Strong interlayer coupling in monoclinic GaTe

Recently, emerging intriguing physical properties have been unraveled in anisotropic layered semiconductors, with their in-plane anisotropy often originated directly from the low crystallographic symmetry. However, little has been known in the case where interlayer couplings dominate the anisotropy of electronic band structures in them. Here, by both experiment and theory, we show rather than geometric factors, the anisotropic energy bands of monoclinic gallium telluride (GaTe) are determined by a subtle bulk-surface interaction. Bulk electronic states are found to be the major contribution of the highest valence band, whose anisotropy is yet immune to surface doping of potassium atoms. The above peculiar behaviors are attributed to strong interlayer couplings, which gives rise to an inverse of anisotropy of hole effective masses and a direct-indirect-direct transition of band gap, depending on the number of layers. Our results thus pave the way for future applications of anisotropic layered semiconductors in nanoelectronics and optoelectronics.Comment: 3 figure

Analyses of internal structures and defects in materials using physics-informed neural networks

Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, and nonlinear deformation. Here, we present a general framework based on physics-informed neural networks for identifying unknown geometric and material parameters. By using a mesh-free method, we parameterize the geometry of the material using a differentiable and trainable method that can identify multiple structural features. We validate this approach for materials with internal voids/inclusions using constitutive models that encompass the spectrum of linear elasticity, hyperelasticity, and plasticity. We predict the size, shape, and location of the internal void/inclusion as well as the elastic modulus of the inclusion. Our general framework can be applied to other inverse problems in different applications that involve unknown material properties and highly deformable geometries, targeting material characterization, quality assurance, and structural design.Nanyang Technological UniversityPublished versionThe work was supported by the Department of Energy PhILMs project DE-SC001954 and OSD/AFOSR MURI grant FA9550-20-1-0358. M.D. was supported by the National Science Foundation (NSF) award 2004556. S.S. was supported by Nanyang Technological University, Singapore, through the Distinguished University Professorship (S.S.)

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano- and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.Comment: 93 pages, 10 figure

G2Φnet:Relating genotype and biomechanical phenotype of tissues with deep learning

Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical phenotype neural network (G2Φnet) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that G2Φnet can infer the biomechanical response while simultaneously ascribing the associated genotype by utilizing limited, noisy, and unstructured experimental data. More broadly, G2Φnet provides a powerful method and a paradigm shift for correlating genotype and biomechanical phenotype quantitatively, promising a better understanding of their interplay in biological tissues

G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning

Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical phenotype neural network (G2Φnet) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that G2Φnet can infer the biomechanical response while simultaneously ascribing the associated genotype by utilizing limited, noisy, and unstructured experimental data. More broadly, G2Φnet provides a powerful method and a paradigm shift for correlating genotype and biomechanical phenotype quantitatively, promising a better understanding of their interplay in biological tissues

A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes

Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases. Standard nonlinear regression in constitutive modeling requires considerable high-quality data and an explicit form of the constitutive model as prior knowledge. By contrast, we propose a novel approach that combines generative deep learning with Bayesian inference to efficiently infer families of constitutive relationships in data-sparse regimes. Inspired by the concept of functional priors, we develop a generative adversarial network (GAN) that incorporates a neural operator as the generator and a fully-connected neural network as the discriminator. The generator takes a vector of noise conditioned on measurement data as input and yields the predicted constitutive relationship, which is scrutinized by the discriminator in the following step. We demonstrate that this framework can accurately estimate means and standard deviations of the constitutive relationships of the murine aorta using data collected either from model-generated synthetic data or ex vivo experiments for mice with genetic deficiencies. In addition, the framework learns priors of constitutive models without explicitly knowing their functional form, providing a new model-agnostic approach to learning hidden constitutive behaviors from data

Influences of thermal crown and wear crown of work roll on strip shape in tandem cold rolling using a novel 3D multi-pass FE model

Thermal crown and wear crown of work roll (TWW) are the main interfering factors of loaded roll gap profile in tandem cold rolling (TCR). However, the effect of TWW on the strip shape is not well understood. This paper presents a quantitative study about the effect of TWW on the strip crown and strip flatness based on a novel 3D multi-pass elastic-plastic finite element (EPFE) model that has been validated by industrial trials in the TCR. The results show that the thermal crown introduces the centre wave and quarter wave, while the wear crown brings in the edge wave and edge-centre coupled wave; the thermal crown has a larger influence efficiency on the quadratic strip shape than the wear crown does, while the wear crown exerts a larger influence efficiency on the quartic strip shape than the thermal crown does. In addition, the influence efficiency of TWW on the strip crown decreases nonlinearly with an increase in strip plastic rigidity from Pass 1 (P1) to Pass 5 (P5). This is the first scientific report on the link between the strip plastic rigidity and the effect of TWW on the strip crown, affording the mathematical models for predicting the influence efficiency of TWW based on the strip plastic rigidity at each pass