Machine learning-enabled feature classification of evaporation-driven multi-scale 3D printing.

The freeform generation of active electronics can impart advanced optical, computational, or sensing capabilities to an otherwise passive construct by overcoming the geometrical and mechanical dichotomies between conventional electronics manufacturing technologies and a broad range of three-dimensional (3D) systems. Previous work has demonstrated the capability to entirely 3D print active electronics such as photodetectors and light-emitting diodes by leveraging an evaporation-driven multi-scale 3D printing approach. However, the evaporative patterning process is highly sensitive to print parameters such as concentration and ink composition. The assembly process is governed by the multiphase interactions between solutes, solvents, and the microenvironment. The process is susceptible to environmental perturbations and instability, which can cause unexpected deviation from targeted print patterns. The ability to print consistently is particularly important for the printing of active electronics, which require the integration of multiple functional layers. Here we demonstrate a synergistic integration of a microfluidics-driven multi-scale 3D printer with a machine learning algorithm that can precisely tune colloidal ink composition and classify complex internal features. Specifically, the microfluidic-driven 3D printer can rapidly modulate ink composition, such as concentration and solvent-to-cosolvent ratio, to explore multi-dimensional parameter space. The integration of the printer with an image-processing algorithm and a support vector machine-guided classification model enables automated, in-situ pattern classification. We envision that such integration will provide valuable insights in understanding the complex evaporative-driven assembly process and ultimately enable an autonomous optimisation of printing parameters that can robustly adapt to unexpected perturbations.

Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials.

Conventional approaches to developing biomaterials and implants require intuitive tailoring of manufacturing protocols and biocompatibility assessment. This leads to longer development cycles, and high costs. To meet existing and unmet clinical needs, it is critical to accelerate the production of implantable biomaterials, implants and biomedical devices. Building on the Materials Genome Initiative, we define the concept 'biomaterialomics' as the integration of multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools throughout the entire pipeline of biomaterials development. The Data Science-driven approach is envisioned to bring together on a single platform, the computational tools, databases, experimental methods, machine learning, and advanced manufacturing (e.g., 3D printing) to develop the fourth-generation biomaterials and implants, whose clinical performance will be predicted using 'digital twins'. While analysing the key elements of the concept of 'biomaterialomics', significant emphasis has been put forward to effectively utilize high-throughput biocompatibility data together with multiscale physics-based models, E-platform/online databases of clinical studies, data science approaches, including metadata management, AI/ Machine Learning (ML) algorithms and uncertainty predictions. Such integrated formulation will allow one to adopt cross-disciplinary approaches to establish processing-structure-property (PSP) linkages. A few published studies from the lead author's research group serve as representative examples to illustrate the formulation and relevance of the 'Biomaterialomics' approaches for three emerging research themes, i.e. patient-specific implants, additive manufacturing, and bioelectronic medicine. The increased adaptability of AI/ML tools in biomaterials science along with the training of the next generation researchers in data science are strongly recommended. STATEMENT OF SIGNIFICANCE: This leading opinion review paper emphasizes the need to integrate the concepts and algorithms of the data science with biomaterials science. Also, this paper emphasizes the need to establish a mathematically rigorous cross-disciplinary framework that will allow a systematic quantitative exploration and curation of critical biomaterials knowledge needed to drive objectively the innovation efforts within a suitable uncertainty quantification framework, as embodied in 'biomaterialomics' concept, which integrates multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools, like machine learning. The formulation of this approach has been demonstrated for patient-specific implants, additive manufacturing, and bioelectronic medicine.

Critical comparison of image analysis workflows for quantitative cell morphological evaluation in assessing cell response to biomaterials.

Quantitative image analysis is an important tool in understanding cell fate processes through the study of cell morphological changes in terms of size, shape, number, and orientation. In this context, this work explores systematically the main challenges involved in the quantitative analysis of fluorescence microscopy images and also proposes a new protocol while comparing its outcome with the widely used ImageJ analysis. It is important to mention that fluorescence microscopy is by far most widely used in biocompatibility analysis (observing cell fate changes) of implantable biomaterials. In this study, we employed two different image analyses toolsets: (a) the conventionally employed ImageJ software, and (b) a recently developed automated digital image analyses framework, called ImageMKS. While ImageJ offers a powerful toolset for image analyses, it requires sophisticated user expertise to design and iteratively refine the analyses workflow. This workflow primarily comprises a sequence of image transformations that typically involve de-noising and labeling of features. On the other hand, ImageMKS automates the image analyses protocol to a large extent, and thereby mitigates the influence of the user bias on the final results. This aspect is addressed using a case study of C2C12 mouse myoblast cells grown on poly(vinylidene difluoride) (PVDF) based polymeric substrates. In particular, we used a number of fluorescence microscopy images of these mouse myoblasts grown on PVDF-based nanobiocomposites under the influence of electric field. In addition to the MKS workflows requiring much less user time because of their automation, it was observed that ImageMKS workflows consistently produced more reliable results that correlated better with the previously reported experimental studies.

Evaluation of Ti-Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression.

Compositionally graded cylinders of Ti-Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of a-b microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress-strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17-1.78 mm, and beta content in the range of 20-83 vol.%. The estimated values of the Young's moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97-130 GPa and 828-1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables.

Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks.

This paper introduces voxelized atomic structure (VASt) potentials as a machine learning (ML) framework for developing interatomic potentials. The VASt framework utilizes a voxelized representation of the atomic structure directly as the input to a convolutional neural network (CNN). This allows for high-fidelity representations of highly complex and diverse spatial arrangements of the atomic environments of interest. The CNN implicitly establishes the low-dimensional features needed to correlate each atomic neighborhood to its net atomic force. The selection of the salient features of the atomic structure (i.e., feature engineering) in the VASt framework is implicit, comprehensive, automated, scalable, and highly efficient. The calibrated convolutional layers learn the complex spatial relationships and multibody interactions that govern the physics of atomic systems with remarkable fidelity. We show that VASt potentials predict highly accurate forces on two phases of silicon carbide and the thermal conductivity of silicon over a range of isotropic strain.

Periprosthetic biomechanical response towards dental implants, with functional gradation, for single/multiple dental loss.

The differences in shape and stiffness of the dental implants with respect to the natural teeth (especially, dental roots) cause a significant alteration of the periprosthetic biomechanical response, which typically leads to bone resorption and ultimately implant loosening. In order to avoid such clinical complications, the implant stiffness needs to be appropriately adapted. In this study, hollow channels were virtually introduced within the designed implant screws for reduction of the overall stiffness of the prototype. In particular, two opposing radial gradients of increasing hollow channel diameters, i.e., outside to inside (Channel 1) and inside to outside (Channel 2) were considered. Two clinical situations of edentulism were addressed in this finite element-based study, and these include a) loss of the first molar, and b) loss of all the three molars. Consequently, two implantation approaches were simulated for multiple teeth loss - individual implantation and implant supported dental bridge. The effects of implant length, approach and channel distribution on the biomechanical response were evaluated in terms of the von Mises stress within the interfacial periprosthetic bone, under normal masticatory loading. The results of our FE analysis clearly reveal significant variation in periprosthetic bone stress between the different implant designs and approaches. An implant screw length of 11 mm with the Channel 2 configuration was found to provide the best biomechanical response. This study also revealed that the implant supported dental bridge approach, which requires lower bone invasion, results in favorable biomechanical response in case of consecutive multiple dental loss.

Probing nanoscale damage gradients in ion-irradiated metals using spherical nanoindentation.

We discuss and demonstrate the application of recently developed spherical nanoindentation stress-strain protocols in characterizing the mechanical behavior of tungsten polycrystalline samples with ion-irradiated surfaces. It is demonstrated that a simple variation of the indenter size (radius) can provide valuable insights into heterogeneous characteristics of the radiation-induced-damage zone. We have also studied the effect of irradiation for the different grain orientations in the same sample.

Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials.

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data.

A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. This workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. Methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. Additionally, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

Extraction of Process-Structure Evolution Linkages from X-ray Scattering Measurements Using Dimensionality Reduction and Time Series Analysis.

The rapid development of robust, reliable, and reduced-order process-structure evolution linkages that take into account hierarchical structure are essential to expedite the development and manufacturing of new materials. Towards this end, this paper lays a theoretical framework that injects the established time series analysis into the recently developed materials knowledge systems (MKS) framework. This new framework is first presented and then demonstrated on an ensemble dataset obtained using small-angle X-ray scattering on semi-crystalline linear low density polyethylene films from a synchrotron X-ray scattering experiment.

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures.

The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

Microstructure-based knowledge systems for capturing process-structure evolution linkages.

This paper reviews and advances a data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations. This approach is called the MKS (Materials Knowledge Systems) framework, and was previously applied successfully for capturing mainly the microstructure-property linkages in spatial multiscale simulations. This paper generalizes this framework by allowing the introduction of different basis functions, and explores their potential benefits in establishing the desired process-structure-property (PSP) linkages. These new developments are demonstrated using a Cahn-Hilliard simulation as an example case study, where structure evolution was predicted three orders of magnitude faster than an optimized numerical integration algorithm. This study suggests that the MKS localization framework provides an alternate method to learn the underlying embedded physics in a numerical model expressed through Green's function based influence kernels rather than differential equations, and potentially offers significant computational advantages in problems where numerical integration schemes are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for multiscale materials modeling and simulations in both space and time.

High throughput exploration of process-property linkages in Al-6061 using instrumented spherical microindentation and microstructurally graded samples.

Recent spherical nanoindentation protocols have proven robust at capturing the local elastic-plastic response of polycrystalline metal samples at length scales much smaller than the grain size. In this work, we extend these protocols to length scales that include multiple grains to recover microindentation stress-strain curves. These new protocols are first established in this paper and then demonstrated for Al-6061 by comparing the measured indentation stress-strain curves with the corresponding measurements from uniaxial tension tests. More specifically, the scaling factors between the uniaxial yield strength and the indentation yield strength was determined to be about 1.9, which is significantly lower than the value of 2.8 used commonly in literature. The reasons for this difference are discussed. Second, the benefits of these new protocols in facilitating high throughput exploration of process-property relationships are demonstrated through a simple case study.

Nacre-like hybrid films: Structure, properties, and the effect of relative humidity.

Functional materials often are hybrids composed of biopolymers and mineral constituents. The arrangement and interactions of the constituents frequently lead to hierarchical structures with exceptional mechanical properties and multifunctionality. In this study, hybrid thin films with a nacre-like brick-and-mortar microstructure were fabricated in a straightforward and reproducible manner through manual shear casting using the biopolymer chitosan as the matrix material (mortar) and alumina platelets as the reinforcing particles (bricks). The ratio of inorganic to organic content was varied from 0% to 15% and the relative humidities from 36% to 75% to determine their effects on the mechanical properties. It was found that increasing the volume fraction of alumina from 0% to 15% results in a twofold increase in the modulus of the film, but decreases the tensile strength by up to 30%, when the volume fraction of alumina is higher than 5%. Additionally, this study quantifies and illustrates the critical role of the relative humidity on the mechanical properties of the hybrid film. Increasing the relative humidity from 36% to 75% decreases the modulus and strength by about 45% and triples the strain at failure. These results suggest that complex hybrid materials can be manufactured and tailor made for specific applications or environmental conditions.

In situ X-ray nanotomography of metal surfaces during electropolishing.

A low voltage electropolishing of metal wires is attractive for nanotechnology because it provides centimeter long and micrometer thick probes with the tip radius of tens of nanometers. Using X-ray nanotomography we studied morphological transformations of the surface of tungsten wires in a specially designed electrochemical cell where the wire is vertically submersed into the KOH electrolyte. It is shown that stability and uniformity of the probe span is supported by a porous shell growing at the surface of tungsten oxide and shielding the wire surface from flowing electrolyte. It is discovered that the kinetics of shell growth at the triple line, where meniscus meets the wire, is very different from that of the bulk of electrolyte. Many metals follow similar electrochemical transformations hence the discovered morphological transformations of metal surfaces are expected to play significant role in many natural and technological applications.

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets.

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations.

Machine learning approaches for elastic localization linkages in high-contrast composite materials.

There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure-property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially result in a fast and efficient material design. This work explores and presents multiple viable approaches for computationally efficient predictions of the microscale elastic strain fields in a three-dimensional (3-D) voxel-based microstructure volume element (MVE). Advanced concepts in machine learning and data mining, including feature extraction, feature ranking and selection, and regression modeling, are explored as data experiments. Improvements are demonstrated in a gradually escalated fashion achieved by (1) feature descriptors introduced to represent voxel neighborhood characteristics, (2) a reduced set of descriptors with top importance, and (3) an ensemble-based regression technique.

Assessment of lamellar level properties in mouse bone utilizing a novel spherical nanoindentation data analysis method.

In this work, we demonstrate the viability of using our recently developed data analysis procedures for spherical nanoindentation in conjunction with Raman spectroscopy for studying lamellar-level correlations between the local composition and local mechanical properties in mouse bone. Our methodologies allow us to convert the raw load-displacement datasets to much more meaningful indentation stress-strain curves that accurately capture the loading and unloading elastic moduli, the indentation yield points, as well as the post-yield characteristics in the tested samples. Using samples of two different inbred mouse strains, A/J and C57BL/6J (B6), we successfully demonstrate the correlations between the mechanical information obtained from spherical nanoindentation measurements to the local composition measured using Raman spectroscopy. In particular, we observe that a higher mineral-to-matrix ratio correlated well with a higher local modulus and yield strength in all samples. Thus, new bone regions exhibited lower moduli and yield strengths compared to more mature bone. The B6 mice were also found to exhibit lower modulus and yield strength values compared to the more mineralized A/J strain.

Measuring the dynamic mechanical response of hydrated mouse bone by nanoindentation.

This study demonstrates a novel approach to characterizing hydrated bone's viscoelastic behavior at lamellar length scales using dynamic indentation techniques. We studied the submicron-level viscoelastic response of bone tissue from two different inbred mouse strains, A/J and B6, with known differences in whole bone and tissue-level mechanical properties. Our results show that bone having a higher collagen content or a lower mineral-to-matrix ratio demonstrates a trend towards a larger viscoelastic response. When normalized for anatomical location relative to biological growth patterns in the antero-medial (AM) cortex, bone tissue from B6 femora, known to have a lower mineral-to-matrix ratio, is shown to exhibit a significantly higher viscoelastic response compared to A/J tissue. Newer bone regions with a higher collagen content (closer to the endosteal edge of the AM cortex) showed a trend towards a larger viscoelastic response. Our study demonstrates the feasibility of this technique for analyzing local composition-property relationships in bone. Further, this technique of viscoelastic nanoindentation mapping of the bone surface at these submicron length scales is shown to be highly advantageous in studying subsurface features, such as porosity, of wet hydrated biological specimens, which are difficult to identify using other methods.