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AbstractManaging, processing, and sharing research data and experimental context produced on modern scientific instrumentation all present challenges to the materials research community. To address these issues, two MaRDA Working Groups on FAIR Data in Materials Microscopy Metadata and Materials Laboratory Information Management Systems (LIMS) convened and generated recommended best practices regarding data handling in the materials research community. Overall, the Microscopy Metadata Group recommends (1) instruments should capture comprehensive metadata about operators, specimens/samples, instrument conditions, and data formation; and (2) microscopy data and metadata should use standardized vocabularies and community standard identifiers. The LIMS Group produced the following guides and recommendations: (1) a cost and benefit comparison when implementing LIMS; (2) summaries of prerequisite requirements, capabilities, and roles of LIMS stakeholders; and (3) a review of metadata schemas and information-storage best practices in LIMS. Together, the groups hope these recommendations will accelerate breakthrough scientific discoveries via FAIR data. Impact statementWith the deluge of data produced in today’s materials research laboratories, it is critical that researchers stay abreast of developments in modern research data management, particularly as it relates to the international effort to make data more FAIR – findable, accessible, interoperable, and reusable. Most crucially, being able to responsibly share research data is a foundational means to increase progress on the materials research problems of high importance to science and society. Operational data management and accessibility are pivotal in accelerating innovation in materials science and engineering and to address mounting challenges facing our world, but the materials research community generally lags behind its cognate disciplines in these areas. To address this issue, the Materials Research Coordination Network (MaRCN) convened two working groups comprised of experts from across the materials data landscape in order to make recommendations to the community related to improvements in materials microscopy metadata standards and the use of Laboratory Information Management Systems (LIMS) in materials research. This manuscript contains a set of recommendations from the working groups and reflects the culmination of their 18-month efforts, with the hope of promoting discussion and reflection within the broader materials research community in these areas. Graphical abstract 

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility. 

ABSTRACT Bacterial biofilms are complex, multi‐component structures consisting primarily of four key elements: polysaccharides, metal ions, proteins, and extracellular DNA. In our research, we specifically focus on the polysaccharide and metal ion components, which play a crucial role in determining the biofilm's mechanical properties. Polysaccharides provide the structural matrix, although metal ions, particularly divalent cations like calcium and cobalt, cross‐link with the polysaccharides, thereby modulating the biofilm's rigidity and viscoelastic behavior. By introducing divalent cations into nanocellulose, we can replicate this natural cross‐linking process, allowing us to finely tune the material's mechanical properties to more closely resemble those of bacterial biofilms. This approach not only enhances the accuracy of synthetic biofilm models over alginate hydrogels but also provides valuable insights into how biofilms maintain their structural integrity in various environments. Our findings indicate that nanocellulose exhibits mechanical properties closer to biofilms than alginate analogs, making it a suitable non‐living control for biofilm studies. Furthermore, divalent nickel, followed by calcium and magnesium, demonstrate a closer mechanical mimicry to biofilms. In conclusion, this research shows the potential of nanocellulose as a versatile material for bacterial biofilm mimicry. 

Abstract Large-density functional theory (DFT) databases are a treasure trove of energies, forces, and stresses that can be used to train machine-learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor potentials (MTPs) for four carbide systems: CHfTa, CHfZr, CMoW, and CTaTi. The resulting MTPs are used to relax ~6300 random symmetric structures, and are subsequently improved via active learning to generate robust potentials (RP) that can relax a wide variety of structures, and accurate potentials (AP) designed for the relaxation of low-energy systems. This protocol is shown to yield convex hulls that are indistinguishable from those predicted by AFLOW for the CHfTa, CHfZr, and CTaTi systems, and in the case of the CMoW system to predict thermodynamically stable structures that are not found within AFLOW, highlighting the potential of the employed protocol within crystal structure prediction. Relaxation of over three hundred (Mo_1−xW_x)C stoichiometry crystals first with the RP then with the AP yields formation enthalpies that are in excellent agreement with those obtained via DFT. 

Abstract Hypersonic vehicles must withstand extreme conditions during flights that exceed five times the speed of sound. These systems have the potential to facilitate rapid access to space, bolster defense capabilities, and create a new paradigm for transcontinental earth-to-earth travel. However, extreme aerothermal environments create significant challenges for vehicle materials and structures. This work addresses the critical need to develop resilient refractory alloys, composites, and ceramics. We will highlight key design principles for critical vehicle areas such as primary structures, thermal protection, and propulsion systems; the role of theory and computation; and strategies for advancing laboratory-scale materials to manufacturable flight-ready components. 

Abstract Many experimental and computational efforts have sought to understand DNA origami folding, but the time and length scales of this process pose significant challenges. Here, we present a mesoscopic model that uses a switchable force field to capture the behavior of single- and double-stranded DNA motifs and transitions between them, allowing us to simulate the folding of DNA origami up to several kilobases in size. Brownian dynamics simulations of small structures reveal a hierarchical folding process involving zipping into a partially folded precursor followed by crystallization into the final structure. We elucidate the effects of various design choices on folding order and kinetics. Larger structures are found to exhibit heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to more accessible structures which exhibit first-order kinetics and virtually defect-free folding. This model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance. 

Abstract The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics^1–3. Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor⁴, most innovation has been slowly driven by experimental means^1–3. Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy–entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries. 

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Abstract Most elastomers undergo strain‐induced crystallization (SIC) under tension; as individual chains are held rigidly in a fixed position by an applied strain, their alignment along the strain field results in a shift from strain‐hardening (SH) to SIC. A similar degree of stretching is associated with the tension necessary to accelerate mechanically coupled, covalent chemical responses of mechanophores in overstretched chains, raising the possibility of an interplay between the macroscopic response of SIC and the molecular response of mechanophore activation. Here, thiol‐yne‐derived stereoelastomers doped covalently with a dipropiolate‐derivatized spiropyran (SP) mechanophore (0.25–0.38 mol%) are reported. The material properties of SP‐containing films are consistent with undoped controls, indicating that the SP is a reporter of the mechanical state of the polymer. Uniaxial tensile tests reveal correlations between mechanochromism and SIC, which are strain‐rate‐dependent. When mechanochromic films are stretched slowly to the point of mechanophore activation, the covalently tethered mechanophore remains trapped in a force‐activated state, even after the applied stress is removed. Mechanophore reversion kinetics correlate with the applied strain rate, resulting in highly tunable decoloration rates. Because these polymers are not covalently crosslinked, they are recyclable by melt‐pressing into new films, increasing their potential range of strain‐sensing, morphology‐sensing, and shape‐memory applications. 

Abstract Physically transient forms of electronics enable unique classes of technologies, ranging from biomedical implants that disappear through processes of bioresorption after serving a clinical need to internet-of-things devices that harmlessly dissolve into the environment following a relevant period of use. Here, we develop a sustainable manufacturing pathway, based on ultrafast pulsed laser ablation, that can support high-volume, cost-effective manipulation of a diverse collection of organic and inorganic materials, each designed to degrade by hydrolysis or enzymatic activity, into patterned, multi-layered architectures with high resolution and accurate overlay registration. The technology can operate in patterning, thinning and/or cutting modes with (ultra)thin eco/bioresorbable materials of different types of semiconductors, dielectrics, and conductors on flexible substrates. Component-level demonstrations span passive and active devices, including diodes and field-effect transistors. Patterning these devices into interconnected layouts yields functional systems, as illustrated in examples that range from wireless implants as monitors of neural and cardiac activity, to thermal probes of microvascular flow, and multi-electrode arrays for biopotential sensing. These advances create important processing options for eco/bioresorbable materials and associated electronic systems, with immediate applicability across nearly all types of bioelectronic studies.