Design Space Research Articles

Reactive vapor-phase dealloying-alloying turns oxides into sustainable bulk nano-structured porous alloys.

For millennia, alloying has been the greatest gift from metallurgy to humankind: a process of mixing elements, propelling our society from the Bronze Age to the Space Age. Dealloying, by contrast, acts like a penalty: a corrosive counteracting process of selectively removing elements from alloys or compounds, degrading their structural integrity over time. We show that when these two opposite metallurgical processes meet in a reactive vapor environment, profound sustainable alloy design opportunities become accessible, enabling bulk nanostructured porous alloys directly from oxides, with zero carbon footprint. We introduce thermodynamically well-grounded treasure maps that turn the intuitive opposition between alloying and dealloying into harmony, facilitating a quantitative approach to navigate synthesis in such an immense design space. We demonstrate this alloy design paradigm by synthesizing nanostructured Fe-Ni-N porous martensitic alloys fully from oxides in a single solid-state process step and substantiating the critical kinetic processes responsible for the desired microstructure.

Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions

Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and ethics. There is an urgent need for action in cities, particularly to (i) enhance the health and wellbeing of urban residents while ensuring inclusivity in urban development (e.g., through the intelligent design of public spaces, mobility, and transportation) and (ii) improve resilience and sustainability (e.g., through better disaster management, planning of city logistics, and waste management). This paper aims to explore how neuroscientific and neurotechnological solutions can contribute to the development of smart cities, as experts in various fields underline that real-time sensing designs and control algorithms inspired by the brain could help build and plan urban systems that are healthy, safe, inclusive, and resilient. Motivated by the potential interplay between societal challenges and these emerging technologies, we provide an overview of state-of-the-art research through a bibliometric analysis of neurochallenges within the context of smart cities using terms and data extracted from the Scopus database between 2018 and 2022. The results indicate that smart city research remains fragmented and technology-driven, relying heavily on internet of things (IoT) and artificial intelligence (AI)-based technologies. Mostly, it also lacks careful integration and adoption tailored to societal goals and human-centric concerns. In this context, the article explores key research streams and discusses how to create new synergies and complementarities in the challenge-technology intersection. We conclude that realizing the vision of smart cities at the nexus of neuroscience, technology, urban space, and society requires more than just technological progress. Integrating the human dimension alongside various technological tools and systems is crucial. This necessitates better interdisciplinary collaboration and co-production of knowledge toward a hybrid intelligence, where synergies of education and research, technological innovation, and societal innovation are genuinely built. We hope the insights from this analysis will help orient neurotechnological interventions on urban living and ensure they are more responsive to societal and environmental challenges as well as to legal and ethical concerns.

High-Efficiency and Ultrawideband Polarization Conversion Metasurface Based on Topology and Shape Optimizaiton Design Method

This study introduces a two-stage optimization method for designing an ultrawideband polarization conversion metasurface. By integrating topology and shape optimization techniques, the proposed method expands the design space to achieve enhanced polarization conversion bandwidth. The first stage employs genetic-algorithm-based topology optimization to establish the initial structural configuration through binary coding. Subsequently, the second stage refines the design through shape optimization by extracting and modifying the boundaries of the topology-optimized structure. The optimized design demonstrates high polarization conversion efficiency (>90%) across 4.08–14.39 GHz, yielding a relative bandwidth of 111.64%, which represents a 4.88% improvement compared to topology-only optimization. This enhancement demonstrates the effectiveness of our combined optimization method in ultrawideband polarization conversion metasurface design, offering a promising method for developing high-performance electromagnetic devices.

Fabrication-aware inverse design with shape optimization for photonic integrated circuits

Inverse design (ID) is a computational method that systematically explores a design space to find optimal device geometries based on specific performance criteria. In silicon photonics, ID often generates design features that degrade significantly due to the fabrication process, limiting the applicability of these devices in scalable fabrication. We demonstrate a solution to this performance degradation through fabrication-aware inverse design (FAID), integrating lithography models for deep-ultraviolet (DUV) lithography and electron-beam lithography (EBL) into the shape optimization approach of ID. A Y-branch and a subwavelength grating (SWG)-to-strip converter were generated and fabricated with this new, to the best of our knowledge, approach. Simulation and measurement results verify that FAID yields devices with up to 0.7 dB lower insertion loss per device. The modified workflow enables designers to use ID to generate devices that adjust for process bias predicted by lithography models.

Fast Design Space Exploration for Always-On Neural Networks

An analytical model can quickly predict performance and energy efficiency based on information about the neural network model and neural accelerator architecture, making it ideal for rapid pre-synthesis design space exploration. This paper proposes a new analytical model specifically targeted for convolutional neural networks used in always-on applications. To validate the proposed model, the performance and energy efficiency estimated by the model were compared with actual hardware and post-synthesis gate-level simulations of hardware synthesized with a state-of-the-art electronic design automation (EDA) synthesis tool. Comparisons with hardware created for the Eyeriss neural accelerator showed average execution time and energy consumption error rates of 3.33% and 13.54%, respectively. Comparisons with hardware synthesis results showed an error of 3.18% to 9.44% for two example neural accelerator configurations used to execute MobileNet, EfficientNet, and DarkNet neural network models. Finally, the utility of the proposed model was demonstrated by using it to evaluate the effects of different channel sizes, pruning rates, and batch sizes in several neural network designs for always-on vision, text, and audio processing.

Latent Thermal Energy Storage for Cooling Demands in Battery Electric Vehicles: Development of a Dimensionless Model for the Identification of Effective Heat-Transferring Structures

Thermal energy storage (TES) systems open up alternative paths for air conditioning to increase the range of battery electric vehicles (BEVs) by reducing power consumption. The central prerequisites for this purpose are high storage densities: high-temperature TES systems are being focused on for heat demands, while effective solutions for cooling are missing. Due to their lower temperature potentials, concepts with high storage capacities and heat transports between the storage and cold transferring medium are needed. Latent TES systems based on water enable these capacities but require adequate internal structures for effective heat transfer. Due to the large number of geometric options, high simulation efforts must be conducted to identify favored structures, or the possible design space must be limited for investigations. For this purpose and for the first time, an alternative way is presented using newly developed dimensionless models in a top-down methodology for time-efficient design studies and evaluations. These models were successfully validated and used as a design tool to identify effective structures in latent TES systems for cooling demands in BEVs. A wide array of variation studies on tube, finned plate and novel Triply Periodic Minimal Surface (TPMS) structures were performed and uniformly evaluated with regard to storage densities, cooling efficiencies and geometry. The results show high storage densities for novel TPMS structures, including the enclosure of 100 Wh/kg or 102.2 kWh/m3 with average cooling capacities of 1 kW over 30 min, confirming the usability of latent TES systems in terms of compactness and efficiency for cooling demands in BEVs.

How Do Particles with Complex Interactions Self-Assemble?

In living cells, proteins self-assemble into large functional structures based on specific interactions between molecularly complex patches. Because of this complexity, protein self-assembly results from a competition between a large number of distinct interaction energies, of the order of one per pair of patches. However, current self-assembly models typically ignore this aspect, and the principles by which it determines the large-scale structure of protein assemblies are largely unknown. Here, we use Monte Carlo simulations and machine learning to start to unravel these principles. We observe that despite widespread geometrical frustration, aggregates of particles with complex interactions fall within only a few categories that often display high degrees of spatial order, including crystals, fibers, and oligomers. We then successfully identify the most relevant aspect of the interaction complexity in predicting these outcomes, namely, the particles’ ability to form periodic structures. Our results provide a first extensive characterization of the rich design space associated with identical particles with complex interactions and could inspire engineered self-assembling nano-objects as well as help us to understand the emergence of robust functional protein structures. Published by the American Physical Society 2024

Roads are partial barriers to foraging solitary bees in an urban landscape.

Understanding how animals navigate novel heterogeneous landscapes is key to predicting species responses to land-use change. Roads are pervasive features of human-altered landscapes, known to alter movement patterns and habitat connectivity of vertebrates like small mammals and amphibians. However, less is known about how roads influence movement of insects, a knowledge gap that is especially glaring in light of recent investments in habitat plantings for insect pollinators along roads verges and medians. In this study, we experimentally investigate behavioral avoidance of roads by a solitary bee and explore whether landscape factors are associated with bee movement in urban Massachusetts, USA. Using mark-recapture surveys, we tracked individual solitary bee (Agapostemon virescens) foraging movements among floral patches separated by roads or grass lawn. We found that roads acted as partial barriers to movements of foraging bees, with road crossings nearly half as likely as along-road movements (36% vs. 64%). Movement probabilities were negatively associated with distance and the proportion of roadway between patches, and positively associated with higher floral resource density at the destination patch. Importantly, our findings also suggest that while roads impede bee movement, they are not complete barriers to dispersal of bees and/or transfer of pollen in urban landscapes. In the context of green space design, our findings suggest that prioritizing contiguous habitat and ensuring higher floral densities along road edges may enhance resource access for pollinators and mitigate the risk of ecological traps.

UNDERSTANDING THE MULTI-FUNCTIONAL NATURE OF PUBLIC OPEN SPACE, A CASE STUDY AT KLCC PARK, KUALA LUMPUR, MALAYSIA

Public open spaces are essential in urban areas, performing various functions and services that benefit individuals and communities. This paper examines the multi-functionality of his KLCC Park public open space in Kuala Lumpur, Malaysia. KLCC Park is a vast, well-kept park in the heart of Kuala Lumpur. Recreational facilities within the park include playgrounds, jogging trails, and lakes. Picnics, family gatherings and cultural activities are also common here. This study aims to delve into the multi-functional nature of public open spaces and elucidate the importance of nature in creating a harmonious relationship between humans and their environment. The main objective is to investigate the relationship between public open space and the muti-functionality of nature; the second is to comprehend nature’s significance in public open spaces and its impact on human well-being; and finally, the third objective is to suggest the incorporation of nature elements in the design and planning of public open spaces to optimise their functionality and enhancing user’s overall satisfaction. The research results will help develop a more evidence-based urban planning and design approach. The article argues that public spaces should be constructed and managed to benefit individuals and communities maximum. Additionally, this contribution emphasises the importance of incorporating natural elements into the design and planning of public open spaces.

Deep Learning-Assisted Design of Novel Donor-Acceptor Combinations for Organic Photovoltaic Materials with Enhanced Efficiency.

Designing donor (D) and acceptor (A) structures and discovering promising D-A combinations can effectively improve organic photovoltaic (OPV) device performance. However, to obtain excellent power conversion efficiency (PCE), the trial-and-error structural design in the infinite chemical space is time-consuming and costly. Herein, a deep learning (DL)-assisted design framework for OPV materials is proposed. To effectively digitally represent the D and A structures, a structure representation method, polymer fingerprints, is developed, and a database of OPV materials is constructed. By applying an end-to-end graph neural network modeling method, high-precision DL models for predicting OPV performance are established. After combining the existing structures, ≈0.6 million virtual D-A combinations are generated. Then, the OPV performance of these candidate combinations is predicted by the well-trained models, and numbers of novel D-A combinations with high efficiency are identified. Experimental validations confirm that the prediction accuracy is greater than 93% and one of the screened combinations (i.e., D18:BTP-S11) exhibits an efficiency above 19.3% in single-junction organic solar cells. Finally, based on the structural gene analysis, the design rules to guide experimental explorations are suggested. The developed DL-assisted approach can accelerate the design of D-A combinations with ultrahigh efficiency and bring property breakthroughs for OPV devices.

Enantiospecific Synthesis of Planar Chiral Rhodium and Iridium Cyclopentadienyl Complexes: Enabling Streamlined and Computer-Guided Access to Highly Selective Catalysts for Asymmetric C-H Functionalizations.

Chiral cyclopentadienyl (CpX) metal complexes are frequently used in asymmetric catalysis by virtue of their high reactivity and selectivity. Planar-chiral-only rhodium and iridium cyclopentadienyl complexes are particularly promising due to unrestricted chemical space for CpX ligand design while retaining structural simplicity. However, they are currently still niche because of a lack of efficient synthetic strategies that avoid lengthy chiral auxiliary routes or chiral preparatory HPLC resolution of the complexes. To streamline access to such planar-chiral-only CpX-metal complexes, we designed a straightforward, highly enantiospecific, point-to-planar chirality transfer complexation via facially selective concerted-metalation-deprotonation between metal-carboxylate precursor [M(olefin)2OAc]2 and a chiral cyclopentadiene. This entirely avoids the typical stereoablative complexation of an achiral cyclopentadienyl anion that detrimentally yields a racemate. Exploiting the described enantiospecific complexation protocol and a simple divergent synthetic route to suitable chiral cyclopentadienes, we generated a structurally diverse library of new planar chiral Cp-Rh(I), Cp-Ir(I), Cp-Rh(III), and Cp-Ir(III) complexes. Moreover, the enantiospecific complexation step can be concatenated with a preceding Au-catalyzed cyclization in an efficient one-pot process that likely involves an elaborate point-to-axial-to-point-to-planar chirality transfer. Guided by computational selectivity predictions, the structure of a CpX-Rh complex in our library was tuned to optimize reactivity and selectivity in the asymmetric C-H functionalization of a benzamide with various challenging alkenes. With an optimized CpX-Rh complex in hand, we showcased its excellent catalytic performance and high selectivity for refractory alkene substrates that reacted in poor selectivity with previous CpX-Rh catalysts.

Reinforcement-Learning-Based Synthesis of Custom Approximate Parallel Prefix Adders

Approximate hardware units compute a sufficiently accurate result rather than a fully accurate one using fewer transistors or equivalently logic gates than their accurate counterparts. This approach significantly saves energy while maintaining acceptable application-level quality. In this work, we focus on synthesizing custom approximate parallel prefix adders tailored to specific applications. The introduced reinforcement learning (RL) framework co-optimizes application performance and hardware complexity. An RL agent learns approximate addition strategies by exploring the entire design space and receiving feedback from hardware synthesis and application performance. Experimental results demonstrate that the synthesized adders can reduce area and power consumption by 12% and 10%, respectively, on average without compromising the error behavior of the application, or can significantly improve the application’s error metrics without degrading area and power consumption for a variety of practical applications.

PIMCoSim: Hardware/Software Co-Simulator for Exploring Processing-in-Memory Architectures

As the scope of artificial intelligence (AI) expands and the structure becomes more complex, the amount of data for inference and training has increased. In traditional computer architectures, the memory bandwidth limitations have intensified bottlenecks in AI systems, and processing-in-memory (PIM) architectures have been proposed to overcome this issue. PIM is an architecture that performs computations within memory, thereby reducing data movement between the CPU and memory. However, since PIM is difficult to optimize as a general-purpose architecture, it is essential to adopt an architecture suitable for the target application. While various simulators and emulators have been introduced for the design space exploration (DSE) of different PIM architectures, simulators are limited in debugging hardware operations, and emulators face challenges in flexibly modifying the system configuration, as emulators implement the entire architecture in hardware. Therefore, this paper introduces PIMCoSim, a comprehensive hardware–software co-simulator for the DSE of DRAM-PIM systems. This co-simulator partially emulates simplified hardware-implemented processing elements (PEs) and integrates software models for memory operations, facilitating the DSE of PIM systems. To validate PIMCoSim, we analyzed results for different computational workloads by varying PIM structures and operational policies, demonstrating the efficiency of DRAM-PIM systems. The co-simulation approach in PIMCoSim aims to contribute to analyzing DRAM-PIM configurations and adopting optimized structures.

Outdoor thermal comfort in face of the urban heat island effect: innovative solutions for cooler cities; a case study in Souk Ahras city

This study investigates the relationship between the urban heat island effect and outdoor thermal comfort through a bibliometric analysis of relevant keywords, highlighting their strong connection. The analysis highlights how trees improve urban microclimates and enhance outdoor comfort by integrating them into public space design. The research in Souk Ahras city investigates two public spaces, using in situ measurements and the Rayman model to analyze thermal indices (SVF, Tmrt, and PET) based on climatic data and urban geometry collected on a hot summer day. The findings show that trees at each intervention site reduce average daily air temperature by 0.43°C to 1.043°C, lower mean radiant temperature (Tmrt) by 1°C to 1.2°C, and decrease physiological equivalent temperature (PET) by 0.16°C to 1.58°C. Trees also reduce the sky view factor, highlighting their role in improving outdoor thermal comfort through shading and evapotranspiration. These results confirm that integrating trees into public space design effectively enhances outdoor thermal comfort and urban microclimates during warmer seasons.

A Context-based Design Toolkit (CoDeT) for Socially Assistive Robots (SARs): a methodological approach

ABSTRACT This paper presents the Context-based Design Toolkit (CoDeT) methodology – a practical toolkit designed to facilitate collaborative design and evaluation of Socially Assistive Robots (SARs). CoDeT elicits user and other stakeholder needs, perceptions, and preferences. The methodology for creating a CoDeT is three-phased. The first phase is the Contextual space in which the designer generates a use-case-specific toolkit. The second phase is the Investigation space, which refers to utilising the toolkit among different users and stakeholders. The third phase is the Design Space, which returns to the design team to analyse the outcomes and define design guidelines. Rather than an exhaustive research investigation, our work provides guidelines and illustrative examples for using CoDeT effectively. Following the CoDeT methodology enables designers to gain insights regarding stakeholders’ and users’ perceptions and expectations of robotic roles and their most suitable appearances.

Harnessing quantum power: Revolutionizing materials design through advanced quantum computation

AbstractThe design of advanced materials for applications in areas of photovoltaics, energy storage, and structural engineering has made significant strides. However, the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing, fabrication, and characterization. This review introduces a comprehensive methodology for materials design using cutting‐edge quantum computing, with a particular focus on quadratic unconstrained binary optimization (QUBO) and quantum machine learning (QML). We introduce the loop framework for QUBO‐empowered materials design, including constructing high‐quality datasets that capture critical material properties, employing tailored computational methods for precise material modeling, developing advanced figures of merit to evaluate performance metrics, and utilizing quantum optimization algorithms to discover optimal materials. In addition, we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations. The review also highlights advanced active learning strategies that integrate quantum artificial intelligence, offering a more efficient pathway to explore the vast, complex material design space. Finally, we discuss the key challenges and future opportunities for QML in material design, emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.

A Simulation Method for Fireproof Space Design in Aviation Airport Terminals

A Simulation Method for Fireproof Space Design in Aviation Airport Terminals

Physics and technology maturity level required for the K-DEMO design points

The conceptual design study for the Korean fusion demonstration reactor (K-DEMO) has been conducted, placing an emphasis on the imperative to reduce the reactor's dimension as a means to cost minimization. The design space of K-DEMO was delineated based on maturity level of physics and technology. Advanced technology features such as a use of a tungsten carbide (WC) shield, a tritium breeding blanket concept of He cooled lithium lead (HCLL), the maximum allowable magnetic field at the TF coil, Bmax = 16 T with a Nb3Sn superconducting material, etc. were adopted. With specified design criteria, including a net electric power ≥ 300 MW, a fusion gain, Q > 20.0, a neutron wall loading < 2.0 MW/m2, an indicator of divertor power handling capability (ratio of power to divertor to major radius), Pdiv/R0 < 25 MW/m, and the capability for steady-state operation, a design space of the K-DEMO was established based on the energy confinement scaling law of IPB98[y,2] under physics level of Greenwald density fraction, ne/nG < 1.2, normalized plasma beta (ratio of plasma pressure to magnetic pressure normalized by plasma current divided by the product of minor radius and toroidal magnetic field), βN < 3.0, confinement enhancement factor, H < 1.3, and a direct cost ≤ 7.5 B$. After an exploration of system parameters, prospective design points for K-DEMO were identified, characterized by a major radius, R0 ∼ 6.8 m, an aspect ratio, A = 3.1, a toroidal magnetic field at plasma center, BT ≥ 6.5 T, and a fusion power, Pfusion ∼ 1,500 MW. When β-independent energy confinement scaling law was applied, the design points were accessible with smaller ne/nG, βN, H, Pfusion, and larger BT. From a sensitivity analysis of the minimum major radius to the input parameters, strong sensitivities to Greenwald density fraction, ne/nG, normalized plasma beta, βN, confinement enhancement factor, H, edge safety factor, qedge, and elongation, κ were found. Additionally, the operational envelope in physics and technology parameters was established with system parameters associated with the design points.

DF-BETA: An FPGA-based Memory Locality Aware Decision Forest Accelerator via Bit-Level Early Termination

Decision forests, particularly Gradient Boosted Decision Trees (GBDT), are popular due to their high prediction performance and computational efficiency, making them suitable for embedded systems with circuit size and available energy constraints. In this study, we propose a new lightweight GBDT inference acceleration mechanism through the hardware and algorithm co-design. First, we present LoADPack, a hardware-friendly GBDT algorithm that enhances memory access locality. LoADPack obtains trees where the features and thresholds used across the entire ensemble are regular regardless of a branching direction by unifying some nodes and aligning the memory access patterns. Furthermore, we present DF-BETA, a resource-efficient accelerator for the LoADPack algorithm. DF-BETA utilizes MSB-first bit-serial computation to enable early determination of comparison calculations of 32-bit floating-point numbers, optimizing the operation for determining a branch direction. The hardware complexity and computation termination speed vary with the granularity of bit-serial computation. Therefore, we conduct design space exploration of DF-BETA to identify the optimal configuration. Our findings reveal that using 4-bit-serial comparators minimizes circuit size while achieving the leading throughput. Compared to running unconstrained GBDT on a typical accelerator with 32-bit bit-parallel comparators, our accelerator achieves 1.6 times higher throughput on average while maintaining comparable accuracy.

Critical Lengths of Kitaev Chains for Majorana Zero Modes with a Microsecond Coherence Time and a Quantized Conductance Signature.

The problems of disorder and insufficient system length are generally regarded as central problems in the realization of Majorana zero modes (MZM), which are a promising platform for realizing fault-tolerant topological quantum computing (TQC). In this work, we analyze eigenenergy spectra and transport properties of finite Kitaev chains using quantum transport simulations in a wide design space of hopping amplitude (t), superconductor pairing (Δ), and electrochemical potential. Our goal is to determine critical or minimum acceptable chain lengths to obtain oscillation-free MZMs with suitable microsecond coherence times, and observable zero-bias conductance peaks (ZBCP) quantized almost at ~2e2/h. Due to qualitative equivalence of the Kitaev and Oreg-Lutchyn models, we approximately determine the foreseeable critical length of topological superconducting nanowires (TS NWs) as well. We find that the ZBCP length requirement is looser in comparison to the limit imposed by the coherence time. For a large t/Δ mismatch of ~40 corresponding to the experimental TS NWs, the first condition sets the minimum length to 344 sites (≈5.5 μm), while the second condition requires 605 sites (≈9.7 μm). The calculated lengths are far from the reported experimental hybrid device dimensions, explaining difficulties in observing MZMs in TS NWs fabricated so far. Nonetheless, a decreasing t/Δ mismatch allows for shorter systems, which argues in favor of the proximitized quantum dot path for MZMs in a solid-state system.