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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Fluorescence from photofragments as an aid in identifying new molecular states - The N2 case

Synchrotron radiation has been used to measure the excitation function for producing fluorescence in the 1050-1800 A region through photodissociative excitation of N2 in the 400-620 A region and the photoabsorption cross section of N2 in the 600-670 A region. From the fluorescence excitation function there is found (1) a molecular state(s) in the 21-22.5 eV region which has not been observed in absorption and photoionization spectra, (2) structures which correlate with known two-electron excited Rydberg states, and (3) a decrease in fluorescence cross section at photon energies greater than 23.8 eV, which indicates the existence of new competing processes. The observed molecular state(s) as well as the weak absorption structure in the 19-21 eV region are tentatively assigned as a Rydberg series leading to the D2Pi(g) of N2(+). The newly observed Rydberg states in the 21-22.5 eV region may account for the unusual behavior recently reported in photoelectron spectroscopy results.

Wu, C. Y. R.↗
DOE OSTI2020

Localization in the SCAN meta-generalized gradient approximation functional leading to broken symmetry ground states for graphene and benzene

Density functional theory calculations play a central role in understanding chemical and solid-state systems. Progress depends on density functionals that accurately reproduce both energies, for thermochemistry, and properly describe ground states and other properties that are of interest. The Cr dimer, benzene and graphene are especially important benchmark systems for quantum chemistry and condensed matter physics. The Strongly Constrained and Appropriately Normed (SCAN) functional, which is an advanced meta-generalized gradient approximation functional that significantly improves molecular energies is shown to perform poorly for the Cr dimer. This is connected with its poor performance for itinerant solid-state magnets and is a consequence of over localization of electrons, thus illustrating an analogy between the Cr dimer and itinerant magnets. The Cr dimer is a notoriously difficult system for density functionals. However, we additionally find that SCAN predicts an incorrect symmetry broken ground state for 2D graphene and for the benzene molecule, which is surprising considering that ground states of these are known to be well described even by the simplest local density approximation. We show that SCAN overly favors localized spin polarized states, which is a serious deficiency of this approach. Thus, the challenge of finding density functionals that accurately treat both localized and delocalized electronic systems remains.

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

Characterization of the Interfacial Orientation and Molecular Conformation in a Glass-Forming Organic Semiconductor

The ability to control structure in molecular glasses has enabled them to play a key role in modern technology; in particular, they are ubiquitous in organic light-emitting diodes. While the interplay between bulk structure and optoelectronic properties has been extensively investigated, few studies have examined molecular orientation near buried interfaces despite its critical role in emergent functionality. Direct, quantitative measurements of buried molecular orientation are inherently challenging, and many methods are insensitive to orientation in amorphous soft matter or lack the necessary spatial resolution. To overcome these challenges, we use polarized resonant soft X-ray reflectivity (pRSoXR) to measure nanometer-resolved, molecular orientation depth profiles of vapor-deposited thin films of an organic semiconductor Tris(4-carbazoyl-9-ylphenyl)amine (TCTA). Our depth profiling approach characterizes the vertical distribution of molecular orientation and reveals that molecules near the inorganic substrate and free surface have a different, nearly isotropic orientation compared to those of the anisotropic bulk. Comparison of pRSoXR results with near-edge X-ray absorption fine structure spectroscopy and optical spectroscopies reveals that TCTA molecules away from the interfaces are predominantly planar, which may contribute to their attractive charge transport qualities. Buried interfaces are further investigated in a TCTA bilayer (each layer deposited under separate conditions resulting in different orientations) in which we find a narrow interface between orientationally distinct layers extending across ≈1 nm. Coupling this result with molecular dynamics simulations provides additional insight into the formation of interfacial structure. This study characterizes the local molecular orientation at various types of buried interfaces in vapor-deposited glasses and provides a foundation for future studies to develop critical structure–function relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

General contraction of Gaussian basis sets. Part 2: Atomic natural orbitals and the calculation of atomic and molecular properties

A recently proposed scheme for using natural orbitals from atomic configuration interaction (CI) wave functions as a basis set for linear combination of atomic orbitals (LCAO) calculations is extended for the calculation of molecular properties. For one-electron properties like multipole moments, which are determined largely by the outermost regions of the molecular wave function, it is necessary to increase the flexibility of the basis in these regions. This is most easily done by uncontracting the outmost Gaussian primitives, and/or by adding diffuse primitives. A similar approach can be employed for the calculation of polarizabilities. Properties which are not dominated by the long-range part of the wave function, such as spectroscopic constants or electric field gradients at the nucleus, can generally be treated satisfactorily with the original atomic natural orbital (ANO) sets.

Almloef, Jan↗
NASA NTRS1999

Modeling the Activity of Single Genes

The central dogma of molecular biology states that information is stored in DNA, transcribed to messenger RNA (mRNA) and then translated into proteins. This picture is significantly augmentated when we consider the action of certain proteins in regulating transcription. These transcription factors provide a feedback pathway by which genes can regulate one another's expression as mRNA and then as protein. To review: DNA, RNA and proteins have different functions. DNA is the molecular storehouse of genetic information. When cells divide, the DNA is replicated, so that each daughter cell maintains the same genetic information as the mother cell. RNA acts as a go-between from DNA to proteins. Only a single copy of DNA is present, but multiple copies of the same piece of RNA may be present, allowing cells to make huge amounts of protein. In eukaryotes (organisms with a nucleus), DNA is found in the nucleus only. RNA is copied in the nucleus then translocates(moves) outside the nucleus, where it is transcribed into proteins. Along the way, the RNA may be spliced, i.e., may have pieces cut out. RNA then attaches to ribosomes and is translated to proteins. Proteins are the machinery of the cell other than DNA and RNA, all the complex molecules of the cell are proteins. Proteins are specialized machines, each of which fulfills its own task, which may be transporting oxygen, catalyzing reactions, or responding to extracellular signals, just to name a few. One of the more interesting functions a protein may have is binding directly or indirectly to DNA to perform transcriptional regulation, thus forming a closed feedback loop of gene regulation. The structure of DNA and the central dogma were understood in the 50s; in the early 80s it became possible to make arbitrary modifications to DNA and use cellular machinery to transcribe and translate the resulting genes; more recently, genomes (i.e., the complete DNA sequence) of many organisms have been sequenced. This large-scale sequencing began with simple organisms, viruses and bacteria, progressed to eukaryotes such as yeast, and more recently (1998) progressed to a multi-cellular animal, the nematode Caenorhabditis elegans. Sequencers have now moved on to the fruit fly Drosophila melanogaster, whose sequence is slated for completion by the end of 1999. The human genome project is expected to determine the complete sequence of all 3 billion bases of human DNA within the next five years. In the wake of genome-scale sequencing, further instrumentation is being developed to assay gene expression and function on a comparably large scale. Much of the work in computational biology focuses on computational tools used in sequencing, finding genes that are related to a particular gene, finding which parts of the DNA code for proteins and which do not, understanding what proteins will be formed from a given length of DNA, predicting how the proteins will fold from a one-dimensional structure into a three dimensional structure, and so on. Much less computational work has been done regarding the function of proteins. One reason for this is that different proteins function very differently, and so work on protein function is very specific to certain classes of proteins. There are, for example, proteins such enzymes that catalyze various intracellular reactions, receptors that respond to extracellular signals and ion channels that regulate the flow of charged particles into and out of the cell. In this chapter, we will consider a particular class of proteins called transcription factors(TFs), which are responsible for regulating when a certain gene is expressed in a certain cell, which cells it is express in, and how much is expressed. Understanding these processes will involve developing a deeper understanding of transcription, translation, and the cellular processes that control those processes. All of these elements fall under the aegis of gene regulation or more narrowly transcriptional regulation. Some of the key questions in gene regulation are: What genes are expressed in a certain cell at a certain time? How does gene expression differ from cell to cell in a multicellular organism? Which proteins act as transcription factors, i.e., are important in regulating gene expression? From questions like these, we hope to understand which genes are important for various macroscopic processes. Nearly all of the cells of a multicellular organism contain the same DNA. Yet this same genetic information yields a large number of different cell types. The fundamental difference between a neuron and a liver cell, for example, is which genes are expressed. Thus understanding gene regulation is an important step in understanding development. Furthermore, understanding the usual genes that are expressed in cells may give important clues about various diseases. Some diseases, such as sickle cell anemia and cystic fibrosis, are caused by defects in single, non-regulatory genes; others, such as certain cancers, are caused when the cellular control circuitry malfunctions - an understanding of these diseases will involve pathways of multiple interacting gene products. There are numerous challenges in the area of understanding and modeling gene regulation. First and foremost, biologists would like to develop a deeper understanding of the processes involved, including which genes and families of genes are important, how they interact, etc. From a computation point of view, there has been embarrassingly little work done. In this chapter there are many areas in which we can phrase meaningful, non-trivial computational questions, but questions that have not been addressed. Some of these are purely computational (what is a good algorithm for dealing with a model of type X) and others are more mathematical (given a system with certain characteristics, what sort of model can one use? How does one find biochemical parameters from system-level behavior using as few experiments as possible?). In addition to biological and algorithmic problems, there is also the ever-present issue of theoretical biology - what general principles can be derived from these systems, what can one do with models other than just simulate time-courses, what can be deduced about a class of systems without knowing all the details? The fundamental challenge to computationalists and theorists is to add value to the biology - to use models, modeling techniques and algorithms to understand the biology in new ways.

Mjolsness, Eric↗
DOE OSTI2020

Mechanisms of activation and desensitization of full-length glycine receptor in lipid nanodiscs

Glycinergic synapses play a central role in motor control and pain processing in the central nervous system. Glycine receptors (GlyRs) are key players in mediating fast inhibitory neurotransmission at these synapses. While previous high-resolution structures have provided insights into the molecular architecture of GlyR, several mechanistic questions pertaining to channel function are still unanswered. Here, we present Cryo-EM structures of the full-length GlyR protein complex reconstituted into lipid nanodiscs that are captured in the unliganded (closed), glycine-bound (open and desensitized), and allosteric modulator-bound conformations. A comparison of these states reveals global conformational changes underlying GlyR channel gating and modulation. The functional state assignments were validated by molecular dynamics simulations, and the observed permeation events are in agreement with the anion selectivity and conductance of GlyR. These studies provide the structural basis for gating, ion selectivity, and single-channel conductance properties of GlyR in a lipid environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Molecular growth upon ionization of van der Waals clusters containing HCCH and HCN is a pathway to prebiotic molecules

The growth mechanisms of organic molecules in an ionizing environment such as the interstellar medium are not completely understood. Here we examine by means of ab initio molecular dynamics (AIMD) simulations and density functional theory (DFT) computations the possibility of bond formation and molecular growth upon ionization of van der Waals clusters of pure HCN clusters, and mixed clusters of HCN and HCCH, both of which are widespread in the interstellar medium. Ionization of van der Waals clusters can potentially lead to growth in low temperature and low-density environments. Our results show, that upon ionization of the pure HCN clusters, strongly bound stable structures are formed that contain N∙∙∙H bonds, and growth beyond pairwise HCN molecules is seen only in a small percentage of cases. In contrast, mixed clusters, where HCCH is preferentially ionized over HCN, can grow up to 3 or 4 units long with new carbon–carbon and carbon–nitrogen covalent bonds. Moreover, cyclic molecules formed, such as the radical cation of pyridine, which is a prebiotic molecule. The results presented here are significant as they provide a feasible pathway for molecular growth of small organic molecules containing both carbon and nitrogen in cold and relatively denser environments such as in dense molecular clouds but closer to the photo-dissociation regions, and protoplanetary disks. In the mechanism we propose, first, a neutral van der Waals cluster is formed. Once the cluster is formed it can undergo photoionization which leads to chemical reactivity without any reaction barrier.

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

Demonstration of an x-ray Raman spectroscopy setup to study warm dense carbon at the high energy density instrument of European XFEL

We present a proof-of-principle study demonstrating x-ray Raman Spectroscopy (XRS) from carbon samples at ambient conditions in conjunction with other common diagnostics to study warm dense matter, performed at the high energy density scientific instrument of the European x-ray Free Electron Laser (European XFEL). We obtain sufficient spectral resolution to identify the local structure and chemical bonding of diamond and graphite samples, using highly annealed pyrolytic graphite spectrometers. Due to the high crystal reflectivity and XFEL brightness, we obtain signal strengths that will enable accurate XRS measurements in upcoming pump–probe experiments with a high repetition-rate, where the samples will be pumped with high-power lasers. Molecular dynamics simulations based on density functional theory together with XRS simulations demonstrate the potential of this technique and show predictions for high-energy-density conditions. Our setup allows simultaneous implementation of several different diagnostic methods to reduce ambiguities in the analysis of the experimental results, which, for warm dense matter, often relies on simplifying model assumptions. The promising capabilities demonstrated here provide unprecedented insights into chemical and structural dynamics in warm dense matter states of light elements, including conditions similar to the interiors of planets, low-mass stars, and other celestial bodies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

MolSym : A Python package for handling symmetry in molecular quantum chemistry

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program’s environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree–Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. Furthermore, this package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

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

X-ray crystallographic and hydrogen deuterium exchange studies confirm alternate kinetic models for homolog insulin monomers

Despite the crucial role of various insulin analogs in achieving satisfactory glycemic control, a comprehensive understanding of their in-solution dynamic mechanisms still holds the potential to further optimize rapid insulin analogs, thus significantly improving the well-being of individuals with Type 1 Diabetes. Here, we employed hydrogen-deuterium exchange mass spectrometry to decipher the molecular dynamics of newly modified and functional insulin analog. A comparative analysis of H/D dynamics demonstrated that the modified insulin exchanges deuterium atoms faster and more extensively than the intact insulin aspart. Additionally, we present new insights derived from our 2.5 Å resolution X-ray crystal structure of modified hexamer insulin analog at ambient temperature. Furthermore, we obtained a distinctive side-chain conformation of the Asn3 residue on the B chain (AsnB3) by operating a comparative analysis with a previously available cryogenic rapid-acting insulin structure (PDB_ID: 4GBN). The experimental conclusions have demonstrated compatibility with modified insulin’s distinct cellular activity, comparably to aspart. Additionally, the hybrid structural approach combined with computational analysis employed in this study provides novel insight into the structural dynamics of newly modified and functional insulin vs insulin aspart monomeric entities. It allows further molecular understanding of intermolecular interrelations driving dissociation kinetics and, therefore, a fast action mechanism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2024

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.

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

Melting point of iron at high pressure: An assessment of uncertainties and effect of electronic temperature

An accurate calculation of the melting point of iron at various pressures in the Earth's core is important for understanding the core structure, geodynamo, and the Earth's history. Previous studies have assessed the melt line of iron at these extreme conditions using various experimental measurement techniques as well as both ab initio and classic molecular dynamics simulations. However, experimental measurements have uncertainties up to several hundred Kelvin, and inconsistencies remain among simulation results. Here in this work, we propose an iterative framework that couples density functional theory (DFT) calculations and molecular dynamics simulations performed using an ensemble of interatomic potentials to assess the effect of electronic temperature on the melting point. We systematically validate the potentials by comparing lattice constants and phonon dispersion curves at 0 K and enthalpy differences between liquid and HCP, FCC, BCC phases of iron close to the melt line at 300 GPa with DFT. Our results show that HCP iron melts at 6144 K (at 300 GPa), BCC phase is thermodynamically unstable, and FCC is metastable at this temperature. The melting points of FCC and BCC phases at 300 GPa are 5858 and 5647 K, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1995

Potential ligand-binding residues in rat olfactory receptors identified by correlated mutation analysis

A family of G-protein-coupled receptors is believed to mediate the recognition of odor molecules. In order to identify potential ligand-binding residues, we have applied correlated mutation analysis to receptor sequences from the rat. This method identifies pairs of sequence positions where residues remain conserved or mutate in tandem, thereby suggesting structural or functional importance. The analysis supported molecular modeling studies in suggesting several residues in positions that were consistent with ligand-binding function. Two of these positions, dominated by histidine residues, may play important roles in ligand binding and could confer broad specificity to mammalian odor receptors. The presence of positive (overdominant) selection at some of the identified positions provides additional evidence for roles in ligand binding. Higher-order groups of correlated residues were also observed. Each group may interact with an individual ligand determinant, and combinations of these groups may provide a multi-dimensional mechanism for receptor diversity.

NASA Discipline Neuroscience↗
DOE OSTI2024

Water–Hydrocarbon Interactions in Anionic Pyrene Monohydrate

Interactions between water and polycyclic aromatic hydrocarbons are essential in many aspects of chemistry, from interstellar and atmospheric processes to interfacial hydrophobicity and wetting phenomena. Despite their growing importance, the intermolecular potentials of the water-hydrocarbon interactions are underdeveloped compared to water-water potentials, and there are similarly few experimental probes that are sensitive to the details of the water-hydrocarbon potential. We present a combined experimental and computational study of anionic pyrene monohydrate, one of the simplest water/hydrocarbon clusters. The action spectrum in the OH region of the mass-selected cluster ion provides a rigorous benchmark for intermolecular potentials and computational methodologies. We identify missing intermolecular interactions and shortcomings in conventional dynamics calculations by comparing experimental data to density functional theory and classical molecular dynamics calculations. Kinetic trapping is prevalent, even for one water molecule and one pyrene molecule, leading to slow equilibration in conventional molecular dynamics calculations, even on nanosecond timescales and at low temperatures (50 K). At constant energy, temperature fluctuations for the pair of molecules are substantial. Immersing the system in a bath of soft spheres and employing parallel tempering alleviates kinetic trapping and dampens temperature fluctuations, bringing the system closer to the thermodynamic limit. With such augmented sampling, a simple, flexible water model reproduces the linewidth and the asymmetric broadening of the symmetric OH stretching mode, which we assign to spectral diffusion. In the OH stretching region, dynamics calculations predict a more intense antisymmetric peak than experiments observe but do not predict the bimodal split symmetric peak that the experiments show. Furthermore, our work suggests that electronic polarization, missing in the empirical force field, is responsible for the first discrepancy and that quantum nuclear effects, captured neither in density functional theory nor in classical dynamics, may be responsible for the second.

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

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom

Abstract Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1998

A Synthesis of Fluid Dynamics and Quantum Chemistry for the Design of Nanoelectronics

In 1959, during a famous lecture entitled "There's Plenty of Room at the Bottom", Richard Feynman focused on the startling technical possibilities that would exist at the limit of miniaturization, that being atomically precise devices with dimensions in the nanometer range. A nanometer is both a convenient unit of length for medium to large sized molecules, and the root of the name of the new interdisciplinary field of "nanotechnology". Essentially, "nanoelectronics" denotes the goal of shrinking electronic devices, such as diodes and transistors, as well as integrated circuits of such devices that can perform logical operations, down to dimensions in the range of 100 nanometers. The thirty-year hiatus in the development of nanotechnology can figuratively be seen as a period of waiting for the bottom-up and atomically precise construction skills of synthetic chemistry to meet the top-down reductionist aspirations of device physics. The sub-nanometer domain of nineteenth-century classical chemistry has steadily grown, and state-of-the-art supramolecular chemistry can achieve atomic precision in non-repeating molecular assemblies of the size desired for nanotechnology. For nanoelectronics in particular, a basic understanding of the electron transport properties of molecules must also be developed. Quantum chemistry provides powerful computational methods that can accurately predict the properties of small to medium sized molecules on a desktop workstation, and those of large molecules if one has access to a supercomputer. Of the many properties of a molecule that quantum chemistry routinely predicts, the ability to carry a current is one that had not even been considered until recently. "Currently", there is a controversy over just how to define this key property. Reminiscent of the situation in high-Tc superconductivity, much of the difficulty arises from the different models that are used to simplify the complex electronic structure of real materials. A model-independent approach has been proposed, that sacrifices the plentiful molecular orbitals and Bloch functions of conventional approaches, for a single three-dimensional observable quantity, the electron momentum density Pi(sub rho). This quantity is simply the probability of any electron having momentum rho, multiplied by the total number of electrons in the system (the position of the electron is uncertain). We have explored the utility of this new approach in providing a fundamental understanding of the electron transport properties of molecules that have provi been nominated as candidates for components in the design of nanoelectronics; phenylene-ethynylene oligomers. Some of the molecular systems that have been studied are sketched below.

MacDougall, Preston J.↗