The Computational Mechanics of Materials Lab at Stanford University is concerned with the development of modern multi-scale methods in the field of complex materials and material systems, combining mechanics with mathematics, computations, and materials science. Our expertise in materials theory and the related development of simulation methods for coupled problems of mechanical and non-mechanical properties in materials undergoing large deformations and fracture allows us to tackle applications in energy storage materials and wearables at the interface of engineered and living systems. A few projects we are currently working on are listed below.

Stretchable electronic materials and devices. The recent technical trend indicates that a number of existing technological fields are reaching their maturity, but many new applications are rapidly emerging. Especially, humanfriendly and sensor related domains are showing rapid growth as they are expected to be instrumental in improving contemporary human life. In the future, the technology development will be driven toward the development of human oriented products, which will require the fabrication of humanfriendly and pragmatic devices. For such devices, high performance and human friendliness will be needed, while at the same time an ergonomic design and practical user interface is a must. Among potential technologies related human life, stretchable electronics technology will allow us to devise a way to attach devices directly onto the skin allowing people to go about daily activities for an extended period of time. Such systems could be used to track health and medical conditions and monitor healing near the skin’s surface. They are in a very nascent stage, but will give us business opportunities for human oriented market in the near future. In collaboration with experimental colleagues at Stanford and industrial partners, in this project we are making significant contributions to future information and communication technology industries and human lifestyle.

Stretchability by design - Understanding mechanical phenomena in microarchitectured soft material systems. Conjugated polymers are considered as the basic material for organic semiconductors, which are used for various electronic material systems and sensing devices for applications in energy, healthcare, biomedical, civil, mechanical, aerospace, and chemical engineering. However, conjugated polymers are not stretchable. While generally flexible, their stretchability is restricted up to a few percent. At large deformations, cracks can deteriorate electronic device performance. This weakness limits their use in industrial applications that require large stretchability and new applications demanding complete flexibility. We perform fundamental research to provide the needed knowledge to understand mechanical phenomena in microarchitectured soft materials such as conjugated polymers to achieve stretchability by design and thereby stable device performance under large stresses. Society will benefit from new methods to predict material properties via mechanics-driven simulations for use in flexible hybrid electronics.

Designing safe and high performing lithium-ion batteries based on a fully coupled multiphysics computational model and machine learning. Lithium-Ion Batteries (LIBs) are important energy storage devices with applications ranging from, portable electronics, to sustainable vehicles, to renewable energy plants. The development of LIBs with higher energy density, and a longer cyclic life is in persist demand. One approach to improve their performance is to replace the existing electrodes and electrolyte with better alternatives, for example, using new reaction-type electrodes with higher capacity, such as Si, S, instead of traditional intercalation-type electrodes with lower capacity, such as graphite or LiCoO_2. However, complex mechanical deformation and different physics are involved during charging-discharging cycles. These deformations cause problems, such as fracture of new electrodes, and eventual capacity loss of LIBs, limiting their practical applications. Extensive experimental investigations have been performed to tackle these challenges by designing various smart nanostructures. However, fundamental relationships between battery cyclic performance, mechanical deformation, and underlying electrode physics are still not well explained from both experimental and numerical points of view. In this research, we aim to address this problem by developing a fully coupled computational model considering the involved mechanics and physics for LIBs with composite electrodes. We are investigating various battery cyclic performance influential factors, such as accumulated plastic deformation, the crack formation, the atomic diffusivity and ionic conductivity. A future goal is then to optimize battery structures with advanced algorithms, and assist in the development of LIBs with optimal performance by using a big data approach and machine learning.

Understanding the impact of mechanical constraints on the dendrite formation in lithium metal anodes. Lithium metal is one of the most appealing anode materials for lithium-ion batteries due to its high specific capacity and its low density and negative electrochemical potential. Utilizing lithium metal in lithium/air or lithium/sulfur batteries can achieve a theoretical specific energy several times higher than in existing lithium-ion batteries, which could boost technology innovations of lithium-ion batteries based applications such as portable electronics, electric vehicles, and energy storage systems. However, dendritic lithium growth during charge/discharge cycles poses a major safety challenge to cells made with lithium metal anodes. In this research, dendritic lithium growth is suppressed by inserting an extra stiff layer in lithium-ion batteries acting as a mechanical constraint. This work will fundamentally improve the understanding of the relationship between mechanical deformation and lithium dendrite growth. New generation workforce will be trained in the use of state-of-art computational tools to conduct multidisciplinary research at the interface between computational mechanics and electrochemistry.

Stable finite element methods for multi-field saddle point principles. Many mixed finite element methods with more than one discretized field suffer from stability issues, characterized by oscillating solutions, if certain requirements are not met. Important applications include incompressible materials, flow problems, plate and shell bending, topology optimization, and electromagnetic problems. While these classical two-field problems and their corresponding stability conditions are well-understood, researchers have become interested in multi-physics applications for advancing manufacturing processes including additive manufacturing, blending of immiscible polymers, advancing approaches towards the modeling of electro-elasticity and magneto-elasticity of smart devices, advancing phase field models for the growth of biological materials or solidification dynamics, as well as creating innovative and smart materials, energy technologies and electronics of radar and electromagnetic warfare. A purely analytical understanding of error bounds and corresponding stability conditions for such multi-field problems is currently non-existing and a systematic finite element design hence barely feasible for a variety of these novel applications. In particular, bounds are obtained on a trial-and-error basis, which jeopardizes the success and predictability of numerical simulations in novel applications. To overcome this challenge, we are working on a generalized framework for finite element stability based on saddle point principles in the context of a new variational inequality and proving sufficiency to satisfy necessary error estimates for a variety of relevant engineering applications and by proposing a generalized numerical testing scheme to allow stability verifications of particular finite element discretizations of multi-field models.

A multi-field mathematical approach to discrete-continuum coupling of cellular mechanisms in biological materials. Biological systems are inherently multiscale and highly complex. Even when behavior is well understood on the subcellular or cellular scales, the quantitative implications of the behavior are often poorly interpreted on the tissue and organ scales. Furthermore, the influence of macroscale change is often poorly accounted for on the cellular scale. To address the disconnect between scales, robust, mathematical based computational models are required to efficiently and productively connect observations and make predictions. Applications of computational models range from understanding and predicting the progression of disease to creating computational tools for the design of more efficient engineered systems such as microbial fuel cells or biological computers.

Last modified Sat, 6 Jul, 2019 at 6:35