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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗
DOE OSTI2022

Hybrid Nanocrystals of Small Molecules and Chemically Disordered Polymers

Organic crystals formed by small molecules can be highly functional but are often brittle or insoluble structures with limited possibilities for use or processing from a liquid phase. A possible solution is the nanoscale integration of polymers into organic crystals without sacrificing long-range order and therefore function. This enables the organic crystals to benefit from the advantageous mechanical and chemical properties of the polymeric component. We report here on a strategy in which small molecules co-crystallize with side chains of chemically disordered polymers to create hybrid nanostructures containing a highly ordered lattice. Synchrotron X-ray scattering, absorption spectroscopy, and coarse-grained molecular dynamics simulations reveal that the polymer backbones form an “exo-crystalline” layer of disordered chains that wrap around the nanostructures becoming a handle for interesting properties. The morphology of this “hybrid bonding polymer” nanostructure is dictated by the competition between the polymers’ entropy and the enthalpy of the lattice allowing for control over the aspect ratio of the nanocrystal by changing the degree of polymer integration. Further, we observed that nanostructures with an exo-crystalline layer of polymer exhibit enhanced fracture strength, self-healing capacity, and dispersion in water, which benefits their use as light-harvesting assemblies in photocatalysis. Guided by computation, future work could also explore these hybrid nanostructures as building blocks for functional materials.

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

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Diazo coupling for surface attachment of small molecules to TiO 2 nanoparticles

Robust surface attachment of molecular species to metal oxide semiconductors is desirable for many applications. In this work, we report the interfacial diazo coupling of surface-bound amines with aromatics to bind them to the surface of TiO 2 nanoparticles via a robust siloxane anchor and a diazo link. The technique shows potential for the inexpensive, stable, modular and tunable attachment of molecules to metal oxide surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

Computer Simulations of Small Molecules in Membranes: Insights from Computer Simulations into the Interactions of Small Molecules with Lipid Bilayers

Two of Ernest Overton's lasting contributions to biology are the Meyer-Overton relationship between the potency of an anesthetic and its solubility in oil, and the Overton rule which relates the permeability of a membrane to the oil-water partition coefficient of the permeating molecule. A growing body of experimental evidence, however, cannot be reconciled with these theories. In particular, the molecular nature of membranes, unknown to Overton, needs to be included in any description of these phenomena. Computer simulations are ideally suited for providing atomic-level information about the behavior of small molecules in membranes. The authors discuss simulation studies relevant to Overton's ideas. Through simulations it was found that anesthetics tend to concentrate at interfaces and their anesthetic potency correlates better with solubility at the water-membrane interface than with solubility in oil. Simulation studies of membrane permeation revealed the anisotropic nature of the membranes, as evidenced, for example, by the highly nonuniform distribution of free volume in the bilayer. This, in turn, influences the diffusion rates of solutes, which increase with the depth in the membrane. Small solutes tend to move by hopping between voids in the bilayer, and this hopping motion may be responsible for the deviation from the Overton rule of the permeation rates of these molecules.

Pohorille, Andrew↗
DOE OSTI2024

A General Approach to Activate Second‐Scale Room Temperature Photoluminescence in Organic Small Molecules

Organic small molecules that exhibit second‐scale phosphorescence at room temperature are of interest for potential applications in sensing, anticounterfeiting, and bioimaging. However, such materials systems are uncommon—requiring millisecond to second‐scale triplet lifetimes, efficient intersystem crossing, and slow rates of nonradiative recombination. Here, a simple and scalable approach is demonstrated to activate long‐lived phosphorescence in a wide variety of molecules by suspending them in rigid polymer hosts and annealing them above the polymer's glass transition temperature. This process produces submicron aggregates of the chromophore, which suppresses intramolecular motion that leads to nonradiative recombination and minimizes triplet–triplet annihilation that quenches phosphorescence in larger aggregates. In some cases, evidence of excimer‐mediated intersystem crossing that enhances triplet generation in aggregated chromophores is found. In short, this approach circumvents the current design rules for long‐lived phosphors, which will streamline their discovery and development.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimizing the self-assembly of conjugated polymers and small molecules through structurally programmed non-covalent control

Organic conjugated polymers and oligomers are key electronic materials for applications such as transistors, photovoltaics, and light emitting devices due to their potential for solution processability, mechanical flexibility, and precise structure-based tuning compared to inorganic materials. In dilute environments, the optoelectronic properties of conjugated polymers are largely governed by their constitutional structure and, to a lesser degree, their solution-state intramolecular configuration. In the solid state, intramolecular conformation and intermolecular electronic coupling impact these properties substantially, especially in relation to device performance. Therefore, an increasingly important area of research concerning conjugated materials is developing design strategies aimed at optimizing the solid-state packing for electronic applications. Programming solid-state packing arrangements through discrete non-covalent interactions is an emerging strategy within the context of conjugated polymers. This review focuses on the use of the two most prevalent discrete and directional interactions used to dictate the self-assembly of conjugated polymers and oligomers—hydrogen bonds and chalcogen bonds. Further, we also discuss how these design motifs can imbue conjugated materials with appealing physical properties while simultaneously retaining or improving electronic capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Molecular Design of Al(II) Intermediates for Small Molecule Activation

Promoting societally important small molecule activation processes with earth-abundant metals is foundational for a sustainable chemistry future. In this context, mapping new reaction pathways that would enable abundant main-group elements to mimic the behaviors of d- and f-block elements is facilitated by exploring unusual oxidation states. The most abundant metal on earth, aluminum, has been well studied in the Lewis acidic +III and Lewis basic +I oxidation states but rarely in the potentially biphilic +II oxidation state until recently, when a renaissance of Al(II) chemistry emerged from a range of research groups. In this Perspective, we review the chemistry of mononuclear Al radicals, including both Al-centered radicals (i.e., Al(II) compounds) and redox non-innocent systems (i.e., formally Al(II) species that are physically Al(III) with ligand-centered radicals), with an emphasis on small molecule reactivity. We also provide a meta-analysis of the Al(II) literature to summarize how different design strategies (e.g., redox non-innocence, strained coordination geometries) have been shown to impart biphilic character to Al radicals and tune their behavior, thus allowing Al radicals to mimic the chemistry of certain d- and f-block metal ions such as Ti(III) and Sm(II). We expect these molecular design concepts to inform future Al(II) studies as the chemistry of this unusual oxidation state of Al continues to grow.

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

Molecular packing modulation enabling optimized blend morphology and efficient all small molecule organic solar cells

The molecular planarity of a small molecule donor is one of the key factors affecting its molecular packing behavior, the morphology of the related active blend and thus the photovoltaic properties of all small molecule organic solar cells (ASM-OSCs). Here, a series of new benzodithiophene (BDT)-based A-π-D-π-A type small molecules were developed, namely SD1, SD2 and SD3, by combining the chemical modification of side chain (sulfuration), π-bridge (alkoxy groups introducing) and end group (different type or different alkyl chain). The sulfuration of the BDT side chains and introducing alkoxy group to the π-bridge could induce intermolecular S … S and intramolecular S … O non-covalent interactions for higher planarity of these small molecules. The rhodanine and cyanoacetate end groups showed less effect on the optical and electronic properties but significantly affected the molecular packing behavior and blend morphology. From SD1:Y6-T to SD2:Y6-T and SD3:Y6-T, the face-to-face packing was gradually reduced, while the face-to-edge packing was increased, which resulted in lowered exciton dissociation efficiency, further decreased the device performance. SD1:Y6-T blend exhibited more favorable face-to-face packing and ideal morphology for efficient charge dissociation and transport, contributing to a champion PCE of 10.12%. This work provided a deep insight of the donor and acceptor packing behavior effect on the photovoltaic property by molecular design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Layers Can Be Deceiving: A Hopping Model for Small Molecule Diffusion in TATB Crystal

Sorption of small molecule gases in materials can play a significant role in their long‐term stability and compatibility within multi‐material assemblies. While many material properties of the insensitive high explosive TATB (1,3,5‐triamino‐2,4,6‐trinitrobenzene) are well understood, very little is known regarding its permeability to gases. TATB crystal exhibits a graphitic‐like layered packing structure that evokes a mental schema in which the layers form nanoscopic channels, but it is unclear whether this structure promotes gas transport. Here, we use molecular dynamics (MD) simulations to predict transport of small molecules through TATB single crystal. An approach to fit classical force fields is developed to model TATB interactions with H 2 O, He, Ne, and Ar, which is then combined with steered MD to probe gas transport along selected directions in the crystal. We find that small molecule transport occurs via a hopping mechanism that exhibits distinct jumps between interstitial sites and is substantially faster normal to the layers as compared to through them. This result stems from the finding that intralayer junctions between adjacent TATB molecules are the most stable interstitial sites and that energetic barriers are lower for hopping between adjacent layers. An empirical model for diffusion rate based on the MD data shows that the rate decays exponentially with increasing molecular radius and is negligibly small for all molecules larger than He, including common atmospheric gases. These findings have implications for the interpretation of experiments that measure surface area, material response to extreme conditions, and are expected to help constrain models for material aging.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High Miscibility Compatible with Ordered Molecular Packing Enables an Excellent Efficiency of 16.2% in All-Small-Molecule Organic Solar Cells

In all-small-molecule organic solar cells (ASM-OSCs), a high short-circuit current (Jsc) usually needs a small phase separation, while a high fill factor (FF) is generally realized in a highly ordered packing system. However, small domain and ordered packing always conflicted each other in ASM-OSCs, leading to a mutually restricted J sc and FF. Here, in this study, alleviation of the previous dilemma by the strategy of obtaining simultaneous good miscibility and ordered packing through modulating homo- and heteromolecular interactions is proposed. By moving the alkyl-thiolation side chains from the para- to the meta-position in the small-molecule donor, the surface tension and molecular planarity are synchronously enhanced, resulting in compatible properties of good miscibility with acceptor BTP-eC9 and strong self-assembly ability. As a result, an optimized morphology with multi-length-scale domains and highly ordered packing is realized. The device exhibits a long carrier lifetime (39.8 μs) and fast charge collection (15.5 ns). A record efficiency of 16.2% with a high FF of 75.6% and a J sc of 25.4 mA cm –2 in the ASM-OSCs is obtained. These results demonstrate that the strategy of simultaneously obtaining good miscibility with high crystallinity could be an efficient photovoltaic material design principle for high-performance ASM-OSCs.

14 SOLAR ENERGY↗
DOE OSTI2020

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗