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DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Bridging the Gap Between Modern UX Design and Particle Accelerator Control Room Interfaces

Accelerator control systems often represent relatively complex and safety-sensitive human-machine interfaces within process control industries. These systems are technically robust and reflect the cumulative integration of solutions built and adapted across decades. One of the regular, unfortunate casualties of provisional accelerator control system updates is their human-system interfaces (HSIs) which often lag behind modern usability and design standards. An additional challenge is that although there is a multitude of established human factors (HF), and user experience (UX) principles for everyday digital applications, there are very few (if any) established principles for complex and safety-critical applications for an accelerator. This paper argues for the importance of established HF and UX principles (herein referred to as human-centered design principles) into the development of accelerator HSIs, emphasizing the need for clarity, consistency, responsiveness, and cognitive accessibility. Drawing from HF/UX best practices and human-centered design, this paper discusses how these approaches can enhance operator performance, reduce human error, and improve accelerator personnel collaboration. Case studies from Accelerator Control Operations Research Network (ACORN) at Fermilab are explored to demonstrate how interfaces built with human-centered design principles can scale with system complexity while remaining intuitive and efficient for diverse user roles including operators, machine experts, and engineers. By bridging the gap between traditional control system design and modern human-centered design methods, this paper provides a roadmap for evolving accelerator HSIs into more usable, maintainable, and effective tools.

Hill, Rachael [Idaho Natl. Lab.]↗

DOE OSTI2024

Heterogenous catalysis for oxygen tolerant photoredox atom transfer radical polymerization and small-molecule dehalogenation

Heterogeneous photocatalysts (PCs) have garnered attention for their sustainability and cost-effectiveness. Despite the existence of various types of these PCs, their synthesis often involves complex, multi-step procedures and laborious purification. Herein, we propose a simple method for attaching small-molecule photocatalytic species onto crosslinked 3-D polymer networks as insoluble scaffolds to create robust heterogeneous PCs. The highly swellable poly(ethylene glycol)-based ChemMatrix (CM) resin, known for its amphiphilic properties and high functional group loading, facilitated the covalent immobilization of the photoredox dye Eosin Y (EY), but also streamlined functionalization with Ir( III ) complexes. The resulting heterogeneous CM-EY demonstrated efficient photocatalytic performance in open-to-air dual photoredox catalysis of atom transfer radical polymerization (photo-ATRP) under green light. This was confirmed by the well-controlled synthesis of polymers with molecular masses ranging from 20 kDa to 300 kDa and low dispersities. Furthermore, CM-EY exhibited excellent photostability and recyclability over multiple cycles of ATRP. The heterogeneous catalysis of photo-ATRP provided high temporal control and enabled benign conditions for synthesizing protein-polymer hybrids (PPH). When combined with the initiator-modified CM (CM-BIB), CM-EY facilitated the solid-phase synthesis of homopolymers and block copolymers with recyclable performance. However, the coordinatively bound Ir@CM showed decreased catalytic activity and efficiency toward photoredox dehalogenation due to the leaching of active species during recycling. This study highlights the advantages of the covalent linking of catalysts to solid supports over non-covalent interactions, underscoring the potential of functionalized polymer resin as a promising scaffold. Such an approach offers customization and tunability, presenting opportunities for innovation in green chemistry.

Kapil, Kriti↗

DOE OSTI2025

Mesoscale Modeling of Hydrogels Under Frictional Shear Stress

Hydrogels are three-dimensional networks of hydrophilic polymers often used as a simplified model of hydrated biological materials, from cartilaginous joints to the ocular tear film. However, the lubrication mechanisms of hydrogels remain poorly understood, partly due to their complex polymeric structure, which creates blurred interfaces during sliding that are challenging to study experimentally. In this study, we employ dissipative particle dynamics (DPD) to investigate the frictional behavior of a polymeric hydrogel network sliding against a solid wall in an explicit viscous solvent. This computational approach enables us to model hydrodynamic interactions and mesoscale polymer dynamics, capturing key aspects of hydrogel friction. Our simulations reveal that hydrogel friction is governed by the interplay between polymer relaxation and viscous shear, characterized by the Weissenberg number (Wi). At low Wi, friction coefficient remain nearly constant, dominated by polymer relaxation. However, at higher Wi, friction is dominated by viscous drag within a near-wall solvent layer, leading to a linear increase in friction coefficient with Wi. Furthermore, our results demonstrate an inverse relationship between the friction coefficient and the applied normal load, consistent with experimental observations. This work provides new insights into the fundamental tribological properties of hydrogels, shedding light on the micromechanics of hydrogel friction. Improving our understanding of hydrogel structure and dynamics under friction advances our knowledge of the mechanisms regulating biological lubrication in health and disease.

36 MATERIALS SCIENCE↗

DOE OSTI2025

The developments in modifying functionality of lignin and its application in biocomposites

With the advancement of sustainable material innovations, renewable natural biopolymers are gradually replacing traditional metal and petroleum-based synthetic materials due to their environmental friendliness, biodegradability, and economic advantages. Lignin, the second most abundant natural aromatic polymer in the plant kingdom, has emerged as a key candidate raw material for the development of green polymer systems because of its unique phenylpropane unit network structure, high carbon content, and rich functional group characteristics. However, challenges such as the inherent structural complexity, chemical inertness, and uneven molecular weight distribution of lignin limit its direct application. By employing modification strategies such as chemical functionalization and physical regulation, researchers can precisely control its reactivity, thermal stability, and interfacial compatibility, enabling the preparation of high-performance lignin-based functional composites. Here, this paper systematically reviews the principles and methodological advancements in lignin's multi-dimensional modification technology. It analyzes the mechanisms by which various chemical and physical modification techniques enhance the mechanical properties, functional responsiveness, and environmental adaptability of materials, and discusses innovative applications in fields such as intelligent packaging, biomedical materials, and energy storage devices. Furthermore, this review addresses the key challenges encountered in the high-value transformation of lignin, with the aim of offering a theoretical framework and technical pathway for the transformative development of lignin from agricultural and forestry by-products to functional material platforms.

Functional composites↗

DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗

DOE OSTI2026

Radiation Damage Mechanisms in Oxide-Based Solid Electrolytes

Ion implantation is a promising approach to mitigate dendrite formation in metal anode solid-state batteries by modifying the chemical and mechanical properties of oxide-based electrolyte surfaces such as Li 7 La 3 Zr 2 O 12 (LLZO). Yet, the detailed atomistic mechanisms and potentially adverse side effects of ion implantation remain poorly understood. In this study, we used molecular dynamics simulations to elucidate the evolution of radiation-induced damage in LLZO. The results reveal that radiation damage is primarily driven by recoils from heavy ions in the lattice, producing antisite defect clusters whose density decreases as recoil energy increases, a counterintuitive finding that highlights complex cascade fragmentation. Radiation-induced disruption to the crystal sublattice connectivity, especially in the La–O network critical for Li-ion transport, leads to degradation in ionic pathways at low energies, while higher energy cascades can form new conduction pathways via enhanced Zr–O network connectivity. These findings provide a mechanistic foundation for optimizing ion implantation strategies that balance disruption of the lattice for surface modification for dendrite resistance with preservation of essential ionic conductivity.

Defects↗

DOE OSTI2025

Leveraging dendritic complexity for neuromorphic computing

Abstract Beyond-von Neumann computing approaches are necessary to sustain the growth of microelectronics and the increasing appetite for artificial intelligence/machine learning algorithms. Neuromorphic computing is an emerging paradigm that takes inspiration from the brain to provide a path forward to improve the computational efficiency and computational density of next-generation computing architectures. In nature, we observe brains performing complex computations with a much smaller energy footprint than conventional computing approaches. Current neuromorphic systems are focused primarily on scalability, namely, increasing the number of computational units (neurons) and connections between units (synapses). However, for brain-like cognition and efficiency in next-generation computing hardware, we need increased complexity in function, as well as improved connection density for scalability. Here, we present our work that aims to incorporate dendrites for ‘compute-on-wire’ in neuromorphic architectures to increase the computational complexity (e.g. number of programmable parameters, nonlinear dynamics) as well as computational efficiency (energy/compute) of artificial neural networks (ANNs). We do this by showcasing neuromorphic dendrite elements that can be leveraged for various applications. We will present examples of neuroscience-inspired direction-selective circuits and an ANN with active dendrites leveraging shunting inhibition. We also demonstrate the benefits of using dendrites in deep neural networks. To conclude, we discuss how we can utilize emerging hardware devices in these systems and design next-generation neuromorphic architectures with dendrites.

Cardwell, Suma G. (ORCID:0000000226575545)↗

DOE OSTI2024

Geometric Measures of Trustworthiness for Machine Learning Predictions

his report details the findings from the research and investigation of Geometric Measures of Trustworthiness for Machine Learning Predictions. We explored the trustworthiness of machine learning (ML) models’ predictions using geometric measures to quantify the similarity of a query point with the training data. Predictive uncertainty in ML can originate from at least three sources: (1) Model uncertainty, which represents the uncertainty in model form (e.g. decision tree, vs neural network) and estimating the model parameters from the training data, (2) Data uncertainty, which represents the natural complexities of the data such as class overlap and inherent noise, and (3) Distributional uncertainty, which represents the mismatch between the training and operational distributions. The proposed measures focus on measuring and explaining the data and distributional uncertainties by measuring the relationships of operational data with the training data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

Plant Sciences↗

DOE OSTI2026

Locally purified maximally mixed states at scale: Entanglement pruning and symmetries

Locally Purified Density Operators (LPDOs) are state-of-the-art tensor network ansatze candidates that efficiently represent mixed quantum states at scale. However, given their non-uniqueness, their representational complexity is generally sub-optimal in practical computations. Here, in this work we perform a comprehensive numerical and analytical analysis and resolve this issue in the experimentally relevant limit where noise depolarizes the density operator into a maximally mixed state. To resolve the sub-optimality issue, we analyze two numerical tools, one analytic method, and detail the relations between them. The numerical tools used are fidelity-preserving truncations and isometric gauge transformations leveraging Riemannian optimizations over entropic objective functions. In addition, by invoking the injectivity and symmetry constraints of the maximally mixed LPDO, we also present analytical closed-form expressions for the disentangler and discuss their relation to numerical optimizers. Further, away from the maximally mixed state, our simulations highlight how the truncation threshold smoothly interpolate, as a function of depolarization, between established matrix product results and our new results. Our work shows how, by minimizing the resources required to represent key states of practical interest in experiment, the efficiency of tensor network algorithms can be substantially increased. This paves the path for uncovering tensor network’s fundamental scalability limits and latent potential in representing the wide locus of mixed quantum states that are accessible on near-term quantum devices.

Gangapuram, Amit Jamadagni [Oak Ridge National Lab↗

DOE OSTI2025

Utilizing Single-Crystalline Transformations for Precise Atom Placement in Multicomponent Cluster-Based Coordination Networks

The assembly of cluster or superatom building-blocks into extended solids has revolutionized materials design, enabling the synthesis of modular semiconductors with well-defined structures and tunable electronic, magnetic or optical properties. This strategy has recently advanced the synthesis of complex metal oxides with multifunctional or emergent behaviors, but precise atom placement of multiple elements with similar chemistries or preferred coordination environments remains a significant challenge. Here, in this study, we present a strategy for synthesizing polyoxometalate (POM)-based coordination networks with up to three different cations in precisely defined positions. Our approach leverages a single-crystal-to-single-crystal (SCSC) transformation in which the spatial placement of cations is governed by their availability at distinct stages of crystallization and transformation. Specifically, [ZP 5 W 30 O 110 ] (15-n)- (Z = Na + , K + , Ca 2+ , Ag + , Bi 3+ , Y 3+ , any Ln 3+ , Th 4+ ) is coordinatively assembled with various bridging metal cations (Y 3+ , any Ln 3+ , Th 4+ ). By using the encapsulated cation (Z) to "label" the POM, we track the phase-transformation and confirm the retention of single crystallinity. The integrated use of POM labeling and SCSC transformation enables rational control over cation distribution and establishes a versatile strategy for constructing multicomponent materials with high compositional and spatial precision.

Chen, Linfeng [Univ. of California, San Diego, CA ↗

DOE OSTI2025

Reduced basis approximations of parameterized dynamical partial differential equations via neural networks

Projection-based reduced order models are effective at approximating parameter-dependent differential equations that are parametrically separable. When parametric separability is not satisfied, which occurs in both linear and nonlinear problems, projection-based methods fail to adequately reduce the computational complexity. Devising alternative reduced order models is crucial for obtaining efficient and accurate approximations to expensive high-fidelity models. In this work, we develop a timestepping procedure for dynamical parameter-dependent problems, in which a neural-network is trained to propagate the coefficients of a reduced basis expansion. This results in an online stage with a computational cost independent of the size of the underlying problem. Here, we demonstrate our method on several parabolic partial differential equations, including a problem that is not parametrically separable.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Assessing the Application of a Genomic Network Analysis in Population Ecology: Inferring Patterns of Dispersal and Geographic Structure in the Emerging Pathogen, Coccidioides

A challenge in population ecology studies is identifying how to best group individuals into populations, especially when individual origin is unknown. Machine learning has improved upon traditional methods of identifying population structure and is more efficient at handling large, complex datasets. We demonstrate the applicability of a machine learning method to identify hierarchical population structure in an emerging pathogen, Coccidioides spp., the causative agent of Valley fever. We compared the network clusters to structure identified by traditional tools as a validation of the network performance. We used publicly available whole-genome data for 48 C. immitis and 102 C. posadasii, resulting in 168,211 genome-wide SNPs among the two species. The network analysis grouped samples into populations comparable to the literature for these species but also identified fine-scale geographic structure and travel-associated cases not reported thus far. Exploring different resolutions in the network made it easy to identify unique genotypes specific to California and possibly Nevada, as well as Phoenix- and Tucson-acquired infections in non-endemic areas, regardless of reported travel history. The present study provides a promising example of how a ML-based network analysis can improve our ability to understand pathogen ecology, group cases into populations and infer travel-associated infections.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Metastable Clusters and Competitive Solvation Tune Ion Pairing at Liquid Interfaces

The balance of hydrophobic and hydrophilic interactions underlies emergent phenomena in complex multicomponent chemical systems. Here, we show that a supposedly ‘non–interacting’ nonpolar phase can be used to competitively solvate amphiphilic molecules at an oil/aqueous interface. This solvation, as probed by surface specific nonlinear spectroscopy and simulations, results in a molecularly thin corrugated phase boundary featuring metastable assemblies that alter the hydrogen bonding networks of water and the apparent ‘hard/soft’ descriptors used to describe ionic interactions. We show that competitive solvation enhances amphiphile mobility, opening up otherwise energetically inaccessible complexes that transiently interact with aqueous phase ions. These transient species impact ensemble binding affinities and may represent the molecular agents responsible for aspects of ionic transport and function. In conclusion, the result of this work highlights how seemingly unrelated nonpolar interactions feedback onto aqueous phase chemical phenomena, providing a pathway to tune phase separation and self-assembly to access new reaction pathways using interfaces for a range of chemical and biological systems.

Anions↗

DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗

DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

5G↗