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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2021

Extreme Molecular Complexity Resulting in a Continuum of Carbonaceous Species in Biomass Burning Tar Balls from Wildfire Smoke

Biomass burning emits a wide range of carbonaceous particles into the atmosphere and has negative impacts on human health and the Earth’s radiative balance. Nonvolatile spherical organic aerosol particles, commonly known as tar balls, represent one of the most abundant particles in aged biomass burning smoke. However, the detailed molecular-level composition of ambient tar balls is largely unknown but critical to access their environmental impacts. Ambient aerosol samples collected during a wildfire event, which were ~90% tar balls by number fraction, were analyzed using ultrahigh resolution Orbitrap Elite mass spectrometry with four complementary ionization modes. Our results show the molecular composition of tar balls to be complex, composed of over 10,000 molecular formulas. Model estimated saturation mass concentrations and relative humidity dependent glass transition temperatures were consistent with low volatility and solid morphology as expected for tar balls. Room-temperature evaporation kinetics showed that these particles were composed of ~90% non-volatile species. The molecular complexity detected here signifies a continuum of carbonaceous species, ranging from C3 to C45 with continuous ranges of oxygenation and hydrogen saturation for each Cn. Approximately 24% of molecular formulas were estimated the be highly aromatic, which could indicate chemical compounds with negative health effects, and which may contribute to visible light absorption. The carbon continuum observed here has significant implications for the molecular characterization of atmospheric organic matter. The level of complexity detected here should not be ignored in future studies and we demonstrate that multiple analytical methods may be required to suitably interpret this complexity on a molecular level.

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

Strategies for breaking molecular scaling relationships for the electrochemical CO 2 reduction reaction

The electrocatalytic CO 2 reduction reaction (CO 2 RR) is a promising strategy for converting CO 2 to fuels and value-added chemicals using renewable energy sources. Molecular electrocatalysts show promise for the selective conversion of CO 2 to single products with catalytic activity that can be tuned through synthetic structure modifications. However, for the CO 2 RR by traditional molecular catalysts, beneficial decreases in overpotentials are usually correlated with detrimental decreases in catalytic activity. This correlation is sometimes referred to as a “molecular scaling relationship”. Overcoming this inverse correlation between activity and effective overpotential remains a challenge when designing new, efficient molecular catalyst systems. Here, in this perspective, we discuss some of the concepts that give rise to the molecular scaling relationships in the CO 2 RR by molecular catalysts. We then provide an overview of some reported strategies from the last decade for breaking these scaling relationships. We end by discussing strategies and progress in our own research designing efficient molecular catalysts with redox-active ligands that show high activity at low effective overpotentials for the CO 2 RR.

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

Unified understanding of the impact of semiflexibility, concentration, and molecular weight on macromolecular-scale ring diffusion

Conformationally fluctuating, globally compact macromolecules such as polymeric rings, single-chain nanoparticles, microgels, and many-arm stars display complex dynamic behaviors due to their rich topological structure and intermolecular organization. Synthetic rings are hybrid objects with conformations that display both ideal random walk and compact globular features, which can serve as models of genomic DNA. To date, emphasis has been placed on the effect of ring molecular weight on their unusual behaviors. Here, we combine simulations and a microscopic force-level theory to build a unified understanding for how key aspects of ring dynamics depend on different tunable molecular properties including backbone rigidity, monomer concentration, degree of traditional entanglement, and molecular weight. Our large-scale molecular dynamics simulations of ring melts with very different backbone stiffnesses reveal unanticipated behaviors which agree well with our generalized theory. This includes a universal master curve for center-of-mass diffusion constants as a function of molecular weight scaled by a chemistry and thermodynamic state-dependent critical molecular weight that generalizes the concept of an entanglement cross-over for linear chains. The key physics is how backbone rigidity and monomer concentration induced changes of the entanglement length, interring packing, degree of interpenetration, and liquid compressibility slow down space-time dynamic-force correlations on macromolecular scales. A power law decay of the center-of-mass diffusion constant with inverse molecular weight squared is the first consequence, followed by an ultraslow activated hopping transport regime. Our results set the stage to address slow dynamics and kinetic arrest in different families of compact synthetic and biological polymeric systems.

Science & Technology - Other Topics↗
DOE OSTI2022

Switching charge states in quasi-2D molecular conductors

Abstract 2D molecular entities build next-generation electronic devices, where abundant elements of organic molecules are attractive due to the modern synthetic and stimuli control through chemical, conformational, and electronic modifications in electronics. Despite its promising potential, the insufficient control over charge states and electronic stabilities must be overcome in molecular electronic devices. Here, we show the reversible switching of modulated charge states in an exfoliatable 2D-layered molecular conductor based on bis(ethylenedithio)tetrathiafulvalene molecular dimers. The multiple stimuli application of cooling rate, current, voltage, and laser irradiation in a concurrent manner facilitates the controllable manipulation of charge crystal, glass, liquid, and metal phases. The four orders of magnitude switching of electric resistance are triggered by stimuli-responsive charge distribution among molecular dimers. The tunable charge transport in 2D molecular conductors reveals the kinetic process of charge configurations under stimuli, promising to add electric functions in molecular circuitry.

Huang, Yulong (ORCID:0000000296160134)↗
DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗
DOE OSTI2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗
DOE OSTI2021

Single-Cell RNA-Seq Reveals Transcriptomic Heterogeneity and Post-Traumatic Osteoarthritis-Associated Early Molecular Changes in Mouse Articular Chondrocytes

Articular cartilage is a connective tissue lining the surfaces of synovial joints. When the cartilage severely wears down, it leads to osteoarthritis (OA), a debilitating disease that affects millions of people globally. The articular cartilage is composed of a dense extracellular matrix (ECM) with a sparse distribution of chondrocytes with varying morphology and potentially different functions. Elucidating the molecular and functional profiles of various chondrocyte subtypes and understanding the interplay between these chondrocyte subtypes and other cell types in the joint will greatly expand our understanding of joint biology and OA pathology. Although recent advances in high-throughput OMICS technologies have enabled molecular-level characterization of tissues and organs at an unprecedented resolution, thorough molecular profiling of articular chondrocytes has not yet been undertaken, which may be in part due to the technical difficulties in isolating chondrocytes from dense cartilage ECM. In this study, we profiled articular cartilage from healthy and injured mouse knee joints at a single-cell resolution and identified nine chondrocyte subtypes with distinct molecular profiles and injury-induced early molecular changes in these chondrocytes. We also compared mouse chondrocyte subpopulations to human chondrocytes and evaluated the extent of molecular similarity between mice and humans. This work expands our view of chondrocyte heterogeneity and rapid molecular changes in chondrocyte populations in response to joint trauma and highlights potential mechanisms that trigger cartilage degeneration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics

Simulating the coupled electronic and nuclear response of a molecule to light excitation requires the application of nonadiabatic molecular dynamics. However, when faced with a specific photophysical or photochemical problem, selecting the most suitable theoretical approach from the wide array of available techniques is not a trivial task. The challenge is further complicated by the lack of systematic method comparisons and rigorous testing on realistic molecular systems. This absence of comprehensive molecular benchmarks remains a major obstacle to advances within the field of nonadiabatic molecular dynamics. A CECAM workshop, Standardizing Nonadiabatic Dynamics: Towards Common Benchmarks, was held in May 2024 to address this issue. This Perspective highlights the key challenges identified during the workshop in defining molecular benchmarks for nonadiabatic dynamics. Specifically, this work outlines some preliminary observations on essential components needed for simulations and proposes a roadmap aiming to establish, as an ultimate goal, a community-driven, standardized molecular benchmark set.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Molecular axis distribution moments in ultrafast transient absorption spectroscopy: A path toward ultrafast quantum state tomography

In ultrafast time-resolved experiments with gas phase molecules, the alignment of the molecular axis relative to the polarization of the interacting laser pulses plays a crucial role in determining the dynamics following this light–matter interaction. The molecular axis distribution is influenced by the interacting pulses and is intrinsically linked to the electronic coherences of the excited molecules. However, in typical theoretical calculations of such interactions, the signal is either calculated for a single molecule in the molecular frame or averaged over all possible molecular orientations to compare with the experiment. Such averaging removes information about anisotropy in the molecular-axis distribution, even though anisotropic contributions can play a significant role in the measured experimental signal. Here, we calculate the laboratory frame transient electronic first-order polarization [P (1) ] spectra in terms of separated molecular frame and laboratory frame quantities. The laboratory frame polarizations are compared with orientation-averaged quantum master equation calculations, demonstrating that orientation-averaging captures only the isotropic contributions. We show that our formalism also allows us to evaluate the anisotropic contributions to the spectrum. Lastly, we discuss the application of this approach to achieve ultrafast quantum state tomography using transient absorption spectroscopy and field observables in nonlinear spectroscopy.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Molecular Hypernetworks for Exploration of Multi-Dimensional Metabolomics Data (Chyper)

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

DNA Strand Displacement Driven Molecular Additive Manufacturing (DSD-MAM)

The goal of this project was to validate two-dimensional molecular printers, initially selfassembled from DNA and then actuated by externally driven cycles of DNA strand displacement, as prototype integrated nanosystems for molecular additive manufacturing. Novel functionalities of these nanomachines were explored during this project, including the following: nanometer-precision positioning mechanisms based on DNA strand displacement with multivalent interactions for discrete stepping or else diffusive capture; integration of independently moving layers of DNA origami to achieve 2D controllable motion; integration of spatial positioning with deposition functionality. The principal importance of this project was to provide an essential step in the development of a new technology for atomically precise manufacturing. Our first generation molecular 2D printer offers several advantages over conventional DNA-origami patterning, such as faster prototyping, faster dynamic rearrangement of patterns, and the ability to respond with feedback. We anticipate that our first-generation molecular printers may inspire future generations of molecular printers with iterative improvements in robustness and throughput. Potential applications of atomically precise manufacturing include the following: photovoltaics; photosynthetic and fuel cells; thermoelectrics and anisotropic heat spreaders; solid-state lighting; molecular electronic and plasmonic circuits; selectively preamble membranes; self-repairing materials with high strength-to-weight and fracture resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantum bath augmented stochastic nonequilibrium atomistic simulations for molecular heat conduction

Classical molecular dynamics (MD) has been shown to be effective in simulating heat conduction in certain molecular junctions since it inherently takes into account some essential methodological components which are lacking in the quantum Landauer-type transport model, such as many-body full force-field interactions, anharmonicity effects and nonlinear responses for large temperature biases. However, the classical MD reaches its limit in the environments where the quantum effects are significant (e.g. with low-temperatures substrates, presence of extremely high frequency molecular modes). Here, we present an atomistic simulation methodology for molecular heat conduction that incorporates the quantum Bose–Einstein statistics into an “effective temperature” in the form of a modified Langevin equation. We show that the results from such a quasi-classical effective temperature MD method deviates drastically when the baths temperature approaches zero from classical MD simulations and the results converge to the classical ones when the bath approaches the high-temperature limit, which makes the method suitable for full temperature range. In addition, we show that our quasi-classical thermal transport method can be used to model the conducting substrate layout and molecular composition (e.g. anharmonicities, high-frequency modes). Anharmonic models are explicitly simulated via the Morse potential and compared to pure harmonic interactions to show the effects of anharmonicities under quantum colored bath setups. Finally, the chain length dependence of heat conduction is examined for one-dimensional polymer chains placed in between quantum augmented baths.

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

Lithiation bridged molecular conducting magnets

Combining ferromagnetism and metallic conductivity has been extensively studied in contemporary materials science. However, building such multifunctionalities in molecular chemistry is particularly challenging, on which two collective properties are long considered as mutually exclusive. Here, we report molecular conducting magnet consisting of two-dimensional graphene and molecular planar magnetic networks bridged by electrochemical lithiation, exhibiting the co-existence of distinct magnetism and conductivity and magneto-electric coupling effects. Here, the room temperature magnetoresistance and magnetoelectric coupling coefficient of such complex solid reach 10.7% and 1.77 V/Oe cm, respectively, which can be further tailored by modulating the interaction between graphene and molecular magnetic network. In addition, the graphene passivation simultaneously introduces environmental stability of molecular mag-nets. The electrochemical lithiation offers a pathway towards coupling magnetism and conductivity in molecular complexes.

2D organic complexes↗
DOE OSTI2024

A MPET 2 -mPBPK model for subcutaneous injection of biotherapeutics with different molecular weights: From local scale to whole-body scale

Subcutaneous injection of biotherapeutics has attracted considerable attention in the pharmaceutical industry. However, there is limited understanding of the mechanisms underlying the absorption of drugs with different molecular weights and the delivery of drugs from the injection site to the targeted tissue. Here, we propose the MPET 2 -mPBPK model to address this issue. This multiscale model couples the MPET 2 model, which describes subcutaneous injection at the local tissue scale from a biomechanical view, with a post-injection absorption model at injection site and a minimal physiologically-based pharmacokinetic (mPBPK) model at whole-body scale. Utilizing the principles of tissue biomechanics and fluid dynamics, the local MPET 2 model provides solutions that account for tissue deformation and drug absorption in local blood vessels and initial lymphatic vessels during injection. Additionally, we introduce a model accounting for the molecular weight effect on the absorption by blood vessels, and a nonlinear model accounting for the absorption in lymphatic vessels. The post-injection model predicts drug absorption in local blood vessels and initial lymphatic vessels, which are integrated into the whole-body mPBPK model to describe the pharmacokinetic behaviors of the absorbed drug in the circulatory and lymphatic system. We establish a numerical model which links the biomechanical process of subcutaneous injection at local tissue scale and the pharmacokinetic behaviors of injected biotherapeutics at whole-body scale. With the help of the model, we propose an explicit relationship between the reflection coefficient and the molecular weight and predict the bioavalibility of biotherapeutics with varying molecular weights via subcutaneous injection. The considered drug absorption mechanisms enable us to study the differences in local drug absorption and whole-body drug distribution with varying molecular weights. This model enhances the understanding of drug absorption mechanisms and transport routes in the circulatory system for drugs of different molecular weights, and holds the potential to facilitate the application of computational modeling to drug formulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗
DOE OSTI2021

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

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

Imaging Reconfigurable Molecular Concentration on a Graphene Field-Effect Transistor

The spatial arrangement of adsorbates deposited onto a clean surface under vacuum typically cannot be reversibly tuned. Here we use scanning tunneling microscopy to demonstrate that molecules deposited onto graphene field-effect transistors (FETs) exhibit reversible, electrically tunable surface concentration. Continuous gate-tunable control over the surface concentration of charged F 4 TCNQ molecules was achieved on a graphene FET at T = 4.5K. This capability enables the precisely controlled impurity doping of graphene devices and also provides a new method for determining molecular energy level alignment based on the gate-dependence of molecular concentration. Gate-tunable molecular concentration is explained by a dynamical molecular rearrangement process that reduces total electronic energy by maintaining Fermi level pinning in the device substrate. Finally, the molecular surface concentration is fully determined by the device back-gate voltage, its geometric capacitance, and the energy difference between the graphene Dirac point and the molecular LUMO level.

77 NANOSCIENCE AND NANOTECHNOLOGY↗