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14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

Tensile performance data of 3D printed photopolymer gyroid lattices.

Additive manufacturing has provided the ability to manufacture complex structures using a wide variety of materials and geometries. Structures such as triply periodic minimal surface (TPMS) lattices have been incorporated into products across many fields due to their unique combinations of mechanical, geometric, and physical properties. Yet, the near limitless possibility of combining geometry and material into these lattices leaves much to be discovered. This article provides a dataset of experimentally gathered tensile stress-strain curves and measured porosity values for 389 unique gyroid lattice structures manufactured using vat photopolymerization 3D printing. The lattice samples were printed from one of twenty different photopolymer materials available from either Formlabs, LOCTITE AM, or ETEC that range from strong and brittle to elastic and ductile and were printed on commercially available 3D printers, specifically the Formlabs Form2, Prusa SL1, and ETEC Envision One cDLM Mechanical. The stress-strain curves were recorded with an MTS Criterion C43.504 mechanical testing apparatus and following ASTM standards, and the void fraction or "porosity" of each lattice was measured using a calibrated scale. This data serves as a valuable resource for use in the development of novel printing materials and lattice geometries and provides insight into the influence of photopolymer material properties on the printability, geometric accuracy, and mechanical performance of 3D printed lattice structures. The data described in this article was used to train a machine learning model capable of predicting mechanical properties of 3D printed gyroid lattices based on the base mechanical properties of the printing material and porosity of the lattice in the research article [1].

Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition-Property Relationship: A Case Study of NanoMine Database.

The NanoMine database, one of two nodes in the MaterialsMine database, is a new materials data resource that collects annotated data on polymer nanocomposites (PNCs). This work showcases the potential of NanoMine and other materials data resources to assist fundamental materials understanding and therefore rational materials design. This specific case study is built around studying the relationship between the change in the glass transition temperature Tg (ΔTg) and key descriptors of the nanofillers and the polymer matrix in PNCs. We sifted through data from over 2000 experimental samples curated into NanoMine, trained a decision tree classifier to predict the sign of PNC ΔTg, and built a multiple power regression metamodel to predict ΔTg. The successful model used key descriptors including composition, nanoparticle volume fraction, and interfacial surface energy. The results demonstrate the power of using aggregated materials data to gain insight and predictive capability. Further analysis points to the importance of additional analysis of parameters from processing methodologies and continuously adding curated data sets to increase the sample pool size.

Hierarchically structured bioinspired nanocomposites.

Next-generation structural materials are expected to be lightweight, high-strength and tough composites with embedded functionalities to sense, adapt, self-repair, morph and restore. This Review highlights recent developments and concepts in bioinspired nanocomposites, emphasizing tailoring of the architecture, interphases and confinement to achieve dynamic and synergetic responses. We highlight cornerstone examples from natural materials with unique mechanical property combinations based on relatively simple building blocks produced in aqueous environments under ambient conditions. A particular focus is on structural hierarchies across multiple length scales to achieve multifunctionality and robustness. We further discuss recent advances, trends and emerging opportunities for combining biological and synthetic components, state-of-the-art characterization and modelling approaches to assess the physical principles underlying nature-inspired design and mechanical responses at multiple length scales. These multidisciplinary approaches promote the synergetic enhancement of individual materials properties and an improved predictive and prescriptive design of the next era of structural materials at multilength scales for a wide range of applications.

The materials tetrahedron has a "digital twin".

Abstract: For over three decades, the materials tetrahedron has captured the essence of materials science and engineering with its interdependent elements of processing, structure, properties, and performance. As modern computational and statistical techniques usher in a new paradigm of data-intensive scientific research and discovery, the rate at which the field of materials science and engineering capitalizes on these advances hinges on collaboration between numerous stakeholders. Here, we provide a contemporary extension to the classic materials tetrahedron with a dual framework-adapted from the concept of a "digital twin"-which offers a nexus joining materials science and information science. We believe this high-level framework, the materials-information twin tetrahedra (MITT), will provide stakeholders with a platform to contextualize, translate, and direct efforts in the pursuit of propelling materials science and technology forward. Impact statement: This article provides a contemporary reimagination of the classic materials tetrahedron by augmenting it with parallel notions from information science. Since the materials tetrahedron (processing, structure, properties, performance) made its first debut, advances in computational and informational tools have transformed the landscape and outlook of materials research and development. Drawing inspiration from the notion of a digital twin, the materials-information twin tetrahedra (MITT) framework captures a holistic perspective of materials science and engineering in the presence of modern digital tools and infrastructures. This high-level framework incorporates sustainability and FAIR data principles (Findable, Accessible, Interoperable, Reusable)-factors that recognize how systems impact and interact with other systems-in addition to the data and information flows that play a pivotal role in knowledge generation. The goal of the MITT framework is to give stakeholders from academia, industry, and government a communication tool for focusing efforts around the design, development, and deployment of materials in the years ahead.

Metalized polyamide heterostructure as a moisture-responsive actuator for multimodal adaptive personal heat management.

Personal thermal management textile/wearable is an effective strategy to expand the indoor temperature setpoint range to reduce a building's energy consumption. Usually, textiles/wearables that were engineered for controlling conduction, convection, radiation, or sweat evaporation have been developed separately. Here, we demonstrate a multimodal adaptive wearable with moisture-responsive flaps composed of a nylon/metal heterostructure, which can simultaneously regulate convection, sweat evaporation, and mid-infrared emission to accomplish large and rapid heat transfer tuning in response to human perspiration vapor. We show that the metal layer not only plays a crucial role in low-emissivity radiative heating but also enhances the bimorph actuation performance. The multimodal adaptive mechanism expands the thermal comfort zone by 30.7 and 20.7% more than traditional static textiles and single-modal adaptive wearables without any electricity and energy input, making it a promising design paradigm for personal heat management.

ChemProps: A RESTful API enabled database for composite polymer name standardization.

The inconsistency of polymer indexing caused by the lack of uniformity in expression of polymer names is a major challenge for widespread use of polymer related data resources and limits broad application of materials informatics for innovation in broad classes of polymer science and polymeric based materials. The current solution of using a variety of different chemical identifiers has proven insufficient to address the challenge and is not intuitive for researchers. This work proposes a multi-algorithm-based mapping methodology entitled ChemProps that is optimized to solve the polymer indexing issue with easy-to-update design both in depth and in width. RESTful API is enabled for lightweight data exchange and easy integration across data systems. A weight factor is assigned to each algorithm to generate scores for candidate chemical names and optimized to maximize the minimum value of the score difference between the ground truth chemical name and the other candidate chemical names. Ten-fold validation is utilized on the 160 training data points to prevent overfitting issues. The obtained set of weight factors achieves a 100% test accuracy on the 54 test data points. The weight factors will evolve as ChemProps grows. With ChemProps, other polymer databases can remove duplicate entries and enable a more accurate "search by SMILES" function by using ChemProps as a common name-to-SMILES translator through API calls. ChemProps is also an excellent tool for auto-populating polymer properties thanks to its easy-to-update design.

Polymer Nanocomposite Data: Curation, Frameworks, Access, and Potential for Discovery and Design.

With the advent of the materials genome initiative (MGI) in the United States and a similar focus on materials data around the world, a number of materials data resources and associated vocabularies, tools, and repositories have been developed. While the majority of systems focus on slices of computational data with an emphasis on metallic alloys, NanoMine is an open source platform with the goal of curating and storing widely varying experimental data on polymer nanocomposites (polymers doped with nanoparticles) and providing access to characterization and analysis tools with the long-term objective of promoting facile nanocomposite design. Data on over 2500 samples from the literature and individual laboratories has been curated to date into NanoMine, including 230 samples from the papers bound in this virtual issue. This virtual issue represents an experiment of the flexibility of the data repository to capture the unique experimental metadata requirements of many data sets at one time and to challenge the authors to participate in the curation of their research data associated with a given publication. In principle, NanoMine offers a FAIR platform in which data published in papers becomes directly Findable and Accessible via simple search tools, with open metadata standards that are Interoperable with larger materials data registries, and allows easy Reuse of data, e.g. benchmarking against new results. Our hope is that with time, platforms such as this one could capture much of the newly published data on materials and form nodes in an interconnected materials data ecosystem which would allow researchers to robustly archive their data, add to the growing body of readily accessible data, and enable new forms of discovery by application of data analysis and design tools.

Temperature effects on the nanoindentation characterization of stiffness gradients in confined polymers.

The stiffening of polymers near inorganic fillers plays an important role in strengthening polymer nanocomposites, and recent advances in metrology have allowed us to sample such effects using local mechanical measurement techniques such as nanoindentation and atomic force microscopy. A general understanding of temperature and confinement effects on the measured stiffness gradient length-scale ξint is lacking however, which convolutes molecular interpretation of local property measurements. Using coarse-grained molecular dynamics and finite element nanoindentation simulations, we show that the measured ξint increases with temperature in highly confined polymer systems, a dependence which acts in the opposite direction in systems with low confinement. These disparate trends are closely related to the polymer's viscoelastic state and the resulting changes in incompressibility and dissipative ability as the polymer transitions from glassy to rubbery. At high temperatures above the glass transition temperature, a geometrically confined system restricts the viscous dissipation of the applied load by the increasingly incompressible polymer. The indentation causes a dramatic build-up of hydrostatic pressure near the confining surface, which contributes to an enlarged measurement of ξint. By contrast, a less-confined system allows the pressure to dissipate via intermolecular motion, thus lowering the measured ξint with increased temperature above the glass transition temperature. These findings suggest that the well-established thin film-nancomposite analogy for polymer mobility near interfaces can be convoluted when measuring local mechanical properties, as the viscoelastic state and geometric confinement of the polymer can affect the nanomechanical response during indentation purely from continuum effects.

A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions.

Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials.

Open-source micro-tensile testers via additive manufacturing for the mechanical characterization of thin films and papers.

The cost of specialized scientific equipment can be high and with limited funding resources, researchers and students are often unable to access or purchase the ideal equipment for their projects. In the fields of materials science and mechanical engineering, fundamental equipment such as tensile testing devices can cost tens to hundreds of thousands of dollars. While a research lab often has access to a large-scale testing machine suitable for conventional samples, loading devices for meso- and micro-scale samples for in-situ testing with the myriad of microscopy tools are often hard to source and cost prohibitive. Open-source software has allowed for great strides in the reduction of costs associated with software development and open-source hardware and additive manufacturing have the potential to similarly reduce the costs of scientific equipment and increase the accessibility of scientific research. To investigate the feasibility of open-source hardware, a micro-tensile tester was designed with a freely accessible computer-aided design package and manufactured with a desktop 3D-printer and off-the-shelf components. To our knowledge this is one of the first demonstrations of a tensile tester with additively manufactured components for scientific research. The capabilities of the tensile tester were demonstrated by investigating the mechanical properties of Graphene Oxide (GO) paper and thin films. A 3D printed tensile tester was successfully used in conjunction with an atomic force microscope to provide one of the first quantitative measurements of GO thin film buckling under compression. The tensile tester was also used in conjunction with an atomic force microscope to observe the change in surface topology of a GO paper in response to increasing tensile strain. No significant change in surface topology was observed in contrast to prior hypotheses from the literature. Based on this result obtained with the new open source tensile stage we propose an alternative hypothesis we term 'superlamellae consolidation' to explain the initial deformation of GO paper. The additively manufactured tensile tester tested represents cost savings of >99% compared to commercial solutions in its class and offers simple customization. However, continued development is needed for the tensile tester presented here to approach the technical specifications achievable with commercial solutions.

Multi-modal magnetic resonance elastography for noninvasive assessment of ovarian tissue rigidity in vivo.

For centuries, physicians have relied on touch to palpate tissue and detect abnormalities throughout the body. While this time-tested method has provided a simple diagnostic examination for large, superficial abnormalities, it does not permit quantifiable measurements of stiffness in deeper, small organs. Advances in noninvasive imaging to measure tissue rigidity represent important extensions of manual palpation techniques. Tissue fibrosis occurs with age in many organs; in the ovary, it is thought to be a marker of polycystic ovary syndrome and age-related idiopathic infertility, although quantitative assessment of fibrosis in this deep, abdominal tissue has not been possible. We used noninvasive methods to quantify ovarian tissue rigidity and clarify the role of tissue stiffness in reproductive health. With proper validation against accepted standards, noninvasive imaging techniques may become the quantitative counterpart to interior probing palpation methods and invasive (surgical) diagnoses, with applications across many clinical settings, including evaluation of adolescent and young adult ovarian function.

Characterization of local elastic modulus in confined polymer films via AFM indentation.

The properties of polymers near an interface are altered relative to their bulk value due both to chemical interaction and geometric confinement effects. For the past two decades, the dynamics of polymers in confined geometries (thin polymer film or nanocomposites with high-surface area particles) has been studied in detail, allowing progress to be made toward understanding the origin of the dynamic effects near interfaces. Observations of mechanical property enhancements in polymer nanocomposites have been attributed to similar origins. However, the existing measurement methods of these local mechanical properties have resulted in a variety of conflicting results on the change of mechanical properties of confined polymers. Here, an atomic force microscopy (AFM)-based method is demonstrated that directly measures the mechanical properties of polymers adjacent to a substrate with nanometer resolution. This method allows us to consistently observe the gradient in mechanical properties away from a substrate in various materials systems, and paves the way for a unified understanding of thermodynamic and mechanical response of polymers. This gradient is both longer (up to 170 nm) and of higher magnitude (50% increase) than expected from prior results. Through the use of this technique, we will be better able to understand how to design polymer nanocomposites and polymeric structures at the smallest length scale, which affects the fields of structures, electronics, and healthcare.

Thermomechanical properties and deformation of coarse-grained models of hard-soft block copolymers.

In this paper, we investigate the enhancement mechanism of the mechanical properties for hard-soft block copolymers by using molecular dynamics simulations at various temperatures. A coarse-grained approach is adopted to study sufficiently generic models. Our numerical experiments demonstrate that the nonbond potential plays a more significant role in mechanical properties compared to the bond potential. This finding serves as a cornerstone to understand the hard-soft materials. To explore the effect of hard segments, four copolymers with different concentrations and energy factors that describe the interaction between hard beads are conducted. Simulation results show that the mechanical performances of the system with large attractive force and small concentration of hard segments could be improved dramatically in conjunction with a moderate increment of the glass transition temperature. In particular, the energy factor shows a substantial influence in determining the microphase separation as well as the morphology of hard domains. These observations are believed to provide design guidelines for polymeric materials in engineering practice.

Effect of high-energy X-ray irradiation on creep mechanisms in bone and dentin.

Under long-term loading creep conditions, mineralized biological tissues like bone are expected to behave in a similar manner to synthetic composites where the creeping matrix sheds load to the elastic reinforcement as creep deformation progresses. To study this mechanism in biological composites, creep experiments were performed at 37 °C on bovine compact bone and dentin. Static compressive stresses were applied to the samples, while wide- and small-angle scattering patterns from high energy synchrotron X-rays were used to determine, respectively, the elastic strain in the hydroxyapatite (HAP) platelets and the strain in the mineralized collagen fibril, as a function of creep time. In these highly irradiated biological composites, the reinforcing hydroxyapatite platelets progressively transfer some of their stress back to the softer protein matrix during creep. While such behavior can be explained by damage at the interface between the two phases, it is not consistent with measurements of the apparent moduli--the ratio of applied stress to elastic HAP strain measured throughout the creep experiments by elastic unload/load segments--which remained constant throughout the experiment and thus indicated good HAP/protein bonding. A possible explanation is a combination of X-ray and load induced interfacial damage explaining the shedding of load from the HAP during long term creep, coupled with interfacial re-bonding of the load-disrupted reversible bonds upon unloading, explaining the unaffected elastic load partitioning during unload/load segments. This hypothesis is further supported by finite element modeling which shows results mirroring the experimental strain measurements when considering interfacial delamination and a compliant interstitial space at the ends of the HAP platelets.

Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers.

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle-filled polymers is demonstrated. A key component of this "Materials Genomics" approach is the development and use of Materials Quantitative Structure-Property Relationship (MQSPR) models trained on atomic-level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies. Surface energy differences are then correlated with the nanofiller dispersion morphology and filler/matrix interface properties and integrated into a numerical analysis approach that allows the prediction of thermomechanical properties of the spherical nanofilled polymer composites. Systematic experimental studies of silica nanoparticles modified with three different surface chemistries in polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA) and poly(2-vinyl pyridine) (P2VP) are used to validate the models. While demonstrated here as effective for the prediction of meso-scale morphologies and macro-scale properties under quasi-equilibrium processing conditions, the protocol has far ranging implications for Virtual Design.

Exfoliation and reassembly of cobalt oxide nanosheets into a reversible lithium-ion battery cathode.

An exfoliation-reassembly-activation (ERA) approach to lithium-ion battery cathode fabrication is introduced, demonstrating that inactive HCoO(2) powder can be converted into a reversible Li(1-x) H(x) CoO(2) thin-film cathode. This strategy circumvents the inherent difficulties often associated with the powder processing of the layered solids typically employed as cathode materials. The delamination of HCoO(2) via a combination of chemical and mechanical exfoliation generates a highly processable aqueous dispersion of [CoO(2) ](-) nanosheets that is critical to the ERA approach. Following vacuum-assisted self-assembly to yield a thin-film cathode and ion exchange to activate this material, the generated cathodes exhibit excellent cyclability and discharge capacities approaching that of low-temperature-prepared LiCoO(2) (~83 mAh g(-1) ), with this good electrochemical performance attributable to the high degree of order in the reassembled cathode.