L. Catherine Brinson

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

Contemporary scientometric indicators remain anchored in paradigms and axioms from when academic research was conducted in small scholarly communities. With the global proliferation of scientific research, academia is now organized in large communities with high rates of information incompleteness regarding work impact and individual contributions. This has significant implications for how research output is measured and quality controlled, especially as the rate of academic publishing continues to rise. Exploits of complex systems are typically found at discrete transition points where rules turn on or off, and academia is not immune to this pattern. Exploitative career boosting strategies are a growing problem, largely enabled by misaligned incentives and traditional metrics that force discretization of credit to authors and prior works despite their fundamentally continuous nature. This article introduces Liberata's scientometrics, a share based framework for academic publishing and quality control. In this system, authorship positions are replaced with contribution shares that sum to unity and encode both ordinality and relative contribution distances. These shares can be traded on Liberata's academic marketplaces for quality control services such as peer review and replication, rewarding contributors based on the long term success of the work. Citations are weighted to guard against frivolous referencing and credit inflation, and modular correction factors allow multiple measures of impact. Liberata's metrics are formalized through two fundamental graphs, Shares and References, from which the system constructs academic capital and derives scientometrics capturing impact, risk, collaboration, collusion, value of quality control, and diversification. These metrics represent academic contributions and extend naturally to institutions, regions, time periods, and research fields.

Contemporary scientometric indicators remain anchored in paradigms and axioms from when academic research was conducted in small scholarly communities. With the global proliferation of scientific research, academia is now organized in large communities with high rates of information incompleteness regarding work impact and individual contributions. This has significant implications for how research output is measured and quality controlled, especially as the rate of academic publishing continues to rise. Exploits of complex systems are typically found at discrete transition points where rules turn on or off, and academia is not immune to this pattern. Exploitative career boosting strategies are a growing problem, largely enabled by misaligned incentives and traditional metrics that force discretization of credit to authors and prior works despite their fundamentally continuous nature. This article introduces Liberata's scientometrics, a share based framework for academic publishing and quality control. In this system, authorship positions are replaced with contribution shares that sum to unity and encode both ordinality and relative contribution distances. These shares can be traded on Liberata's academic marketplaces for quality control services such as peer review and replication, rewarding contributors based on the long term success of the work. Citations are weighted to guard against frivolous referencing and credit inflation, and modular correction factors allow multiple measures of impact. Liberata's metrics are formalized through two fundamental graphs, Shares and References, from which the system constructs academic capital and derives scientometrics capturing impact, risk, collaboration, collusion, value of quality control, and diversification. These metrics represent academic contributions and extend naturally to institutions, regions, time periods, and research fields.

Polymer nanocomposites, inherently tailorable materials, are potentially capable of providing higher strength to weight ratio than conventional hard metals. However, their disordered nature makes processing control and hence tailoring properties to desired target values a challenge. Additionally, the interfacial region, also called the interphase, is a critical material phase in these heterogeneous materials and its extent depends on variety of microstructure features like particle loading and dispersion or inter-particle distances. Understanding process-structure–property (PSP) relation can provide guidelines for process and constituents’ design. Our work explores nuances of PSP relation for polymer nanocomposites with attractive pairing between particles and the bulk polymer. Past works have shown that particle functionalization can help tweak these interactions in attractive or repulsive type and can cause slow or fast decay of stiffness properties in polymer nanocomposites. In this work, we develop a material model that can represent decay for small strain elastoplastic(Young’s modulus and yield strength) properties in interfacial regions and simulate representative or statistical volume element behavior. The interfacial elastoplastic material model is devised by combining local stiffness and glass transition measurements from atomic force microscopy and fluorescence microscopy. This model is combined with a microstructural design of experiments for agglomerated nanocomposite systems. Agglomerations are particle aggregations arising from processing artifacts. Twin screw extrusion process can reduce extent of aggregation in hot pressed samples via erosion or rupture depending on screw rpms and torque. We connect this process-structure relation to structure–property relation that emerges from our study. We discover that balancing between local stress concentration zones (SCZ) and interfacial property decay governs how fast yield stress can improve by breaking down agglomeration via erosion. Rupture is relatively less effective in helping improve nanocomposite yield strength. We also observe an inflection point where incremental increase brought on by rupture is slowed due to increasing SCZ and saturation in interphase percolation.

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.

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 34 total projects developed during the second annual Large Language Model 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 Managing, 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 statement With 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

Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages. We propose a largely automated framework that integrates unsupervised and supervised learning methods to classify micrographs according to microstructure phase/class and, for multiphase microstructures, segments them into different homogeneous regions. With the advance of manufacturing and imaging techniques, the ultra-high resolution of imaging that reveals the complexity of microstructures and the rapidly increasing quantity of images (i.e., micrographs) enables and necessitates a more powerful and automated framework to extract materials characteristics and knowledge. The framework we propose can be used to gradually build a database of microstructure classes relevant to a particular process or group of materials, which can help in analyzing and discovering/identifying new materials. The framework has three steps: (1) segmentation of multiphase micrographs through a recently developed score-based method so that different microstructure homogeneous regions can be identified in an unsupervised manner; (2) {identification and classification of} homogeneous regions of micrographs through an uncertainty-aware supervised classification network trained using the segmented micrographs from Step $1$ with their identified labels verified via the built-in uncertainty quantification and minimal human inspection; (3) supervised segmentation (more powerful than the segmentation in Step $1$) of multiphase microstructures through a segmentation network trained with micrographs and the results from Steps $1$-$2$ using a form of data augmentation. This framework can iteratively characterize/segment new homogeneous or multiphase materials while expanding the database to enhance performance. The framework is demonstrated on various sets of materials and texture images.

For polymer nanocomposites, disordered microstructural nature makes processing control and tailoring properties to desired values a challenge. Understanding process-structure-property relation can provide guidelines for process and constituents design. Our work explores nuances of PSP relation for polymer nanocomposites with attractive pairing between particles and polymer bulk. In the absence of any nano or micro-scale local property measurement, we develop a material model that can represent decay for small strain elastoplastic properties in interfacial regions and simulate representative or statistical volume element behavior. This interfacial model is further combined with a microstructural design of experiments for agglomerated nanocomposite systems. Agglomerations are particle aggregations that are microstructural defects resulting from lack of processing control. Twin screw extrusion process can reduce extent of aggregation in hot pressed samples via erosion or rupture depending on screw rpms and toque. We connect this process-structure relation to structure-property relation that emerges from our study. We discover that balancing between local stress concentration zone and interfacial property decay governs how fast yield stress can improve if we break down agglomeration via erosion. Rupture is relatively less effective in helping improve nanocomposite yield strength. Additionally, we allude to yield initiation and progression in these multiphase materials. We have come up with a field quantity called local yield resistance that indicates balance stress concentration zones and interfacial effects. Yield resistance map from linear regime acts as a predictor of local yielding process and can be a useful tool for interface design for plastic deformation behavior.

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (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 scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.

Understanding the mechanical properties of polymer nanocomposite materials is essential for industrial use. Particularly, the determination of the polymer modulus at the nanofiller–polymer interphase is important for optimizing the interfacial mechanical properties. Nanoindentation via Atomic Force Microscopy (AFM) is well-established for measuring the modulus of the interphase region with nanoscale spatial resolution. However, indentation into heterogeneous materials presents a confounding issue often referred to as the “substrate effect”, i.e., the structural stress field caused by the rigid body is convoluted with the actual modulus of the interphase region. While finite element analysis (FEA)-based methods can be used to deconvolute the interphase modulus from measured apparent modulus–distance profiles, the experimental validation of this method is still needed. Here, we provide this validation using AFM nanoindentation on a layered model composite that consists of three layers with different moduli to recapitulate the properties of the matrix, the filler, and the interphase of real polymer nanocomposites. By systematically varying the thickness of the “artificial” interphase layer and the AFM probe radius, we obtain modulus–distance profiles over a wide range of indentation conditions. We validate a method to deconvolute the substrate effect using an empirically derived master curve obtained from FEA analysis. Furthermore, we showed that the effect of the artificial interphase on modulus– distance profiles can be distinguished only if the interphase layer is thick enough compared to the contact radius of the probe. Finally, we established an innovative and quantitative framework to predict the interphase thickness from mechanical nanoindentation measurements and discussed the lower, practical limit for interphase thickness determination. In summary, we provide a broadly applicable method to extract interphase mechanical properties of multiphase soft materials and practical guidelines for choosing optimal characterization conditions.

In the realm of metamaterial research, the exploration of random structures presents an innovative path less traveled, compared to the conventional focus on periodic designs. Our study introduces a novel framework for generating random metamaterials using graph algorithms, which ensures connectivity and adaptability across a multitude of base shapes, such as cylinders, triangles, pyramids, and cubes. This flexibility enables the application of our designs across various domains, allowing for the investigation of properties including stiffness, density, and acoustic impedance. By leveraging graph algorithms in our framework, data representation and manipulation become more intuitive and efficient, facilitating the design process. Our approach demonstrates significant versatility in manipulating the macroscale and microscale elements of the designs, providing a tailored fit for specific applications. We present a series of designs, showcasing the ability to control and predict the material’s behavior under different conditions. The designs can be effectively implemented across various fields and subjected to multiple analytical studies, encompassing static, dynamic, and eigenfrequency assessments. Properties such as impedance, stiffness, density, and more can be explored, opening the door to a wide array of applications and potential innovations in metamaterial research. We illustrate the computational results for stiffness and acoustic impedance, highlighting the method’s efficacy through examples ranging from rod-based to cube-based designs. This framework not only paves the way for advancements in metamaterial research but also opens up new possibilities for innovation in fields requiring customized material properties.

Understanding the mechanical properties of polymer nanocomposite materials is essential for industrial use. Particularly, the determination of the polymer modulus at the nanofiller-polymer interphase is important for optimizing the interfacial mechanical properties. Nanoindentation via Atomic Force Microscopy (AFM) is well established for measuring the modulus of the interphase region with nanoscale spatial resolution. However, indentation into heterogeneous materials presents a confounding issue often referred to as the "substrate effect", i.e., the structural stress field caused by the rigid body is convoluted with the actual modulus of the interphase region. While finite element analysis (FEA)-based methods can be used to deconvolute the interphase modulus from measured apparent modulus-distance profiles, the experimental validation of this method is still needed. Here, we provide this validation using AFM nanoindentation on a layered model composite which consists of three layers with different moduli to recapitulate the properties of the matrix, the filler, and the interphase of real polymer nanocomposites. By systematically varying the thickness of the “artificial” interphase layer and the AFM probe radius, we obtain modulus - distance profiles over a wide range of indentation conditions. We validate a method to deconvolute the substrate effect using an empirically derived master curve obtained from FEA analysis. Furthermore, we showed that the effect of the artificial interphase on modulus - distance profiles can be distinguished only if the interphase layer is thick enough compared to the contact radius of the probe. Finally, we established an innovative and quantitative framework to predict the interphase thickness from mechanical nanoindentation measurements, and we discussed the lower, practical limit for interphase thickness determination. In summary, we provide a broadly applicable method to extract interphase mechanical properties of multiphase soft materials, and practical guidelines for choosing optimal characterization conditions.

Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over multiple frequency ranges. In this article, hierarchical unit-cells are obtained, where features at each length scale result in a band gap within a targeted frequency range. Our novel approach, the ``hierarchical unit-cell template method,'' is an interpretable machine-learning approach that uncovers global unit-cell shape/topology patterns corresponding to predefined band-gap objectives. A scale-separation effect is observed where the coarse-scale band-gap objective is mostly unaffected by the fine-scale features despite the closeness of their length scales, thus enabling an efficient hierarchical algorithm. Moreover, the hierarchical patterns revealed are not predefined or self-similar hierarchies as common in current hierarchical phononic materials. Thus, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges.

Holistic and intentional training prepares next-generation materials informatics leaders and workforce for expedited materials discovery and design.

This paper investigates the use of large language models (LLMs) for extracting sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers. The challenge lies in the complex nature of PNC samples, which have numerous attributes scattered throughout the text. The complexity of annotating detailed information on PNCs limits the availability of data, making conventional document-level relation extraction techniques impractical due to the challenge in creating comprehensive named entity span annotations. To address this, we introduce a new benchmark and an evaluation technique for this task and explore different prompting strategies in a zero-shot manner. We also incorporate self-consistency to improve the performance. Our findings show that even advanced LLMs struggle to extract all of the samples from an article. Finally, we analyze the errors encountered in this process, categorizing them into three main challenges, and discuss potential strategies for future research to overcome them.

Modern materials design strategies take advantage of the increasing amount of materials property data available and increasingly complex algorithms to take advantage of those data. However, viscoelastic materials resist this trend towards increased data rates due to their inherent time-dependent properties. Therefore, viscoelasticity measurements present a roadblock for data collection in an important aspect of material design. For thermorheologically simple (TRS) materials, time-temperature superposition (TTS) made relaxation spectrum measurements faster relative to, for example, very long creep experiments. However, TTS itself currently faces a speed limit originating in the common logarithmic discrete frequency sweep (DFS) mode of operation. In DFS, the measurement time is proportional (by a factor much greater than one) to the lowest frequency of measurement. This state of affairs has not improved for TTS for half a century or more. We utilize recent work in experimental rheometry on windowed chirps to collect three decades of complex modulus data simultaneously, resulting in a ∼500% increase in data collection. In BOTTS, we superpose several isothermal chirp responses to produce a master curve in a fraction of time required by the traditional DFS-TTS technique. The chirp responses have good, albeit nontrivial, signal-to-noise properties. We use linear error propagation and a noise-weighted least squares approach to automatically incorporate all the data into a reliable shifting method. Using model thermoset polymers, we show that DFS-TTS and BOTTS results are comparable, and therefore BOTTS data represent a first step towards a faster method for master curve generation from unmodified rheological measurement instruments.

This paper investigates the use of large language models (LLMs) for extracting sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers.The challenge lies in the complex nature of PNC samples, which have numerous attributes scattered throughout the text.The complexity of annotating detailed information on PNCs limits the availability of data, making conventional document-level relation extraction techniques impractical due to the challenge in creating comprehensive named entity span annotations.To address this, we introduce a new benchmark and an evaluation technique for this task and explore different prompting strategies in a zero-shot manner.We also incorporate selfconsistency to improve the performance.Our findings show that even advanced LLMs struggle to extract all of the samples from an article.Finally, we analyze the errors encountered in this process, categorizing them into three main challenges, and discuss potential strategies for future research to overcome them.

This paper studies the utility of techniques within uncertainty quantification, namely spectral projection and polynomial chaos expansion, in reducing sampling needs for characterizing acoustic metamaterial dispersion band responses given stochastic material properties and geometric defects. A novel method of encoding geometric defects in an interpretable, resolution independent is showcased in the formation of input space probability distributions. Orders of magnitude sampling reductions down to $\sim10^0$ and $\sim10^1$ are achieved in the 1D and 7D input space scenarios respectively while maintaining accurate output space probability distributions through combining Monte Carlo, quadrature rule, and sparse grid sampling with surrogate model fitting.

Polymer nanodielectrics present a particularly challenging materials design problem for capacitive energy storage applications like polymer film capacitors. High permittivity and breakdown strength are needed to achieve high energy density and loss must be low. Strategies that increase permittivity tend to decrease the breakdown strength and increase loss. We hypothesize that a parameter space exists for fillers of modest aspect ratio functionalized with charge-trapping molecules that results in an increase in permittivity and breakdown strength simultaneously, while limiting increases in loss. In this work, we explore this parameter space, using physics-based, multiscale 3D dielectric property simulations, mixed-variable machine learning and Bayesian optimization to identify the compositions and morphologies which lead to the optimization of these competing properties. We employ first principle-based calculations for interface trap densities which are further used in breakdown strength calculations. For permittivity and loss calculations, we use continuum scale modelling and finite difference solution of Poisson's equation for steady-state currents. We propose a design framework for optimizing multiple properties by tuning design variables including the microstructure and interface properties. Finally, we employ mixed-variable global sensitivity analysis to understand the complex interplay between four continuous microstructural and two categorical interface choices to extract further physical knowledge on the design of nanodielectrics.

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.

The production, collection, and curation of data require painstaking planning and the use of sophisticated experimental and computational facilities. In order to maximize the impact of these investments and create best practices that lead to scientific discovery and innovation, a diverse set of stakeholders defined a set of findable, accessible, interoperable, and reusable (FAIR) principles in 2016 1 , 2 . The original intent was that these principles would apply seamlessly to data and all scholarly digital objects, including research software 3 , workflows 4 , and even domain-specific custom digital objects 5 . However, because they were specifically written in the context of data, it became clear over time that the original set of FAIR principles would have to be translated or reinterpreted for digital assets beyond data 6 , 7 . This realization has led to initiatives that have proposed and/or developed practical FAIR definitions for research software and workflows, and more recently, for artificial intelligence (AI) models 8 , 9 .

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].

Advancements in additive manufacturing (AM) technology and three-dimensional (3D) modeling software have enabled the fabrication of parts with combinations of properties that were impossible to achieve with traditional manufacturing techniques. Porous designs such as truss-based and sheet-based lattices have gained much attention in recent years due to their versatility. The multitude of lattice design possibilities, coupled with a growing list of available 3D printing materials, has provided a vast range of 3D printable structures that can be used to achieve desired performance. However, the process of computationally or experimentally evaluating many combinations of base material and lattice design for a given application is impractical. This research proposes a framework for quickly predicting key mechanical properties of 3D printed gyroid lattices using information about the base material and porosity of the structure. Experimental data was gathered to train a simple, interpretable, and accurate kernel ridge regression machine learning model. The performance of the model was then compared to numerical simulation data and demonstrated similar accuracy at a fraction of the computation time. Ultimately, the model development serves as an advancement in ML-driven mechanical property prediction that can be used to guide extension of current and future models.