Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “CHESS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a “window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-extreme conditions at the Second Target Station

Three concepts for the application of multi-extreme conditions under in situ neutron scattering are described here. The first concept is a neutron diamond anvil cell made from a non-magnetic alloy. It is shrunk in size to fit existing magnets and future magnet designs and is designed for best pressure stability upon cooling. This will allow for maximum pressures above 10 GPa to be applied simultaneously with (steady-state) high magnetic field and (ultra-)low temperature. Additionally, an implementation of miniature coils for neutron diamond cells is presented for pulsed-field applications. The second concept presents a set-up for laser-heating a neutron diamond cell using a defocused CO 2 laser. Cell, anvil, and gasket stability will be achieved through stroboscopic measurements and maximum temperatures of 1500 K are anticipated at pressures to the megabar. The third concept presents a hybrid levitator to enable measurements of solids and liquids at temperatures in excess of 4000 K. This will be accomplished by a combination of bulk induction and surface laser heating and hyperbaric conditions to reduce evaporation rates. The potential for deployment of these multi-extreme environments within this first instrument suite of the Second Target Station is described with a special focus on VERDI, PIONEER, CENTAUR, and CHESS. Furthermore, considerations for deployment on future instruments, such as the one proposed as TITAN, are discussed. Overall, the development of these multi-extremes at the Second Target Station, but also beyond, will be highly advantageous for future experimentation and will give access to parameter space previously not possible for neutron scattering.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Quantum model learning agent: characterisation of quantum systems through machine learning

Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ≥ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Characterization of 128 × 128 MM-PAD-2.1 ASIC: a fast framing hard x-ray detector with high dynamic range

Here, we characterize a new x-ray Mixed-Mode Pixel Array Detector (MM-PAD-2.1) Application Specific Integrated Circuit (ASIC). Using an integrating pixel front-end with dynamic charge removal architecture, the MM-PAD-2.1 ASIC extends the maximum measurable x-ray signal (in 20 keV photon units) to > 10 7 x-rays/pixel/frame while maintaining a low read noise across the full dynamic range, all while imaging continuously at a frame rate of up to 10 kHz. The in-pixel dynamic charge removal mechanism prevents saturation of the input amplifier and proceeds in parallel with signal integration to achieve deadtime-less measurements with incident x-ray rates of > 10 10 x-rays/pixel/s. The ASIC format consists of 128 × 128 square pixels each 150 μm on a side and is designed to be 3-side buttable so large arrays can be effectively tiled. Here we use both laboratory x-ray sources and the Cornell High Energy Synchrotron Source (CHESS) to characterize two single ASIC prototype detectors for both low (single x-ray) and high incident flux detection. In the first detector the ASIC was solder bump-bonded to a 500 μm thick Si sensor for efficient detection of x-rays below 20 keV, whereas the second detector used a 750 μm thick CdTe sensor for x-rays above ~20 keV.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reinforcement learning in discrete action space applied to inverse defect design

Abstract Reinforcement learning (RL) algorithms that include Monte Carlo Tree Search (MCTS) have found tremendous success in computer games such as Go, Shiga and Chess. Such learning algorithms have demonstrated super-human capabilities in navigating through an exhaustive discrete action search space. Motivated by their success in computer games, we demonstrate that RL can be applied to inverse materials design problems. We deploy RL for a representative case of the optimal atomic scale inverse design of extended defects via rearrangement of chalcogen (e.g. S) vacancies in 2D transition metal dichalcogenides (e.g. MoS 2 ). These defect rearrangements and their dynamics are important from the perspective of tunable phase transition in 2D materials i.e. 2H (semi-conducting) to 1T (metallic) in MoS 2 . We demonstrate the ability of MCTS interfaced with a reactive molecular dynamics simulator to efficiently sample the defect phase space and perform inverse design—starting from randomly distributed S vacancies, the optimal defect rearrangement of defects corresponds a line defect of S vacancies. We compare MCTS performance with evolutionary optimization i.e. genetic algorithms and show that MCTS converges to a better optimal solution (lower objective) and in fewer evaluations compared to GA. We also comprehensively evaluate and discuss the effect of MCTS hyperparameters on the convergence to solution. Overall, our study demonstrates the effectives of using RL approaches that operate in discrete action space for inverse defect design problems.

42 ENGINEERING↗
DOE OSTI2020

Characterization of water-based liquid scintillator for Cherenkov and scintillation separation

Abstract This paper presents measurements of the scintillation light yield and time profile for a number of concentrations of water-based liquid scintillator, formulated from linear alkylbenzene (LAB) and 2,5-diphenyloxazole (PPO). We find that the scintillation light yield is linear with the concentration of liquid scintillator in water between 1 and 10% with a slope of $$127.9\pm 17.0$$ 127.9 ± 17.0 ph/MeV/concentration and an intercept value of $$108.3\pm 51.0$$ 108.3 ± 51.0 ph/MeV, the latter being illustrative of non-linearities with concentration at values less than 1%. This is larger than expected from a simple extrapolation of the pure liquid scintillator light yield. The measured time profiles are consistently faster than that of pure liquid scintillator, with rise times less than 250 ps and prompt decay constants in the range of 2.1–2.85 ns. Additionally, the separation between Cherenkov and scintillation light is quantified using cosmic muons in the CHESS experiment for each formulation, demonstrating an improvement in separation at the centimeter scale. Finally, we briefly discuss the prospects for large-scale detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Graph-Env

The Graph-Env library provides a framework for adapting graph search problems into OpenAI Gym environments for reinforcement learning. In other words, Graph-Env enables reinforcement learning algorithms to be applied to graph search problems. Graph search problems include molecule and crystal structure design problems; route planning problems, including traveling salesperson problem; puzzles and games, such as chess and go; shortest path problems; minimum spanning tree; vehicle path generation; and others.

Tripp, Charles↗
DOE OSTI2025

Twinac: initiation of a community-driven accelerator digital twin framework

We present the initiation of a community-driven framework for the integration of accelerator digital twins into control systems: Twinac. Few facilities have fully integrated accelerator digital twins like at Cornell’s CHESS. Many facilities have active research to employ surrogate models to aid in operational decisions like at Argonne’s ALS, MSU’s FRIB, SLAC’s LCLS-II, and Fermilab’s FAST/IOTA, PIP-II, and main complex. To lower the barrier to entry for all accelerator facilities to build and benefit from a digital twin of their own accelerators, we propose the following software framework. Twinac will provide the capability to compose one’s own digital twin using reusable components engineered at other facilities. With this model in place, Twinac will also support tools for (1) predictive maintenance systems; (2) discovery of correlated but uncontrolled environmental factors, like seasonal temperature variations causing performance changes on power supplies, magnets, etc.; and (3) prototyping and updating sophisticated optimization and controls algorithms. The Twinac framework will enable sharing and simplified deployment of modeled components and control algorithms at all facilities. With an inter-facility team to build and support the Twinac framework, it will be easy to publish and try out the latest advancements at one’s own facility.

Miceli, Tia [Fermilab]↗
DOE OSTI2022

graphenv: a Python library for reinforcement learning on graph search spaces

Many important and challenging problems in combinatorial optimization (CO) can be expressed as graph search problems, in which graph vertices represent full or partial solutions and edges represent decisions that connect them. Graph structure not only introduces strong relational inductive biases for learning (Battaglia et al., 2018) - in this context, by providing a way to explicitly model the value of transitioning (along edges) between one search state (vertex) and the next - but lends itself to problems both with and without clearly defined algebraic structure. For example, classic CO problems on graphs such as the Traveling Salesman Problem (TSP) can be expressed as either pure graph search or integer programs. Other problems, however, such as molecular optimization, do no have concise algebraic formulations and yet are readily implemented as a graph search (V. et al., 2022; Zhou et al., 2019). Such "model-free" problems constitute a large fraction of modern reinforcement learning (RL) research owing to the fact that it is often much easier to write a forward simulation that expresses all of the state transitions and rewards, than to write down the precise mathematical expression of the full optimization problem. In the case of molecular optimization, for example, one can use domain knowledge alongside existing software libraries to model the effect of adding a single bond or atom to an existing but incomplete molecule, and let the RL algorithm build a model of how good a given decision is by "experiencing" the simulated environment many times through. In contrast, a model-based mathematical formulation that fully expresses all the chemical and physical constraints is intractable. In recent years, RL has emerged as an effective paradigm for optimizing searches over graphs and led to state-of-the-art heuristics for games like Go and chess, as well as for classical CO problems such as the TSP. This combination of graph search and RL, while powerful, requires non-trivial software to execute, especially when combining advanced state representations such as Graph Neural Networks (GNN) with scalable RL algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Quantifying the Role of APB Tubes on the Work-hardening of Ordered Phases

For sixty years, it has been theorized that antiphase boundary (APB) tubes, which are defects produced by deformation, contribute to the high strength of ordered alloys or intermetallic compounds. The concept of an APB tube was originally proposed by Vidoz and Brown [A.E. Vidoz, L.M. Brown, "On work-hardening in ordered alloys", Philosophical Magazine, 7 (1962) 1167–1175] in 1962 to explain the ~50% increase in strength observed when an alloy, such as Ni 3 Fe, is chemically ordered compared to when it is disordered. The existence of APB tubes was only definitively confirmed by Chou and Hirsch in 1981 [C.T. Chou, P.B. Hirsch Antiphase domain boundary tubes in plastically deformed ordered Fe 30.5 at.% Al alloy, Philosophical Magazine, A. 44 (1981) 1415–1419] using a transmission electron microscope (TEM). Prior to the current work, there had been no studies trying to correlate the presence of APB tubes with their effect on the mechanical properties. The main finding of this project was that APB tubes do not contribute to the strength of the strongly-ordered intermetallic compounds FeAl and Ni 3 Al. This was shown by removing the APB tubes by heating the materials and showing that the mechanical properties were unaffected. In addition, in-situ deformation experiments were undertaken in a TEM on both disordered and ordered Ni 3 Fe, in which it was shown that defects (superdislocations) in the ordered alloy underwent many more interaction events than defect (dislocations) in the disordered alloy. This difference can explain the increase in the increase in strength observed when this material is ordered. The project supported a PhD student, Rachel Osmundsen, who successfully defended her thesis in April, 2022. It also supported the principal investigator, Ian Baker, two PhD exchange students and an undergraduate researcher. The work involved collaborations with the following people and use of the following facilities: Dr. Si Chen, Advanced Photon Source at Argonne National Laboratory, for synchrotron X-ray diffraction; and Dr. Katherine Shanks and Dr. Kelly Nygren, Cornell High Energy Synchrotron Source (CHESS), for synchrotron X-ray diffraction. The work led to 12 papers published in refereed journals with two papers currently submitted to journals. There were also 16 presentation made at conferences and universities on the work, ten of which were invited.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Solute effects upon dislocation motion and recovery in Mg alloys (Final Report)

The objective the research was to develop a firmer understanding of the interactions between substitutional solute atoms and dislocations within Mg alloys. These interactions govern the absolute and relative mobilities of various types of dislocations in Mg (e.g., basal < a>, non-basal < a>, and pyramidal < c+a>). Furthermore, they have an impact on dislocation recovery processes (e.g., cross-glide, climb, rearrangement, and annihilation). Ultimately, solute-dislocation interactions strongly impact a) strain hardening, b) strain rate sensitivity, c) plastic anisotropy of textured polycrystals, d) texture evolution, e) dislocation substructures evolution, and even f) recrystallization behavior. It is broadly known that Mg alloys exhibit poor low temperature formability. Since formability is largely governed by properties (a) – (c) in this list, it is critical to develop a better understanding of solute-dislocation interactions, if one hopes to improve the situation. While the theory of static solute strengthening is well developed, especially for alloys with face centered cubic (FCC) crystal structures, there are outstanding questions related to applications to hexagonal close packed (HCP) crystal structures and dynamic strain aging (DSA) in materials of various structures. DSA has far-reaching implications for metal formability and, in the case of Mg alloys, appears to correlate with the so-called rare-earth (RE) texture which has been shown to benefit formability. The Portevin-Le Chatelier (PLC) effect, and associated negative strain rate sensitivity, occur at higher temperatures (>100℃) in Mg alloys as compared with similar Al alloys, even though they have similar melting points and solute diffusivities. Our preliminary research has shown that modern, physics-based models of DSA can be tuned to describe the behavior of Mg alloys if a rather higher activation enthalpy is assumed for cross-core diffusivity. While this partially explains the delay in DSA to higher temperatures, it is also hypothesized that this delay is due in part to the intrinsically more thermally activated (rate sensitive) nature of non-basal < a> dislocation motion which is required for macroscopic flow of Mg alloys, whereas octahedral slip in many FCC metals like aluminum is essentially athermal at room temperature. It was originally proposed to employ a combination of in-situ diffraction-based experimental characterization to validate existing theory. It was envisioned to perform in-situ transmission electron microscopy (TEM) to assess individual dislocation behavior and in-situ high-energy X-ray diffraction (HEXRD) techniques which were showing great promise for elucidating collective dislocation behavior, 2 including recovery, especially if the contributions to various forms of diffraction peak broadening (𝜂𝜂,𝜔𝜔,and 2𝜃𝜃) can be effectively integrated. Finally, it was envisioned to perform discrete dislocation dynamics (DDD) modeling approaches to aide in the interpretation of both TEM and HEXRD experiments. In the end, mechanical tests were performed on more complex Mg alloys which exhibited evidence of dynamic strain aging, and this led to the establishment of another project. Mechanical test data obtained at McMaster University served as the basis of an assessment of the applicability of Bazinski’s “stress equivalence” theory of solute strengthening to polycrystalline alloys of Mg. Although we did not succeed in applying the approach to Mg alloys, we did develop expertise with the HEXRD approach using a BCC, β-Ti alloy and demonstrated numerous new capabilities that may be applied to any polycrystalline material in collaboration with researchers at CHESS and around the world: (1) assessment of details of the elastoplastic transition (yielding) using a combination of HEXRD and full-field polycrystal plasticity modeling, (2) the first-ever experimental observation of strong stress rotation within the individual grains of a polycrystalline material, and (3) a comprehensive analysis of the grain-level dislocation density evolution based upon diffraction peak broadening along 𝜂𝜂,𝜔𝜔,and 2𝜃𝜃 directions. This final aspect allowed us to confirm that dislocations were gliding on multiple plane types and not restricted to {110} type planes, and it also provided clues as to why some grains were unloading during straining, with surprising implications for our understanding of the effects of geometrically necessary dislocations (GNDs). Finally, graduate student, Mohammed Shabana, developed a MATLAB code which confirmed the conclusions of Prof. Catalin Picu (Rensallear Polytechnic Institute, RPI) regarding the effect of solute-trapped, forest dislocations on the breaking stress of Lomer lock junctions in FCC metal alloys. He applied the same anisotropic line-tension model to a variety of dislocation junction configurations and found an inconsistency in the widely cited results of Dupuy and Fivel regarding the Hirth Lock, and he outlined an approach to extend these finding to HCP Mg alloys that we are still pursuing with discretionary fundings at UVA.

36 MATERIALS SCIENCE↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Thermal Conductivity of AM Fabricated Stainless Steel 316L Part

Thermal properties are an integral part of many diverse engineering applications, and additive manufacturing (AM), particularly laser powder bed fusion (LPBF) is shown to affect thermal properties due to the laser processing parameters. For 316L stainless steel, there is little prior research to determine the effects of the process on thermal properties. In this work, the temperature gradient is shown to create uniform, chess board-like distributions of grains in the build direction. These zones create dislocations which were visualized with EBSD techniques. Processing parameters cause hierarchal grain size distribution, with localized concentrations of small grains and large grains. Thermomechanical stresses in the rapid solidification increases dislocation density during grain formation. Previous research shows a higher density of dislocations decreases local thermal conductivity. Local and bulk thermal conductivity are shown in this work to have statistically lowered values to an average of 10-12 W/m-K compared to 14 W/m-K for conventional 316L.

36 MATERIALS SCIENCE↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2026

Stress and Strain Heterogeneity and Persistence in Uniaxially‐ and Triaxially‐Loaded Sandstone

Two critical questions in brittle rock mechanics are how rocks developed localized strains and to what extent internal stress heterogeneity controls this localization and subsequent macroscopic failure. Definitive answers have not yet been found, but would provide insight into rock fracture mechanics as relevant to hydrocarbon extraction and sequestration. Here, we use synchrotron X‐ray tomography (XRT) and 3D X‐ray diffraction (3DXRD) during uniaxial and triaxial tests on Nugget and Bentheimer sandstones to examine strain and stress localization prior to mechanical failure. 3DXRD was used to measure intra‐granular lattice strains which were used to compute elastic stress tensors of each grain. Digital volume correlation (DVC) was applied to XRT images to determine the strain field in the sample. Both samples featured marked spatial heterogeneity, localization, and temporal persistence of elevated stresses and strains during their mechanical deformation toward failure. Both samples featured a majority of grains with at least one principal stress component that was tensile, a signature of the influence of heterogeneity on stress transmission. Measurements further revealed that compressive stress orientations and statistics evolved in a similar manner to those of inter‐particle forces in loose granular materials, with triaxially‐compressed rock exhibiting enhanced grain stress heterogeneity compared to uniaxially‐compressed rock. Our results complement recent work by others who employed XRT and scanning 3DXRD to study triaxially‐compressed sandstone, but extend those results to uniaxial compression, sandstones of varied porosity, and grain stress measurements throughout the 3D full extent of the samples rather than in a single layer examined with scanning 3DXRD.

58 GEOSCIENCES↗
DOE OSTI2025

Spontaneous Formation of Single-Crystalline Spherulites in a Chiral 2D Hybrid Perovskite

In two-dimensional (2D) chiral metal-halide perovskites (MHPs), chiral organic spacers induce structural chirality and chiroptical properties in the metal-halide sublattice. This structural chirality enables reversible crystalline-glass phase transitions in (S-NEA) 2 PbBr 4 , a prototypical chiral 2D MHP where NEA + represents 1-(1-naphthyl)ethylammonium. Here, in this study, we investigate two distinct spherulite states of (S-NEA) 2 PbBr 4 , exhibiting either radial-like or stripe-like banded patterns depending on the annealing conditions of the amorphous film. Despite similarities in optical absorption and photoluminescence, the stripe-like, banded spherulite exhibits higher crystallinity and improved optical transparency compared to those of radial-like spherulite. X-ray nanoprobe measurements reveal tilting-angle modulations in the octahedral plane of stripe-like spherulites, correlating with the film’s surface geometry. Transfer matrix calculations indicate that the optical contrast in stripe-like patterns, seen in bright-field optical microscopy, arises from optical interference effects, differing from the contrast mechanism observed in polymer spherulites. Ultrafast carrier dynamics experiments suggest that the stripe-like spherulites resemble single crystals more closely than radial-like spherulites, while electrical conductivity measurements show enhanced charge carrier transport in stripe-like spherulites. These findings offer insights into MHP spherulite states with a single composition but different morphologies, previously observed only in polymers, highlighting their potential for optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fast product release requires active-site water dynamics in carbonic anhydrase

Water plays an essential role in enzyme structure, stability, and the substantial rate enhancement of enzyme catalysis. However, direct observations linking enzyme catalysis and active-site water dynamics pose a significant challenge due to experimental difficulties. By integrating an ultraviolet (UV) photolysis technique with temperature-controlled X-ray crystallography, we track the catalytic pathway of carbonic anhydrase II (CAII) at 1.2 Å resolution. This approach enables us to construct molecular movies of CAII catalysis, encompassing substrate (CO 2 ) binding, conversion from substrate to product (bicarbonate), and product release. In the catalytic pathway, we identify an unexpected configuration in product binding and correlate it with sub-nanosecond rearrangement of active-site water. Based on these experimental observations, we propose a comprehensive mechanism of CAII and describe the detailed structure and dynamics of active-site water in CAII. Our findings suggest that CAII has evolved to utilize the structure and fast dynamics of the active-site waters for its diffusion-limited catalytic efficiency.

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

Pervasive symmetry-lowering nanoscale structural fluctuations in the cuprate La 2−𝑥 ⁢Sr 𝑥 ⁢Cu⁢O 4

The cuprate superconductors are among the most widely studied quantum materials, yet there remain fundamental open questions regarding their electronic properties and the role of structural degrees of freedom. Recent neutron and x-ray scattering measurements uncovered exponential scaling with temperature of the strength of orthorhombic fluctuations in the tetragonal phase of La 2−𝑥⁢ Sr 𝑥 ⁢Cu⁢O 4 and Tl 2 ⁢Ba 2 ⁢Cu⁢O 6+𝛿 , unusual behavior that closely resembles prior results for the emergence of superconducting fluctuations, and that points to a common origin rooted in inherent correlated structural inhomogeneity. Here, in this study, we use neutron and x-ray diffuse scattering to provide further insight into structural fluctuations in La 2−𝑥 ⁢Sr 𝑥 ⁢Cu⁢O 4 . We perform measurements up to temperatures that approach the melting point of the undoped parent compound, and we investigate the effects of in situ in-plane uniaxial elastic stress and plastic deformation. Our neutron scattering results for the parent compound La 2 ⁢Cu⁢O 4 reveal that short-range orthorhombic fluctuations persist to the maximum experimental temperature of nearly 1000 K, i.e., to a significant fraction of the crystallization temperature. At this temperature, the spatial characteristic length extracted from the momentum-space data is still about three lattice constants. The neutron scattering experiment enables quasistatic discrimination and reveals that the response is increasingly dynamic at higher temperatures. We also find that neither compressive elastic stress along the tetragonal [110] direction nor irreversible plastic deformation activating the ⟨100⟩⁢[001] slip system significantly alter the robust orthorhombic fluctuations. Overall, these results support the notion that these fluctuations couple to subtle, underlying inhomogeneity that underpins the cuprate phase diagram. Finally, in La 1.8⁢ Sr 0.2⁢ Cu⁢O 4 , we uncover low-energy structural fluctuations at a nominally forbidden reflection that are distinct from the orthorhombic distortions. While the origin of these fluctuations is not clear, they might be related to the presence of extended defects such as dislocations or stacking faults.

Spieker, R. J. [Univ. of Minnesota, Minneapolis, M↗