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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Defect engineering of porous aromatic frameworks via end capping improves dioxane removal from water

Amorphous porous organic polymers show promise for energy-efficient adsorptive separations, but it is difficult to understand or improve their performance through intentional structural modification. Herein, we report the synthesis of porous aromatic frameworks (PAFs) with pore structures tailored by the incorporation of a monofunctionalized end-capping monomer that disrupts framework topology. Combining experimental characterization with molecular simulations, we show that this defect engineering strategy yields less densely crosslinked networks, which leads to pore collapse and the presence of unique adsorption sites. These defect-engineered PAFs exhibit enhanced removal of 1,4-dioxane, an important environmental pollutant, from water. Increasing the concentration of end-capping monomers produces PAFs with narrower pore size distributions and improved 1,4-dioxane uptake. These results illustrate that defect engineering can effectively modulate polymer connectivity and porosity for applications in selective adsorptive separations. In conclusion, this technique avoids post-synthetic treatments and presents another approach to tailor amorphous polymeric adsorbents.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solvent oligomerization pathways facilitated by electrolyte additives during solid-electrolyte interphase formation

The solid–electrolyte interphase (SEI) layer formation is known to play an important role in determining the lifetime of lithium-ion batteries. A thin, stable SEI layer is linked to overall improved battery performance and longevity, however, the factors and mechanisms that lead to optimal SEI morphology and composition are not well understood. Inclusion of electrolyte additives (fluoroethylene carbonate, FEC; and vinylene carbonate, VC) is often necessary for improving SEI characteristics. To understand how these electrolyte additives impact SEI formation, we employed molecular dynamics (MD) and density functional theory (DFT) simulations to study the reaction networks and oligomerization pathways, respectively, for three systems containing ethylene carbonate (EC), a lithium ion, and FEC or VC. MD simulations suggest radical oligomerization pathways analogous to traditional oligomerization with nucleophilic alkoxide species via SN1 reaction mechanisms. Both S N 1 and S N 2 mechanisms were studied for all three systems using DFT. Oligomerization reactions were studied with both a standard alkoxide species and a ring-opened EC radical as the nucleophiles and EC, FEC, and VC as the electrophiles. For all cases, FEC and VC exhibited lower free energy barriers and more stable adducts when compared with EC. We conclude that one of the role of additives is to modify the oligomerization process of EC by introducing branching points (FEC) or termination points (VC).

25 ENERGY STORAGE↗
DOE OSTI2024

Self–Healing and Stretchable Molecular Ferroelectrics with High Expandability

The interplay between crystal ordering and stretchability is frequently encountered in contemporary materials science, particularly in the case of ferroelectrics. The inherent dilemma arises when these materials need to withstand repetitive mechanical deformations or stretching without sacrificing their crystal integrity, all while retaining their remarkable ferroelectric properties and even exhibiting self-healing capabilities. This complexity further presents a significant challenge in the design and engineering of mechanically rigid molecular ferroelectric crystals, particularly for applications where both precise crystalline structure and mechanical adaptability are crucial. In this study, the humidity-controlled expansion and contraction, dissolution, and recrystallization of a self-assembled molecular ferroelectric-in-hydrogel framework are reported. Self-healing ferroelectric-in-hydrogel networks exhibit a recyclable humidity-tailored ionic conductivity from 2.86 × 10 –6 to 1.36 × 10 –5 S cm –1 , facilitating the stretchable piezoelectric sensing. Additionally, the dynamic bond reforming interactions are observed, leading to the tailoring of Young's modulus from 452 to 170 MPa, maintaining ferroelectricity under a strain of 20% with a piezoelectric coefficient of 15.7 pC N –1 . Upon lattice contraction, the molecular contacts undergo reforming, leading to the restoration of stretchable ferroelectrics/piezoelectrics and paving the way for stretchable bioelectronics for full-body motion monitoring. The capabilities highlighted here open avenues for stretchable and self-healing ferroelectric-in-hydrogel bioelectronic technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep learning of accurate force field of ferroelectric HfO 2

The discovery of ferroelectricity in HfO 2 -based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density nonvolatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at finite temperatures. Molecular dynamics is an ideal technique for investigating dynamical processes on large length and time scales, though its applications to new materials are often hindered by the limited availability and accuracy of classical force fields. Here we present a deep neural network–based interatomic force field of HfO 2 learned from ab initio data using a concurrent learning procedure. The model potential is able to predict structural properties such as elastic constants, equation of states, phonon dispersion relationships, and phase transition barriers of various hafnia polymorphs with accuracy comparable with density functional theory calculations. Here, the validity of this model potential is further confirmed by the reproduction of experimental sequences of temperature-driven ferroelectric-paraelectric phase transitions of HfO 2 with isobaric-isothermal ensemble molecular dynamics simulations. We suggest a general approach to extend the model potential of HfO 2 to related material systems including dopants and defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Deciphering the non-linear impact of Al on chemical durability of silicate glass

The role of Al in aluminosilicate glasses remains somewhat a mystery: at low concentrations, it increases the resistance to hydrolysis of the glass, whereas at high concentrations an opposite effect is observed. To understand the origin of the phenomenon on a fundamental atomistic scale, we performed 577 MD simulations and applied potential mean force (PMF) calculations to estimate the activation barriers for hydrolysis and to statistically correlate them with local structural features of the glass. Models of pure silicate and aluminosilicate glasses are constructed and investigated. PMF simulation results are further validated by the experimental measurements and revealed that Al is very easy to dissociate, but it also increases the glass chemical durability through significantly increasing both the strength of Si and network connectivity of the glass. In contrast, at high Al concentration, preferential dissolution of Al weakens the silicate network, which it supposes to strengthen, and so the glass resistance becomes poor. Through PMF calculations, we evaluated the activation barriers for dissociating bonds around Al as 0.49 eV, which is less than a half of the energy to dissociate bonds around Si in pure silicate (1.22 eV) and around Si in aluminosilicate glass (1.34 eV), all these energy differences being statistically significant. Molecular structural level investigation revealed that Si with Al as a second neighbor in the glass network has a significantly higher activation energy for dissociation than Si in pure silicate glass. Furthermore, the proposed approach opens the way to the development of quantitative predictive models of glass durability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Highly functional microspheres facilitating Diels–Alder network formation

Introducing particles to dynamic covalent networks is a common approach to improve their performance. However, network formation can be impacted by their size and functionality. The influence can be predicted by common theories for small molecular precursors, but it is unclear whether they are applicable to precursors bearing numerous reactive groups and micrometer-scale dimensions. In this work, an experimental study was undertaken using dynamic covalent networks formed by the Diels–Alder reaction between furan and maleimide groups. The gelation behavior of the Diels–Alder networks was studied using rheometry to track their network formation at 40 °C with varying maleimide-functionalized microsphere loading. The highly functional microspheres can interact with the furan precursor, aiding in the formation of the Diels–Alder networks. A 5 wt% microsphere sample can reduce the gelation time by 23% and facilitate network formation in an unbalanced stoichiometry near the critical composition to form a percolating network.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Anisotropic Dynamics and Mechanics of Macromolecular Crystals Containing Lattice-Patterned Polymer Networks

The mechanical and functional properties of many crystalline materials depend on cooperative changes in lattice arrangements in response to external perturbations. However, the flexibility and adaptiveness of crystalline materials are limited. Additionally, the bottom-up, molecular-level design of crystals with desired dynamic and mechanical properties at the macroscopic level remains a considerable challenge. To address these challenges, we had previously integrated mesoporous, cubic ferritin crystals with hydrogel networks, resulting in hybrid materials (polymer integrated crystals or PIX) which could undergo dramatic structural changes while maintaining crystalline periodicity and display efficient self-healing. Here, the dynamics and mechanics of these ferritin-PIX were devoid of directionality, which is an important attribute of many molecular and macroscopic materials/devices. In this study, we report that such directionality can be achieved through the use of ferritin crystals with anisotropic symmetries (rhombohedral or trigonal), which enable the templated formation of patterned hydrogel networks in crystallo. The resulting PIX expand and contract anisotropically without losing crystallinity, undergo prompt bending motions in response to stimuli, and self-heal efficiently, capturing some of the essential features of sophisticated biological devices like skeletal muscles.

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

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely 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 a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, 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 (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Breaking the curse of dimensionality: Solving configurational integrals for crystalline solids by tensor networks

Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a tensor network approach that reformulates the high-dimensional configurational integral for identical-particle crystals into a sequence of computationally efficient summations. We represent the integrand as a high-dimensional tensor and apply tensor-train (TT) decomposition together with a custom TT-cross interpolation. This approach circumvents the need to explicitly construct the full tensor. We introduce tailored rank-1 and rank-2 schemes optimized for sharply peaked Boltzmann probability densities, typical for identical-particle crystals. When applied to the calculation of internal energy and pressure-temperature curves for crystalline Cu and Ar at high (GPa) pressures, as well as the alpha-to-beta phase transition diagram of Sn, our method accurately reproduces molecular dynamics simulation results using tight-binding, machine learning, hierarchical interacting particle–neural network, and modified embedded atom method potentials,all within seconds of computation time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Role of Intact Hydrogen-Bond Networks in Multiproton-Coupled Electron Transfer

The essential role of a well-defined hydrogen-bond network in achieving chemically reversible multiproton translocations triggered by one-electron electrochemical oxidation/reduction is investigated by using pyridylbenzimidazole–phenol models. The two molecular architectures designed for these studies differ with respect to the position of the N atom on the pyridyl ring. In one of the structures, a hydrogen-bond network extends uninterrupted across the molecule from the phenol to the pyridyl group. Experimental and theoretical evidence indicates that an overall chemically reversible two-protoncoupled electron-transfer process (E2PT) takes place upon electrochemical oxidation of the phenol. This E2PT process yields the pyridinium cation and is observed regardless of the cyclic voltammogram scan rate. In contrast, when the hydrogen-bond network is disrupted, as seen in the isomer, at high scan rates (~1000 mV s –1 ) a chemically reversible process is observed with an E 1/2 characteristic of a one-proton-coupled electron-transfer process (E1PT). At slow cyclic voltammetric scan rates (<1000 mV s –1 ) oxidation of the phenol results in an overall chemically irreversible two-proton-coupled electron-transfer process in which the second proton-transfer step yields the pyridinium cation detected by infrared spectroelectrochemistry. In this case, we postulate an initial intramolecular proton-coupled electron-transfer step yielding the E1PT product followed by a slow, likely intermolecular chemical step involving a second proton transfer to give the E2PT product. Insights into the electrochemical behavior of these systems are provided by theoretical calculations of the electrostatic potentials and electric fields at the site of the transferring protons for the forward and reverse processes. Furthermore, this work addresses a fundamental design principle for constructing molecular wires where protons are translocated over varied distances by a Grotthuss-type mechanism.

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

Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity

Abstract Plants demonstrate a broad range of responses to environmental shifts. One of the most remarkable responses is plasticity, which is the ability of a single plant genotype to produce different phenotypes in response to environmental stimuli. As with all traits, the ability of plasticity to evolve depends on the presence of underlying genetic diversity within a population. A common approach for evaluating the role of genetic variation in driving differences in plasticity has been to study genotype-by-environment interactions (G × E). G × E occurs when genotypes produce different phenotypic trait values in response to different environments. In this review, we highlight progress and promising methods for identifying the key environmental and genetic drivers of G × E. Specifically, methodological advances in using algorithmic and multivariate approaches to understand key environmental drivers combined with new genomic innovations can greatly increase our understanding about molecular responses to environmental stimuli. These developing approaches can be applied to proliferating common garden networks that capture broad natural environmental gradients to unravel the underlying mechanisms of G × E. An increased understanding of G × E can be used to enhance the resilience and productivity of agronomic systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Asymmetric Composition of Ionic Aggregates and the Origin of High Correlated Transference Number in Water-in-Salt Electrolytes

"Water-in-salt" electrolytes open up exciting new avenues for expanding the electrochemical window of aqueous electrolytes. We investigated the solvation structure and dynamics of highly concentrated lithium bis(trifluoromethane)sulfonimide aqueous electrolyte using experimentally corroborated molecular dynamics simulations. The simulations revealed that the heterogeneous structure of the electrolyte comprises percolating networks of ion and water domains/aggregates. Interestingly, the ionic regions are composed of more TFSI - ions than Li + ions. The Li + -ion transport mechanism was further explored. Li + ions can hop along the coordinated TFSI- ions in the ionic aggregates. The calculated correlated transference number of the 20 m electrolyte is similar to 0.32, which is reasonably high for the high concentration due to a weak negative correlation between the motion of cations and anions within the heterogeneous microscopic domains. These molecular dynamics results connect the heterogeneous structure of the electrolyte to the correlated dynamics of the Li + ion and provide a new understanding of the Li + -ion transport mechanism in this novel electrolyte.

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

Efficient sampling of free energy landscapes with functions in Sobolev spaces

Molecular simulations of biological and physical phenomena generally involve sampling complicated, rough energy landscapes characterized by multiple local minima. In this work, we introduce a new family of methods for advanced sampling that draw inspiration from functional representations used in machine learning and approximation theory. As shown here, such representations are particularly well suited for learning free energies using artificial neural networks. As a system evolves through phase space, the proposed methods gradually build a model for the free energy as a function of one or more collective variables, from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. Implementation of the methods is relatively simple and, more importantly, for the representative examples considered in this work, they provide computational efficiency gains of up to several orders of magnitude over other widely used simulation techniques.

Approximation theory↗
DOE OSTI2023

Riverine organic matter functional diversity increases with catchment size

A large amount of dissolved organic matter (DOM) is transported to the ocean from terrestrial inputs each year (~0.95 Pg C per year) and undergoes a series of abiotic and biotic reactions, causing a significant release of CO 2 . Combined, these reactions result in variable DOM characteristics (e.g., nominal oxidation state of carbon, double-bond equivalents, chemodiversity) which have demonstrated impacts on biogeochemistry and ecosystem function. Despite this importance, however, comparatively few studies focus on the drivers for DOM chemodiversity along a riverine continuum. Here, we characterized DOM within samples collected from a stream network in the Yakima River Basin using ultrahigh-resolution mass spectrometry (i.e., FTICR-MS). To link DOM chemistry to potential function, we identified putative biochemical transformations within each sample. We also used various molecular characteristics (e.g., thermodynamic favorability, degradability) to calculate a series of functional diversity metrics. We observed that the diversity of biochemical transformations increased with increasing upstream catchment area and landcover. This increase was also connected to expanding functional diversity of the molecular formula. This pattern suggests that as molecular formulas become more diverse in thermodynamics or degradability, there is increased opportunity for biochemical transformations, potentially creating a self-reinforcing cycle where transformations in turn increase diversity and diversity increase transformations. We also observed that these patterns are, in part, connected to landcover whereby the occurrence of many landcover types (e.g., agriculture, urban, forest, shrub) could expand DOM functional diversity. For example, we observed that a novel functional diversity metric measuring similarity to common freshwater molecular formulas (i.e., carboxyl-rich alicyclic molecules) was significantly related to urban coverage. These results show that DOM diversity does not decrease along stream networks, as predicted by a common conceptual model known as the River Continuum Concept, but rather are influenced by the thermodynamic and degradation potential of molecular formula within the DOM, as well as landcover patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS

Abstract Li 10 Ge(PS 6 ) 2 (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for concerted ion hopping, a mechanism common among superionic conductors such as Li 7 La 3 Zr 2 O 12 (LLZO) and Li 1.3 Al 0.3 Ti 1.7 (PO 4 ) 3 (LATP). While first principles simulations using molecular dynamics (MD) provide insight into the Li + transport mechanism, historically, there has been a gap in the temperature ranges studied in simulations and experiments. Here, we used a neural network potential trained on density functional theory (DFT) simulations, to run up to 40-nanosecond long MD simulations at DFT-like accuracy to characterize the ion conduction mechanisms across a range of temperatures that includes previous simulations and experimental studies. We have confirmed a Li + sublattice phase transition in LGPS around 400 K, below which the ab -plane diffusivity D a b ∗ is drastically reduced. Concomitant with the sublattice phase transition near 400 K, there is less cation-cation (cross) correlation, as characterized by Haven ratios closer to 1, and the vibrations in the system are more harmonic at lower temperature. Intuitively, at high temperature, the collection of vibrational modes may be sufficient to drive concerted ion hops. However, near room temperature, the vibrational modes available may be insufficient to overcome electrostatic repulsion, thus resulting in less correlated ion motion and comparatively slower ion conduction. Such phenomena of a sublattice phase transition, below which concerted hopping plays a less significant role, may be extended to other highly concentrated solid electrolytes such as LLZO and LATP.

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

PECAN2

PECAN2, Pose Classification with 3D Atomic Network 2, presents an innovative approach to pose classification in computational modeling, particularly in the context of molecular docking. Traditional methods like docking, which rely on physics-based calculations, are prone to inaccuracies in predicting binding poses during experimental testing. While machine learning (ML) approaches, such as ML-driven pose classification, have been introduced to address these issues, they often depend on the availability of crystal structure data. In contrast, PECAN2 introduces a novel pose classification method that operates independently of crystal structures. Instead, it utilizes a 3D atomic neural network with Point Cloud Network (PCN) to establish correlations between docking scores and experimental data. This approach marks a departure from previous studies that heavily relied on crystal structures for labeling. The key innovation lies in the ability of PECAN2 to enhance the performance of molecular docking by filtering out false positives and false negatives through the correlation between docking scores and experimental data.

Shim, Heesung↗
DOE OSTI2025

Molecular Interlocking Multidimensional Modulations of Cathode‐Electrolyte Interface for Constructing High Energy Density Quasi‐Solid‐State Batteries

Gel polymers are regarded as a promising candidate electrolyte for lithium-metal quasi-solid-state batteries, primarily due to their high ionic conductivity and solid-liquid synergistic properties. However, challenges such as interfacial side reactions, limitations in Li + transport caused by interfacial issues, and leaching of transition metals from the cathode have yet to be effectively solved. Herein, a novel gel electrolyte modulation strategy based on electrostatic filler assembly is proposed to address the issues of ineffective capacity utilization and inadequate cycling stability of high-energy-density cathode materials in solid-state lithium-ion batteries. It constructs a 3D interpenetrating charge-bridge network that effectively tackles the phase-separation challenge between fillers and electrolytes at the molecular level. Meanwhile, the molecular interlocking structure effectively inhibits the electrolyte erosion. More critically, it optimizes and stabilizes the cathode-electrolyte interface film, which facilitates the conduction of Li + -ions through a size-sieving mechanism. Consequently, this strategy enables effective adaptation across diverse high-energy-density cathode materials with satisfactory capacity performance (170.4 mAh g −1 at 4.5 V/1 C for LiNi 0.6 Co 0.2 Mn 0.2 O 2 and 194.0 mAh g −1 at 4.3 V/1 C for LiNi 0.9 Co 0.08 Mn 0.02 O 2 ). In conclusion, this investigation offers a straightforward and effective reference for addressing the critical challenges of ionic transport and interface stabilization in the design of gel electrolytes.

cathode-electrolyte interface↗
DOE OSTI2023

Gas-Phase Synthesis of Coronene through Stepwise Directed Ring Annulation

Molecular beam experiments together with electronic structure calculations provide the first evidence of a complex network of elementary gas-phase reactions culminating in the bottom-up preparation of the 24π aromatic coronene (C 24 H 12 ) molecule–a representative peri-fused polycyclic aromatic hydrocarbon (PAH) central to the complex chemistry of combustion systems and circumstellar envelopes of carbon stars. The gas-phase synthesis of coronene proceeds via aryl radical-mediated ring annulations through benzo[e]pyrene (C 20 H 12 ) and benzo[ghi]perylene (C 22 H 12 ) involving armchair-, zigzag-, and arm-zig-edged aromatic intermediates, highlighting the chemical diversity of molecular mass growth processes to polycyclic aromatic hydrocarbons. Furthermore, the isomer-selective identification of five- to six-ringed aromatics culminating with the detection of coronene is accomplished through photoionization and is based upon photoionization efficiency curves along with photoion mass-selected threshold photoelectron spectra, providing a versatile concept of molecular mass growth processes via aromatic and resonantly stabilized free radical intermediates to two-dimensional carbonaceous nanostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗