71 BM is likely due to the near‐ideal miscibility of Y6 to begin with and indicates that strategies for developing successful ternary or quaternary solar cells are likely very different for D18 than for other high‐performing donors. This work reveals several unique property–performance relations of D18‐based photovoltaic devices and helps guide design or fabrication of yet higher efficiency OSCs.

Wang, Zhen↗
DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗
DOE OSTI2020

Spatially resolved molecular gas properties of host galaxy of Type I superluminous supernova SN 2017egm

Abstract We present the results of CO(1–0) observations of the host galaxy of a Type I superluminous supernova (SLSN-I), SN 2017egm, one of the closest SLSNe-I at z = 0.03063, by using the Atacama Large Millimeter/submillimeter Array. The molecular gas mass of the host galaxy is Mgas = (4.8 ± 0.3) × 109 M⊙, placing it on the sequence of normal star-forming galaxies in an Mgas–star-formation rate (SFR) plane. The molecular hydrogen column density at the location of SN 2017egm is higher than that of the Type II SN PTF10bgl, which is also located in the same host galaxy, and those of other Type II and Ia SNe located in different galaxies, suggesting that SLSNe-I have a preference for a dense molecular gas environment. On the other hand, the column density at the location of SN 2017egm is comparable to those of Type Ibc SNe. The surface densities of molecular gas and the SFR at the location of SN 2017egm are consistent with those of spatially resolved local star-forming galaxies and follow the Schmidt–Kennicutt relation. These facts suggest that SLSNe-I can occur in environments with the same star-formation mechanism as in normal star-forming galaxies.

Hatsukade, Bunyo (ORCID:0000000164698725)↗
DOE OSTI2023

Star formation and molecular gas properties of post-starburst galaxies

Post-starburst galaxies are believed to be in a rapid transition between major merger starbursts and quiescent ellipticals. Their optical spectrum is dominated by A-type stars, suggesting a starburst that was quenched recently. While optical observations suggest little ongoing star formation, some have been shown to host significant molecular gas reservoirs. This led to the suggestion that gas depletion is not required to end the starburst, and that star formation is suppressed by other processes. We present NOEMA CO(1−0) observations of 15 post-starburst galaxies with emission lines consistent with active galactic nucleus (AGN) photoionization. We collect post-starburst candidates with molecular gas measurements from the literature, with some classified as classical E + A, while others with line ratios consistent with AGN and/or shock ionization. Using far-infrared observations, we show that systems that were reported to host exceptionally large molecular gas reservoirs host in fact obscured star formation, with some systems showing star formation rates comparable to ULIRGs. Among E + A galaxies with molecular gas measurements, 7 out of 26 (26 per cent) host obscured starbursts. Using far-infrared observations, post-starburst candidates show similar SFR– M H 2 and Kennicutt–Schmidt relations to those observed in star-forming and starburst galaxies. In particular, there is no need to hypothesize star formation quenching by processes other than the consumption of molecular gas by star formation. The combination of optical, far-infrared, and CO observations indicates that some regions within these galaxies have been recently quenched, while others are still forming stars in highly obscured regions. All this calls into question the traditional interpretation of such galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.

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

MACAW v1.0

The ability to embed molecules in a numeric space is essential in order to build mathematical and machine-learning models describing molecular properties or other processes affected by molecules. MACAW is a cheminformatic tool that allows embedding small molecules into a multidimensional numeric space. In the embedding, each molecule is assigned a numeric vector that captures information of the molecule in relation to other molecules, and that vector can be used as input to mathematical models. Molecules that are more similar to each other are embedded closer in this numeric space, whereas molecules that are more different are embedded further away. One advantage of the MACAW embedding technology compared to established alterantives is that it is fast and does not require extensive computational resources or expertise. In particular, MACAW embeddings can be used as input to mathematical models without the need for variable cleaning or feature selection, saving time and simplifying their use. On the other hand, MACAW also contains methods to generate new molecules and to recommend new molecules satisfying a desired molecular property. The generation of new molecules can be biased based on an input set of molecules, effectively generating molecular diversity around it. In its turn, MACAW's molecular recommendation tool is a novel method for evolving molecules in silico towards a desired molecular specification. In this method, the biased molecular generator tool is applied iteratively in combination with a molecular selection step. As a result, in each iteration the molecules selected by the software are increasingly closer to the desired specification. Both the molecular generation and the molecular recommendation tools are very fast, efficient, and intuitive to use.

Roger, VincentBlay↗
DOE OSTI2025

Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders

In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Relativistic Effects From Coupled-Cluster Theory

We discuss the theory and computational challenges of the relativistic coupled-cluster methods. Example calculations of heavy-atom-containing molecules are then presented to demonstrate the importance of scalar-relativistic, spin-orbit coupling, and electron-correlation effects on molecular properties as well as the applicability and usefulness of relativistic coupled-cluster methods in calculations aiming at high-accuracy results. Here, a unique applicability of the spinor-based relativistic coupled-cluster methods is also highlighted using the calculations of open shell actinide-containing small molecules. As a result, a summary is given together with an outlook into future developments.

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

pnnl/emp-gnn

Efficient Graph Neural Network for Predicting Molecular Properties software can compare the prediction quality with ab initio DFT results reported by the high-performance state-of-theart NWChem quantum chemistry package [1] through Mean Absolute Error (MAE) obtained by the fitting between DFT and model predictions i.e, MPNN [2]. We also demonstrated the performance benefits by grouping large molecules by atom sizes, and executing GNN models on different types of resources. Since the training times depend on the number of atoms, we demonstrate the impact of distributing the workloads on two GPUs with varying capabilities (e.g., NVIDIA A100 vs. GeForce RTX 2080 Ti) to optimize the efficiency. We are at the precipice of broad adoption of GNNs for molecular property prediction tasks; hence, our work is timely by comparing model prediction against classical approaches with the intent of providing initial screening for specific classes of molecules.

Lee, Hyungro↗
DOE OSTI2025

Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model

Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms’ approximations. Our model accurately predicted the excitation energies of the trimer and tetramer of perylene and tetracene and estimated S1 oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different couplings on their size dependency. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗