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DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗

DOE OSTI2025

Directed self-assembly of block copolymers for high-precision patterning in the era of extreme ultraviolet lithography

Extreme ultraviolet (EUV) lithography enables unprecedented resolution in semiconductor patterning but faces critical challenges in developing resist materials that achieve high-precision at economically viable throughput. Directed self-assembly (DSA) of block copolymers (BCPs) offers a promising solution for pattern rectification by leveraging thermodynamically determined domain structures to decouple BCP pattern quality from the imperfect original lithographic pattern. This prospective presents an overview of recent progress on the EUV + DSA strategy, covering advances in BCP material design, processing, metrology, and pattern transfer. We highlight recent advances in high-χ BCPs with perpendicular orientation and domain spacings compatible with EUV dimensions, leveraging A-b-(B-r-C) architectures. We also discuss progress in chemical pre-pattern fabrication using both positive- and negative tone resists, along with processing strategies to minimize defects and roughness based on BCP thermodynamics and assembly kinetics. We further examine metrology platforms for characterizing the thermodynamics of BCP materials and quantifying the size and shape of BCP domains. Lastly, we review pattern transfer strategies for generating functional inorganic masks suitable for semiconductor manufacturing. Together, these advances highlight the potential of DSA to complement EUV lithography, offering a pathway to address critical challenges in achieving high-precision patterning for the semiconductor industry.

Lee, Kyunghyeon [Univ. of Chicago, IL (United Stat↗

DOE OSTI2025

Entropy-Assisted Quality Pattern Identification in Finance

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.

Physics↗

DOE OSTI2018

Atomic layer chemical patterns for block copolymer assembly

Provided herein are methods of directed self-assembly (DSA) on atomic layer chemical patterns and related compositions. The atomic layer chemical patterns may be formed from two-dimensional materials such as graphene. The atomic layer chemical patterns provide high resolution, low defect directed self-assembly. For example, DSA on a graphene pattern can be used achieve ten times the resolution of DSA that is achievable on a three-dimensional pattern such as a polymer brush. Assembly of block copolymers on the atomic layer chemical patterns may also facilitate subsequent etch, as the atomic layer chemical patterns are easier to etch than conventional pattern materials.

Nealey, Paul Franklin↗

DOE OSTI2018

Lithography-Free Electrochemical Patterning of Conductive Substrates with Metal Oxides

Reactive interface patterning promoted by lithographic electrochemistry serves as a facile method for generating submicron structures on conductive substrates. A binary-potential step applied to a metal layer with a resist overlayer allows silicon to be patterned with metal oxides. Here, the role and influence of the resist overlayer on the uniformity of pattern formation are examined. The ability of the resist to detach from the underlying metal is a critical determinant of pattern geometry. By choosing an appropriate resist, large patterns with submicron precision are generated quickly by the application of the binary-potential steps. From this information, a lithography-free method to generating identical patterns is achieved with simple resists such as that furnished from a lacquer–water emulsion, thus greatly simplifying the patterning of silicon with metal oxide catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Diffusion-assisted growth of periodic patterns on metal surfaces

Periodic patterns formed on various solid surfaces could seriously affect the properties of materials. Understanding the underlying mechanism is a key step to apply and control pattern formation in many fields. In this work, periodic patterns induced by low energy plasma exposures on tungsten surfaces are observed and related new formation mechanisms are developed. Results from experiments and computer simulations demonstrate that atom diffusion accelerated by low energy and high flux exposures is the dominant mechanism for pattern formation. Additionally, a similar pattern formation has been confirmed by plasma exposure experiments on molybdenum. Based on this mechanism, patterns with controllable size and shape have been successfully prepared on tungsten surface by adjusting plasma exposure parameters, which may provide a new understanding of pattern formation and related surface damage in tungsten induced by plasma exposures.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Nucleation and growth of blue phase liquid crystals on chemically-patterned surfaces: a surface anchoring assisted blue phase correlation length

In condensed matter, the correlation length is an essential parameter which describes the distance over which a material maintains its structural properties. In liquid crystals, for the orientational order parameter this characteristic length is of the order of nanometers, while in solid crystals it can extend to macroscopic length scales. Here, we report the measurement of the correlation length, or persistence length, of the crystallographic orientation of blue phases (BPs)—chiral liquid crystals with long-range 3D-crystalline structures and submicron-sized lattice-parameters. These materials exhibit phase transformations that have been identified as the liquid analog of crystal-crystal martensitic transformations. In this work, we use liquid epitaxial growth to achieve spontaneous BP-crystal nucleation and subsequent growth. Specifically, we design patterned substrates made of a binary array of regions with different liquid crystal anchoring, which facilitate a uniform nucleation and growth of BP-crystals with (100)-lattice orientation and a simple cubic symmetry. Furthermore, our results indicate that this simple cubic BP, the so-called blue phase II (BPII), forms first on the patterned surface, thereby propagating the growth of domains in directions parallel or perpendicular to the patterned regions. These results are used to understand the emergence of a surface anchoring assisted BPII-correlation length, taken as the distance over which the BP preserves its lattice orientation, as a function of time and pattern characteristics. We found that BPII single crystals can be achieved on patterned regions whose lateral dimensions are equal to or larger than 10 μm, consistent with our measurements of the BPII-correlation length. This newly acquired understanding of the role of surfaces on the formation of BPs is then used to design processes that permit formation of macroscopically large mono-domain, single-crystal BPs by relying on significantly reduced patterned areas (only part of the area is patterned), a feature that could benefit the applications of this intriguing class of materials.

correlation length↗

DOE OSTI2015

The effect of process parameters on Twin Wire Arc spray pattern shape

A design of experiments approach was used to describe process parameter—spray pattern relationships in the Twin Wire Arc process using zinc feed stock in a TAFA 8835 (Praxair, Concord, NH, USA) spray torch. Specifically, the effects of arc current, primary atomizing gas pressure, and secondary atomizing gas pressure on spray pattern size, spray pattern flatness, spray pattern eccentricity, and coating deposition rate were investigated. Process relationships were investigated with the intent of maximizing or minimizing each coating property. It was determined that spray pattern area was most affected by primary gas pressure and secondary gas pressure. Pattern eccentricity was most affected by secondary gas pressure. Pattern flatness was most affected by primary gas pressure. Lastly, coating deposition rate was most affected by arc current.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Intrinsically Honeycomb-Patterned Hydrogenated Graphene

Since the advent of graphene ushered the era of 2D materials, many forms of hydrogenated graphene have been reported, exhibiting diverse properties ranging from a tunable bandgap to ferromagnetic ordering. Patterned hydrogenated graphene with micron-scale patterns has been fabricated by lithographic means. We report successful millimeter-scale synthesis of an intrinsically honeycomb-patterned form of hydrogenated graphene on Ru(0001) by epitaxial growth followed by hydrogenation is reported. Combining scanning tunneling microscopy observations with density-functional-theory (DFT) calculations, it is revealed that an atomic-hydrogen layer intercalates between graphene and Ru(0001). The result is a hydrogen honeycomb structure that serves as a template for the final hydrogenation, which converts the graphene into graphane only over the template, yielding honeycomb-patterned hydrogenated graphene (HPHG). In effect, HPHG is a form of patterned graphane. DFT calculations find that the unhydrogenated graphene regions embedded in the patterned graphane exhibit spin-polarized edge states. This type of growth mechanism provides a new pathway for the fabrication of intrinsically patterned graphene-based materials.

2D material↗

DOE OSTI2019

Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges

Characterization of a parallel application’s communication patterns can be useful for performance analysis, debugging, and system design. However, obtaining and interpreting a characterization can be difficult. AChax implements an approach that uses search and a library of known communication patterns to automatically characterize communication patterns. Our approach has some limitations that reduce its effectiveness for the patterns and pattern combinations used by some real-world applications. By viewing AChax’s pattern recognition problem as an image recognition problem, it may be possible to use deep learning to address these limitations. In this position paper, we present our current ideas regarding the benefits and challenges of integrating deep learning into AChax and our conclusion that a hybrid approach combining deep learning classification, regression, and the existing AChax approach may be the best long-term solution to the problem of parameterizing recognized communication patterns.

Roth, Philip↗

DOE OSTI2024

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

Abstract Background There are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (> 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce. Objective Additive manufacturing capabilities offer consistent patterns using programmable fabrication methods. Methods In this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature. Results The results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges. Conclusion Our results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

Novich, K. A. (ORCID:0000000204466022)↗

DOE OSTI2024

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. Here, we hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network design on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6 x 10 -4 . The average prediction error of β-phase volume fraction was within 1.6 x 10 -2 (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Morphology and orientation of β-BaB{sub 2}O{sub 4} crystals patterned by laser in the inside of samarium barium borate glass

Nonlinear optical β-BaB{sub 2}O{sub 4} crystal lines (β-BBO) were patterned in the inside of 8Sm{sub 2}O{sub 3}–42BaO–50B{sub 2}O{sub 3} glass by irradiations of continuous-wave Yb:YVO{sub 4} lasers with a wavelength of 1080 nm (power: P=0.8–1.0 W, scanning speed: S=0.2–2.5 μm/s), in which the laser focal position was moved gradually from the surface to the inside. The morphology, size, and orientation of β-BBO crystals were examined from polarization optical microscope and birefringence imaging observations. It was demonstrated that c-axis oriented β-BBO crystals with long lengths (e.g., 20 mm) were patterned in the inside of the glass. The morphology of β-BBO in the cross-section of lines was a rectangular shape with rounded corners, and the volume of β-BBO formed increased with increasing laser power and with decreasing laser scanning speed. The maximum depth in the inside from the surface for β-BBO patterning increased with increasing laser power, e.g., D{sub max}∼100 μm at P=0.8 W, D{sub max}∼170 μm at P=0.9 W, and D{sub max}∼200 μm at P=1 W. The present study proposes that the laser-induced crystallization opens a new door for applied engineering in glassy solids. - Graphical abstract: This figure shows the POM photographs for β-BaB{sub 2}O{sub 4} crystal lines patterned by cw Yb:YVO{sub 4} fiber laser irradiations with a laser power of P=0.8 W and a laser scanning speed S=2 μm/s in the glass. The laser focal point was moved gradually from the surface into the inside. The results shown in Fig. 1 demonstrate that it is possible to pattern highly oriented β-BaB{sub 2}O{sub 4} crystals even in the inside of glasses. - Highlights: • β-BaB{sub 2}O{sub 4} crystal lines were patterned in the inside of a glass by lasers. • Laser focal position was moved gradually from the surface to the inside. • Birefringence imaging was observed. • Morphology, size, and orientation of crystals were clarified. • Crystal lines with long lengths (e.g., 20 mm) were patterned at the depth of 200 μm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Advanced morphological analysis of patterns of thin anodic porous alumina

Different conditions of fabrication of thin anodic porous alumina on glass substrates have been explored, obtaining two sets of samples with varying pore density and porosity, respectively. The patterns of pores have been imaged by high resolution scanning electron microscopy and analyzed by innovative methods. The regularity ratio has been extracted from radial profiles of the fast Fourier transforms of the images. Additionally, the Minkowski measures have been calculated. It was first observed that the regularity ratio averaged across all directions is properly corrected by the coefficient previously determined in the literature. Furthermore, the angularly averaged regularity ratio for the thin porous alumina made during short single-step anodizations is lower than that of hexagonal patterns of pores as for thick porous alumina from aluminum electropolishing and two-step anodization. Therefore, the regularity ratio represents a reliable measure of pattern order. At the same time, the lower angular spread of the regularity ratio shows that disordered porous alumina is more isotropic. Within each set, when changing either pore density or porosity, both regularity and isotropy remain rather constant, showing consistent fabrication quality of the experimental patterns. Minor deviations are tentatively discussed with the aid of the Minkowski measures, and the slight decrease in both regularity and isotropy for the final data-points of the porosity set is ascribed to excess pore opening and consequent pore merging. - Highlights: • Thin porous alumina is partly self-ordered and pattern analysis is required. • Regularity ratio is often misused: we fix the averaging and consider its spread. • We also apply the mathematical tool of Minkowski measures, new in this field. • Regularity ratio shows pattern isotropy and Minkowski helps in assessment. • General agreement with perfect artificial patterns confirms the good manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Additive Lithography–Organic Monolayer Patterning Coupled with an Area-Selective Deposition

The combination of area-selective deposition (ASD) with a patternable organic monolayer provides a versatile additive lithography platform, enabling the generation of a variety of nanoscale feature geometries. Stearate hydroxamic acid self-assembled monolayers (SAMs) were patterned with extreme ultraviolet (λ = 13.5 nm) or electron beam irradiation and developed with ASD to achieve line space patterns as small as 50 nm. Density functional theory was employed to aid in the synthesis of hydroxamic acid derivatives with optimized packing density to enhance the imaging contrast and improve dose sensitivity. Near-edge X-ray absorption fine structure spectroscopy and infrared spectroscopy reveal that the imaging mechanism is based on improved deposition inhibition provided by the cross-linking of the SAM to produce a more effective barrier during a subsequent deposition step. With patterned substrates composed of coplanar copper lines and silicon spacers, hydroxamic acids selectively formed monolayers on the metal portions and could undergo a pattern-wise exposure followed by ASD in the first combination of a patternable monolayer with ASD. This material system presents an additional capability compared to traditional ASD approaches that generally reflect a starting patterned surface. Finally, this bottoms-up additive approach to lithography may be a viable alternative to subtractive nanoscale feature generation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Habitat-specific patterns and drivers of bacterial β-diversity in China’s drylands

The existence of biogeographic patterns among most free-living microbial taxa has been well established, yet little is known about the underlying mechanisms that shape these patterns. In this work, we examined soil bacterial β-diversity across different habitats in the drylands of northern China. We evaluated the relative importance of environmental factors versus geographic distance to a distance-decay relationship, which would be explained by the relative effect of basic ecological processes recognized as drivers of diversity patterns in macrobial theoretical models such as selection and dispersal. While the similarity of bacterial communities significantly declined with increasing geographic distance, the distance-decay slope and the relative importance of factors driving distance-decay patterns varied across different habitats. A strong distance-decay relationship was observed in the alpine grassland, where the community similarity was influenced only by the environmental factors. Yet, geographic distance was solely responsible for community similarity in the desert. Even the average compositional similarity among locations in the desert was distinctly lower compared with those in other habitats. We found no evidence that dispersal limitation strongly influenced the β-diversity of bacterial communities in the desert grassland and typical grassland. Together, our results provide robust evidence of habitat specificity for microbial diversity patterns and their underlying drivers. Our findings suggest that microorganisms also have multiple drivers of diversity patterns and some of which may be parallel to some fundamental processes for explaining biodiversity patterns in macroorganisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Directed self-assembly of block copolymer films on atomically-thin graphene chemical patterns

Directed self-assembly of block copolymers is a scalable method to fabricate well-ordered patterns over the wafer scale with feature sizes below the resolution of conventional lithography. Typically, lithographically-defined prepatterns with varying chemical contrast are used to rationally guide the assembly of block copolymers. The directed self-assembly to obtain accurate registration and alignment is largely influenced by the assembly kinetics. Furthermore, a considerably broad processing window is favored for industrial manufacturing. Using an atomically-thin layer of graphene on germanium, after two simple processing steps, we create a novel chemical pattern to direct the assembly of polystyreneblock-poly(methyl methacrylate). Faster assembly kinetics are observed on graphene/germanium chemical patterns than on conventional chemical patterns based on polymer mats and brushes. This new chemical pattern allows for assembly on a wide range of guiding periods and along designed 90° bending structures. We also achieve density multiplication by a factor of 10, greatly enhancing the pattern resolution. Lastly, the rapid assembly kinetics, minimal topography, and broad processing window demonstrate the advantages of inorganic chemical patterns composed of hard surfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2015

A quantitative analysis of the efficiency of solar-driven water-splitting device designs based on tandem photoabsorbers patterned with islands of metallic electrocatalysts

The trade-off between the optical obscuration and kinetic overpotentials of electrocatalyst films patterned onto the surface of tandem light-absorber structures in model photoelectrosynthetic water-splitting systems was investigated using a 0-dimensional load-line analysis and experimental measurements. The electrocatalytic performance of the catalyst at high current densities, normalized to the electrocatalyst surface area, is an important factor in the dependence of the optimal solar-to-hydrogen (STH) conversion efficiency, η STH,opt , on the filling fraction ( f c ) of the patterned catalysts, because even under conditions that produce minority-carrier current densities of ~10 mA cm -2 at the solid/liquid interface, the current density at catalyst-bearing sites can be >1–2 A cm -2 in low filling-fraction films. A universal current-density versus potential relationship, up to current densities of 10 A cm -2 , was obtained experimentally for the hydrogen-evolution reaction (HER) using patterned Pt ultramicroelectrode (UME) arrays with a range of filling fractions and disc diameters. The η STH,opt of system designs that utilize patterned electrocatalysts located on the illuminated side of tandem photoabsorbers was then evaluated systematically. The maximum STH conversion efficiency, η STH,max , using a hypothetical electrocatalyst that was optically transparent but which nevertheless exhibited a current-density versus potential behavior that is characteristic of the most active Pt films measured experimentally regardless of their optical obscuration, was 26.7%. By comparison, the maximum η STH,opt of 24.9% for real patterned Pt electrocatalyst films closely approached this ideal-case limit. The performance and materials utilization of the patterned electrocatalysts and of the uniformly coated electrocatalysts on tandem photoabsorbers were also compared in this research. Hence, patterned electrocatalysts with very low filling fractions can provide a potentially promising path to the realization of efficient large-scale photoelectrolysis systems while minimizing the use of scarce noble metals.

14 SOLAR ENERGY↗