From Equations to Discovery: AI-Driven Parameter Space Navigation in Materials Science & Process Engineering
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organic↗
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From Equations to Discovery: AI-Driven Parameter Space Navigation in Materials Science & Process Engineering
Explore the source record for details and available documents.
organic↗
Achieving ultrahigh modulus of resilience and enhanced thermal stability in ZnO x /SU-8 interpenetrating network polymer nanocomposite nanopillars
The modulus of resilience, a mechanical property that quantifies the maximum strain energy density a material can store during elastic deformation, is a crucial parameter for materials used in flexible displays, micro/nano-electro-mechanical system (M/NEMS) actuators, and ultra-sensitive pressure sensors. In this study, ZnO x /SU-8 nanocomposite nanopillars with a diameter of 300 nm, fully infiltrated with a uniformly distributed, interpenetrating amorphous ZnO x filler network, were synthesized via vapor-phase infiltration (VPI). In-situ uniaxial nano-compression tests revealed that the modulus of resilience of ZnO x /SU-8 reaches ∼ 12 MJ/m 3 , which is an ultrahigh value among all engineering materials with comparable strength. In addition, the synthesis fidelity, inorganic infiltration depth, and mechanical performance were all significantly improved compared to VPI-synthesized AlO x nanocomposites. Thermal stability, another key requirement for M/NEMS device materials operating under extreme environments, was also notably enhanced. Furthermore, partial crystallization of the amorphous ZnO x fillers during annealing contributed to an additional increase in modulus of resilience, reaching up to ∼ 13.9 MJ/m 3 . This work presents an effective fabrication strategy for producing nanostructured organic–inorganic hybrid nanocomposites with ultrahigh modulus of resilience and superior thermal stability, paving the way for their integration into next-generation flexible displays and high-performance M/NEMS devices working under harsh environments.
36 MATERIALS SCIENCE↗
MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning
Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.
Tabassum, Anika↗
Performance Limits of an Alternating Current Electroluminescent Device
Abstract The use of an alternating current (AC) voltage is a simple, versatile method of producing electroluminescence from generic emissive materials without the need for contact engineering. Recently, it was shown that AC‐driven, capacitive electroluminescent devices with carbon nanotube network contacts can be used to generate and study electroluminescence from a variety of molecular materials emitting in the infrared‐to‐ultraviolet range. Here, performance trade‐offs in these devices are studied through comprehensive device simulations and illustrative experiments, enhancing understanding of the mechanism and capability of electroluminescent devices based on alternating as opposed to direct current (DC) schemes. AC‐driven electroluminescent devices can overcome several limitations of conventional DC‐driven electroluminescent devices, including the requirement for proper alignment of material energy levels and the need to process emitting materials into uniform thin films. By simultaneously optimizing device geometry, driving parameters, and material characteristics, the performance of these devices can be tuned. Importantly, the turn‐on voltage of AC‐driven electroluminescent devices approaches the bandgap of the emitting material as the gate oxide thickness is scaled, and internally efficient electroluminescence can be achieved using low‐mobility single‐layer emitter films with varying thicknesses and energy barrier heights relative to the contact.
Wang, Vivian↗
Synthesis and Characterization of Carbon Microbeads
We report a microfluidic-based droplet generation platform for synthesizing micron-sized porous carbon microspheres. The setup employs carbon materials such as graphite, carbon nanotubes, graphene, fullerenes, and carbon black as starting materials. Custom composition, structure, and function are achieved through combinations of carbon materials, cross-linkers, and additives along with variations in process parameters. Carbon materials can be assembled into spheres with a mean diameter of units to hundreds of μm with relatively tight size distribution (<25% RSD). Pore structure and size (tens to hundreds of angstrom) can be modulated by incorporating porogen/coporogen dilutants during synthesis. The microbeads have excellent mechanical stability with an elastic modulus of hundreds of MPa. They can sustain high dynamic fluid flow pressures of up to 9000 psi. This work lays the foundation for synthesizing novel tailorable and customizable carbon microbeads. It opens avenues for applying these novel materials for composite and additive manufacturing, energy, life science, and biomedical applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning
Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.
Tabassum, Anika↗
Models and Algorithms for Equilibrium Analysis of Mixed-Material Nucleic Acid Systems
Dynamic programming algorithms within the NUPACK software suite enable analysis of equilibrium base-pairing properties for complex and test tube ensembles containing arbitrary numbers of interacting nucleic acid strands. Currently, calculations are limited to single-material systems that are either all-RNA or all-DNA. Here, to enable analysis of mixed-material systems that are critical for modern applications in vitro, in situ, and in vivo, we develop physical models and dynamic programming algorithms that allow the material of the system to be specified at nucleotide resolution. Free energy parameter sets are constructed for both RNA/DNA and RNA/2'OMe-RNA mixed-material systems by combining available empirical mixed-material parameters with single-material parameter sets to enable treatment of the full complex and test tube ensembles. New dynamic programming recursions account for the material of each nucleotide throughout the recursive process. For a complex with N nucleotides, the mixed-material dynamic programming algorithms maintain the O(N 3 ) time complexity of the single-material algorithms, enabling efficient calculation of diverse physical quantities over complex and test tube ensembles (e.g., complex partition function, equilibrium complex concentrations, equilibrium base-pairing probabilities, minimum free energy secondary structure(s), and Boltzmann-sampled secondary structures) at a cost increase of roughly 2.0-3.5×. The results of existing single-material algorithms are exactly reproduced when applying the new mixed-material algorithms to single-material systems. Accuracy is significantly enhanced using mixed-material models and algorithms to predict RNA/DNA and RNA/2'OMe-RNA duplex melting temperatures from the experimental literature as well as RNA/DNA melt profiles from new experiments. In conclusion, mixed-material analyses can be performed online using the NUPACK web app (www.nupack.org) or locally using the NUPACK Python module.
2′OMe-RNA↗
Scripting ToolKit (STK) 2.0 for Granta Database: Model Calibration Examples [Slides]
Multiple stress cushion (SX-358) uniaxial compression tests are performed. There are 640 (= 4 cycles x 160 tests) compression test curves coming to Granta. Find CHIP-Foam material model parameters. Efficient method is needed to process test data to calibrate custom material models.
97 MATHEMATICS AND COMPUTING↗
Deuterium retention in Be-D co-deposits formed over an ITER relevant parameter space
Be-D co-deposit samples are produced over an extended range of plasma-material interaction parameters reflective of the conditions expected in ITER deposition dominant locations. Deposition temperature, D impact energy, atomic Be deposition rate, and D2 neutral pressure were varied in the ranges 373–623 K, ~2–100 eV, 1–10 × 10 19 m 2 s –1 , and 0.27–13.3 Pa respectively. Thermal desorption spectrometry was carried out on samples, subsequently, to measure the D release profile and retention. In this work, it was found that the D retention in samples agrees well with the existing GDT D/Be scaling over the extended parameter space, and that increased D impact energy and D2 pressure can lead to the observation of a sharp low-temperature release peak at ~440 K in desorption data. Desorption data without the presence of the sharp release mechanism are modeled with TMAP-7 utilizing three traps of de-trapping energies, 0.8, 0.9 and 1.0 eV.
36 MATERIALS SCIENCE↗
A mechanistic interpretation of Nelson curves for PVP failures under high temperature hydrogen attack
As an empirically established design criterion, Nelson curves that relate the service temperature and the allowable hydrogen partial pressure have been developed and utilized for more than sixty years in pressure vessels and piping (PVP) safety design. Despite a relatively clear thermodynamic understanding of the high-temperature-hydrogen-attack (HTHA) problem, the detailed fracture process on the microstructural length scales, however, remains elusive, and a quantitative assessment of the PVP lifetime under HTHA from the available creep fracture dataset is still not possible. This work develops a microstructure-informed and micromechanics-based model by incorporating a synergy between hydrogen transport and intergranular-cavity-based fracture process. Based on the available creep lifetime data of C-0.5Mo steels, we are able to calibrate material constitutive parameters, and then conduct nonlinear finite element simulations that reveal a real-time stress-induced hydrogen diffusional transport along grain boundaries, coupled with a microstructure-explicit failure process, from which Nelson curves can be computed. Such failure analyses allow us to delineate two distinct regimes on the Nelson curves, i.e., dislocation-creep-controlled or grain boundary diffusion-assisted cavity growth. More importantly, we found that a small change of the pipe thickness and applied stresses can significantly shift these lifetime curves. However, these two parameters are usually not provided in Nelson curves, thus limiting their usage in material selection and safety design. In conclusion, this discrepancy can clearly be mitigated by extensive parametric studies from our micromechanical modeling/simulation framework.
36 MATERIALS SCIENCE↗
ASME Section III, Division 5, High Temperature Reactors
Section III, Division 5 stands ready to support near-term deployment of advanced reactors. Progress on improving design rules, extending design lifetimes, adding more materials, and adding advanced component fabrication methods. Lessons-learned from Alloy 617 Code Case effort have streamlined the balloting workflow for Class A material code cases. After requester submitted material design parameters and supporting data package to ASME, Division 5 could turn around a material code case in about three Code Week cycles (less than a year). Data requirements for new materials are described in Division 5, “Nonmandatory Appendix HBB-Y, Guidelines For Design Data Needs For New Materials”.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Machine learning-driven design and self-sensing capabilities of automotive bumper lattices for adaptive impact response
We present a novel approach to design an automotive bumper energy absorber using carbon fiber reinforced polymer composites, optimized to meet conflicting performance requirements for two distinct impact scenarios. The design must satisfy both a low-speed (2.5 mph) pendulum intrusion test, simulating vehicle-to-vehicle collisions, and a high-speed (25 mph) leg flexion test, replicating pedestrian impacts. These tests demand opposing deformation characteristics: high flexibility (deformation < 85 mm) for the former and high stiffness (deformation < 22 mm) for the latter. To address these contradictory requirements, we developed a machine learning (ML) framework for inverse optimization of lattice designs and material selection. Unlike traditional iterative design processes, our ML model directly outputs optimal design parameters and material choices based on target performance inputs. The energy absorber was fabricated using advanced additive manufacturing techniques, including extrusion deposition and digital light processing. The integration of carbon fibers provides multifunctionality to the bumper structure, enabling self-sensing capabilities through changes in electrical resistivity under compression. This electrical response demonstrates high repeatability under multiple cycles at 2% compression and exhibits distinct signatures during crack formation under high deformation. This research offers adaptive performance through innovative design methodologies and smart material integration. The approach has potential applications in various fields requiring adaptive energy absorption and real-time structural health monitoring.
Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
Leverage modern artificial intelligence (AI) enabled systems for waste reduction
Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Learning robust parameter inference and density reconstruction in flyer plate impact experiments
Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.
97 MATHEMATICS AND COMPUTING↗
Trends in Carbon, Oxygen, and Nitrogen Core in the X-ray Absorption Spectroscopy of Carbon Nanomaterials: A Guide for the Perplexed
Successful deployment of carbon nanomaterials in many applications, such as sensing, energy storage, and catalysis, relies on the selection, synthesis, and tailoring of the surface properties. Predictive analysis of the behavior is difficult without detailed knowledge of the differences between various carbon nanomaterials and their surface functionalization, thus leaving the selection process to traditional trial-and-error work. The present characterization fills this knowledge gap for carbon nanomaterial surface properties with respect to chemical states and functionalization. We present an overview of the chemical trends that can be extracted from soft X-ray absorption spectroscopy (XAS) spectra on an extended set of nonideal carbon nanomaterials as a function of sp 2 bonded carbon and bond ordering. In particular, the surface chemical state, the presence of long-range order in the carbon matrix, and a qualitative estimation of the amount of oxygen and nitrogen and their respective functional group formation on the material surface, together with the detailed material fabrication parameters, are reported. The results expand our understanding of carbon nanomaterial functionalization, which can support material selection in practice, provided that the specifications of the application are known.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Cyclic Ramberg–Osgood Parameters of 316SS Weld Metal Under In-Air and PWR-Water Environments
We report at present, the available Ramberg–Osgood (R–O) parameters for different metals (e.g., in ASME code and other literatures) are static (generally based on a tensile curve). These static R–O parameters cannot accurately model the cyclic plasticity behavior. This work presents the cyclic R–O material hardening parameters for 316 stainless steel similar metal welds. The parameters were estimated under various conditions (in-air at room temperature, 300 °C in-air, and in-air at primary water conditions for a pressurized water reactor (PWR)). It is anticipated that the reported results would be useful for computational mechanics-based shakedown analysis and fatigue life estimation of PWR components.
42 ENGINEERING↗
Rare-Earth Permanent Magnet SmCo 5 for Chiral Interfacial Spin-Orbitronics
Interfacially asymmetric magnetic multilayers made of heavy metal/ferromagnet have attracted considerable attention in the spintronics community for accommodating spin-orbit torques (SOTs) and meanwhile for hosting chiral spin textures. In these multilayers, the accompanied interfacial Dzyaloshinskii–Moriya interaction (iDMI) permits the formation of Néel-type spin textures. While significant progresses have been made in Co, CoFeB, Co 2 FeAl, CoFeGd based multilayers, it would be intriguing to identify new magnetic multilayers that could enable spin-torque controllability and meanwhile host nanoscale skyrmions. In this report, first, thin films made of permanent magnet SmCo 5 with perpendicular magnetic anisotropy are synthesized, in which the deterministic SOT switching, enabled by the spin Hall effect, in Pt/SmCo 5 /Ta trilayer is demonstrated. Further, the stabilization of room-temperature skyrmions with diameters ≈100 nm in [Pt/SmCo 5 /Ta] 15 , together with a skyrmionium-like spin texture in [Pt/SmCo 5 /Ir] 15 multilayers is shown. Based on the material specific parameters, micromagnetic simulations are also carried out. The results confirm the presence of chiral spin textures in this new material family. Through interfacial engineering, the results thus demonstrate that rare earth permanent magnets could be a new platform for studying interfacial chiral spintronics.
36 MATERIALS SCIENCE↗