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Results for “Inverse”
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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Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy
Here, the design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure–property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange.
Charge Inversion and Calcium Gating in Mixtures of Ions in Nanopores
Not Available
High Spatial Melt Rate Variability Near the Totten Glacier Grounding Zone Explained by New Bathymetry Inversion
Totten Glacier is a fast-moving East Antarctic outlet with the potential for significant future sea-level contributions. We deployed four autonomous phase-sensitive radars on its ice shelf to monitor ice-ocean interactions near its grounding zone and made active source seismic observations to constrain gravity-derived bathymetry models. We observe an asymmetry in basal melting with mean melt rates along the grounding zone differing by up to 20 m/a. Our new bathymetry model reveals that this melt rate asymmetry coincides with an asymmetry in water column thickness and that the low-melting ice-shelf portion is shielded from the main cavity circulation. A 2-year record yields year-to-year melt rate variability of 7–9 m/a with no seasonal cycle. Our results highlight the key role of bathymetry near grounding lines for accurate modeling of ice-shelf melt, and the importance of sustained multi-year monitoring, especially at ice-shelf cavities where the dominant melt rate drivers vary primarily inter-annually.
Inverse Mapping of the Collision Kernel and Wall Flux Scaling in a Tall Convection‐Cloud Chamber Using Local Sensors and Knowledge‐Informed Deep Learning
Droplet collision–coalescence is a crucial process in cloud physics, but accurately representing this process under different dynamical conditions remains challenging. A proposed future convective‐cloud chamber aims to investigate this key process, but the method for observing it remains unclear, even though it is theoretically established that collision‐coalescence will occur. This study serves as a proof‐of‐concept demonstration of how knowledge‐informed deep learning, combined with measurement data from local sensors in the chamber, can be used to estimate the collision kernels, which determine how the droplet size distribution evolves during collision‐coalescence. In addition to estimating the collision kernel, we also address wall fluxes, another uncertain but important process that acts as a source of heat and moisture in the chamber. Ensemble runs of large‐eddy simulations are conducted by scaling the wall fluxes and the collision kernel, while the measured flow and cloud properties are used as inputs for a neural network. Results indicate that this approach successfully maps the scaling of wall fluxes and the collision kernel with biases of approximately 1% or less relative to the range of the target data. This proof‐of‐concept lays the groundwork for future applications; when the real measurements are available, real sensor data combined with the trained model presented in this work will enable estimation of the actual wall fluxes and collision kernel.
Reply to: “Extracting Kondo temperature of strongly-correlated systems from the inverse local magnetic susceptibility”
In his comment1, Katanin reanalyzes our LDA + DMFT results for the temperature-dependent static local spin susceptibility of Sr 2 RuO 4 and V 2 O 3 fitting them to a Curie–Weiss (CW) form, χ(T) ≃ a/(T + θ). Invoking Wilson’s analysis of the impurity susceptibility of the spin-½ one-channel Kondo model (1CKM) in the wide-band limit, he extracts spin Kondo temperatures using T K = θ/√2, obtaining T K = 350 K and 100 K for Sr 2 RuO 4 and V 2 O 3 , respectively. Noting that these are significantly smaller than the scales $T$$^{onset}_{sp}$ = 2300 K and 1000 K reported in ref. 2, he argues that our $T$$^{onset}_{sp}$ scales “do not characterize the screening process”.
Testing the evolutionary theory of inversion polymorphisms in the yellow monkeyflower (Mimulus guttatus)
Not provided.
Non-volatile electric-field control of inversion symmetry
Competition between ground states at phase boundaries can lead to significant changes in properties under stimuli, particularly when these ground states have different crystal symmetries. A key challenge is to stabilize and control the coexistence of symmetry-distinct phases. Using BiFeO 3 layers confined between layers of dielectric TbScO 3 as a model system, we stabilize the mixed-phase coexistence of centrosymmetric and non-centrosymmetric BiFeO 3 phases at room temperature with antipolar, insulating and polar semiconducting behaviour, respectively. Application of orthogonal in-plane electric (polar) fields results in reversible non-volatile interconversion between the two phases, hence removing and introducing centrosymmetry. Counterintuitively, we find that an electric field 'erases' polarization, resulting from the anisotropy in octahedral tilts introduced by the interweaving TbScO 3 layers. Consequently, this interconversion between centrosymmetric and non-centrosymmetric phases generates changes in the non-linear optical response of over three orders of magnitude, resistivity of over five orders of magnitude and control of microscopic polar order. Here our work establishes a platform for cross-functional devices that take advantage of changes in optical, electrical and ferroic responses, and demonstrates octahedral tilts as an important order parameter in materials interface design.
Unperturbed inverse kinematics nucleon knockout measurements with a carbon beam
Not provided.
A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs
Model-based optimization for real-time forecasting in unconventional reser-voirs requires novel methods and work?ows since the strategies and work?ows used in conventional reservoirs are either inapplicable, or prohibitively expen-sive and time-consuming. Insu?cient site data and computational expense of high-?delity simulations mean that work?ows with high-?delity simulations are not ideal for usage in comprehensive uncertainty quanti?cation stud-ies that require 1000s of forward model runs. We present an alternative, novel work?ow for unconventional reservoirs, based on the interplay between reduced-order models and machine-learning. Our physics-informed machine-learning (PIML) work?ow addresses the challenges to real-time reservoir management in uncoventionals, namely lack of data (the time-frame for which the wells have been producing), and computational expense of high-?delity modeling. We use the machine-learning paradigm of transfer-learning to bind together fast but less accurate reduced-order models with slow, but accurate high-?delity models and circumvent the di?culties inherent in the current state-of-the-art for unconventionals. Such a PIML work?ow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. The signi?cance of our approach is that while it is developed for a particu-lar well and site in the Marcelus Shale gas reservoir of the Appalachian basin (MSEEL), it is not wedded to it. We expect the same work?ow can be ap-plied to other shale formations (e.g., Woodford, Barnett, Utica, EagleFord) should site-data become available, using the same set of machine-learning techniques from transfer learning. Some ?ne-tuning (or minimal retraining of the neural networks) will be required to transfer knowledge across shale gas sites/formations but it is a clearly superior alternative to developing a new machine-learning model altogether when considering a di?erent site.
Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.
A machine learning inversion scheme for determining interaction from scattering
Small angle scattering techniques have now been routinely used to quantitatively determine the potential of mean force in colloidal suspensions. However the numerical accuracy of data interpretation is often compounded by the approximations adopted by liquid state analytical theories. To circumvent this long standing issue, here we outline a machine learning strategy for determining the effective interaction in the condensed phases of matter using scattering. Via a case study of colloidal suspensions, we show that the effective potential can be probabilistically inferred from the scattering spectra without any restriction imposed by model assumptions. Comparisons to existing parametric approaches demonstrate the superior performance of this method in accuracy, efficiency, and applicability. This method can effectively enable quantification of interaction in highly correlated systems using scattering and diffraction experiments.
Shape-anisotropy inverses the behavior of emergent vortices in active chiral fluids
Active colloidal fluids exhibit spontaneous emergence of correlated states, characterized by complex collective dynamics and self-organization. In geometrically confined systems, activity modulations trigger robust polar state reversals of a macroscopic vortex formed by colloidal rollers. Here, we reveal that the shape anisotropy of dielectric rollers transforms the system into a chiral active fluid comprised of rollers of arbitrary handedness. The chiral rollers self-assemble into multiple freestanding vortices with a spontaneously selected sense of rotation. We demonstrate that upon reactivation of the system after a complete cessation of activity beyond all relevant timescales, the vortices simultaneously restore their previous chiral states in striking contrast to the chiral state reversals demonstrated by spherical rollers. The analysis reveals that shape-anisotropy modifies the collective state memory of the ensemble. The findings provide insights into the emergence of complex collective behavior in chiral colloidal fluids governed by an interplay between shape anisotropy, chiral motion, and activity modulations.
Inverse design of nanoporous crystalline reticular materials with deep generative models
Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks in different topologies, with many having desirable properties for gas storage, separation, catalysis, biomedical applications and so on. The notable variety of building blocks makes reticular chemistry both promising and challenging for prospective materials design. Here we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder for the generative design of reticular materials. We demonstrate the automated design process with a class of metal–organic framework (MOF) structures and the goal of separating carbon dioxide from natural gas or flue gas. Our model shows high fidelity in capturing MOF structural features. Here, we show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported.