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Results for “Vision Disorders”

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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10 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials

Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. In this work, we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unveiling the Hidden Evolution of Crystal Defects and Disorder in Energy Materials

Control of point defects and disorder in functional thin films and 2D materials is critical to realizing their full potential in applications ranging from energy storage to advanced electronics. However, these phenomena are often poorly understood, difficult to characterize, and challenging to direct with precision. This presentation explores emerging multi-modal computer vision to decipher and predict order in materials across multiple length scales in the electron microscope, from the atomic to the nanoscale. By fusing data from diverse sources, these powerful models provide unprecedented insights into materials' lifecycles, enabling the control of defects and their associated properties at a fundamental level. This capability promises to transform materials design and accelerate the development of next-generation technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Thermal transport in defective and disordered materials

With significant recent advancements in thermal sciences—such as the development of new theoretical and experimental techniques, and the discovery of new transport mechanisms—it is helpful to revisit the fundamentals of vibrational heat conduction to formulate an updated and informed physical understanding. The increasing maturity of simulation and modeling methods sparks the desire to leverage these techniques to rapidly improve and develop technology through digital engineering and multi-scale, electro-thermal models. With that vision in mind, this review attempts to build a holistic understanding of thermal transport by focusing on the often unaddressed relationships between subfields, which can be critical for multi-scale modeling approaches. For example, we outline the relationship between mode-specific (computational) and spectral (analytical) models. We relate thermal boundary resistance models based on perturbation approaches and classic transmissivity based models. We discuss the relationship between lattice dynamics and molecular dynamics approaches along with two-channel transport frameworks that have emerged recently and that connect crystal-like and amorphous-like heat conduction. Throughout, we discuss best practices for modeling experimental data and outline how these models can guide material-level and system-level design.

42 ENGINEERING↗
DOE OSTI2022

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Most human body proteins' activity and functionality are related to configurational changes of entire subdomains within the protein crystal structure. The crystal structures build the basis for any calculation that describes the structure or dynamics of a protein, most of the time with strong geometrical restrictions. However, these restrictions from the crystal structure are not present in the solution. The structure of the proteins in the solution may differ from the crystal due to rearrangements of loops or subdomains on the pico to nanosecond time scale (i.e., the internal protein dynamics time regime). This study describes how slow motions on timescales of several tens of nanoseconds can be accessed using neutron scattering. In particular, the dynamical characterization of two major human proteins, an intrinsically disordered protein that lacks a well-defined secondary structure and a classical antibody protein, is addressed by neutron spin echo spectroscopy (NSE) combined with a wide range of laboratory characterization methods. Further insights into protein domain dynamics were achieved using mathematical modeling to describe the experimental neutron data and determine the crossover between combined diffusive and internal protein motions. The extraction of the internal dynamic contribution to the intermediate scattering function obtained from NSE, including the timescale of the various movements, allows further vision into the mechanical properties of single proteins and the softness of proteins in their nearly natural environment in the crowded protein solution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Effects on the Ocular Surface from Reading on Different Smartphone Screens: A Prospective Randomized Controlled Study

The purpose of this study was to investigate the influence of smartphone reading on the ocular surface and to compare the various effects of different screens and light conditions on the ocular surface. One hundred nineteen volunteers were randomly divided into: light + organic light‐emitting diode (OLED), light + electronic ink (eINK), dark + OLED, and dark + eINK. Ocular surface examinations, including noninvasive break‐up time (NIBUT), noninvasive keratograph tear meniscus height (NIKTMH), ocular redness, fluorescein break‐up time (FBUT), corneal fluorescein staining, meibomian gland assessment, Schirmer I Test, and blinking frequency, were performed before and after a reading task. Symptoms were evaluated using the Ocular Surface Disease Index (OSDI) and Computer Vision Syndrome Questionnaire (CVS‐Q). NIBUT and FBUT were decreased statistically significantly after participants read on an OLED screen for 2 hours compared with the baseline in light and dark environments, whereas no statistically significant decrease was observed on an eINK screen. NIKTMH was statistically significantly decreased after reading on an OLED screen in light and dark settings, and the eINK screen had a lesser effect on NIKTMH. An obvious increase in the ocular redness, OSDI and CVS‐Q scores was observed after reading on an OLED screen, whereas the eINK screen had a lesser effect on these indicators. Blink rate increased gradually in OLED subgroups during the reading task, whereas no statistically significant difference was observed in the eINK subgroups. Our research suggested that reading on an OLED screen can cause ocular surface disorder and obvious subjective discomfort, whereas reading on an eINK screen can minimize ocular surface disorder in both dark and light environments.

Yuan, Kelan↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗
DOE OSTI2019

Isovalent Alloying and Heterovalent Substititution as Routes to Accelerate the Development and Optimization of Super-Efficient Halide Perovskite PV Solar Cells

Halide perovskites represent one of the fastest growing photovoltaic (PV) device technologies in the last decade, with research efficiencies among the highest confirmed. However, the research needed to advance tandem perovskite solar cells requires a specialized understanding base, but there is no such base for this young group of materials with the same depth as in semiconductor alloy research. Whereas IV-IV, II-V and II-VI semiconductors have a sophisticated science foundation refined over decades due of their use in electronics, lasers, LEDs and night vision applications, this is not yet the case for halide perovskites. At the same time, these materials behave rather differently than conventional semiconductors. Within the rapidly unfolding field of PV, the emerging halide-perovskite alloy research has largely been based on empiricism. Clearly, a concerted and comprehensive application to halide perovskite alloys of the modern quantum theory of solids brought us over the years a broad understanding of the critical factors enabling conventional inorganic PV could leapfrog the Edisonian, trial-and-error search for solutions to critical problems that are poorly understood. These theory approaches are required to accelerate the progress toward commercially ready hybrid-perovskites by providing a theory-guided experiment component to the EERE investments in this high-priority R&D. During this project we have studied three different rungs on the ladder of knowledge about hybrid perovskites. This kind of single halide perovskites represent the first level of development (rung 1) of solar cells, and includes materials such as APbI 3 , ASnI 3 , APbBr 3 , ASnBr 3 with different A cations, such as methylammonium (MA) with formamidinium (FM). Rung 2 is obtained by mixing B cations (Sn and Pb), and X anions (Cl and Br or I) producing a disordered alloy (A, A’)(M,M’)(X,X’) 3 -space. The most advanced rung corresponds to heterovalent-substitution of halide perovskites, creating an ordered double-perovskite compound. Although the focus of the project was on Rungs 2 and 3, we have made important discoveries regarding the nature of pure perovskites. We’ve observed that cubic perovskites should be modeled by a polymorphous network, instead of the basic single unit cell approach. This leads to the conclusion that perovskites have a fluid-like nature. An article regarding this work has been submitted to Nature Materials. Using the approach described above, we have also simulated alloys of halide perovskites, representing rung 2. We have focused on mixtures of [Cs,FA][Sn,Pb]I 3 with different concentrations, being able to disentangle the structural and electronic factors that determines the variation (bowing) of properties such as band gaps, mixing enthalpies and decomposition energies. For rung 3 compounds, we’ve closely interacted with experimental groups clarifying the chemical routes to obtain a certain compound, and what should be the most stable structure for them. As the final product, and consequent dissemination of the research, we’ve submitted two papers for publication, that are currently under evaluation. The interaction with experimental groups was also an important focus. We have participated in several local, national and international meetings, and reached out more closely to groups from CU Boulder and Arkansas.

14 SOLAR ENERGY↗
DOE OSTI2025

Mesoporous Thin Film Architectures: Addressing Material Demands through Molecular Self-Assembly

Mesoporous thin films spark interest across a wide range of disciplines due to their tunable nanostructures, large internal surface areas, and strong compatibility with planar optical, electronic, and microfluidic devices. While attention in the porous materials community has shifted toward macroporous or disordered nanoporous systems, a resurgence in mesoporous thin film research is underway, driven by new molecular self-assembly methods, advanced materials chemistry, and improved characterization techniques. The integration of high-χN block copolymer design, kinetically persistent micelle templating, and postdeposition processing protocols now allows control over structural parameters such as pore size, wall thickness, porosity, and connectivity. These advances have overcome many of the thermodynamic and processing constraints that previously limited widespread adoption. Rather than serving only as high-surface-area supports, mesoporous thin films are engineered as active interfaces where responsive chemistries and nanoscale confinement act in tandem. Embedding switchable ligands, thermoresponsive polymers, redox mediators, or ion-selective groups directly within the pore walls enables real-time control over transport, optical, and electrochemical properties. These capabilities open up new directions in adaptive coatings, gated membranes, and fast-response biosensors. To further expand their functional scope, mesoporous films are integrated into hierarchical and multicomponent architectures. Techniques such as triblock terpolymer templating, crack-directed assembly, and nanoimprint lithography allow for control over spatial organization on the micron and submicron scale and pore system orientation. This enables programmable anisotropy, enhanced molecular diffusion, and wavelength-selective photonic behavior, essential for next-generation sensing, catalysis, and energy applications. Such structural and functional complexity requires equally sophisticated characterization. Multimodal and in situ techniques can track material dynamics under operational conditions. Recent progress includes extended-range ellipsometric porosimetry (EP) for hierarchical architectures, vacuum EP for interface energetics, time-resolved EP for diffusion kinetics, and correlative AFM-SAXS mapping. The introduction of advanced neutron-based spectroscopies, particularly quasielastic neutron scattering (QENS), promises to provide real-time access to ion transport dynamics and segmental motion under nanoscale confinement, offering a path toward deeper mechanistic understanding of structure-performance correlations in mesoporous systems. This Account reflects the technical advances made and the interdisciplinary collaborations that have shaped our collective vision. The particular dimensions of mesopores enable us to subtly tune interactions at the molecular, interfacial, and mesoscopic levels that permit us to harness nanoconfinement. What emerges is a versatile, modular platform capable of chemical gating, energy transduction, and sensing with a level of tunability unmatched by other porous materials. We highlight critical challenges including the need for more robust large-area processing, a deeper understanding of dynamic behavior under cycling, and better integration with device-level architectures. Our strategies support the transition of mesoporous thin films into active high-performance components in next-generation energy, environmental, and biomedical systems.

oxides↗