The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this work, we present an Integrable Deep Neural Network (IDNN) that can be trained to derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover an accurate representation of the free energy. The IDNN is demonstrated by training to the chemical potential data of a binary alloy with B2 ordering. The resulting DNN representation of the free energy is used in a mesoscopic, phase field simulation and found to predict the appropriate formation of antiphase boundaries in the material. In contrast, a B-spline representation of the same data failed to resolve the physics of the system with sufficient fidelity to resolve the antiphase boundaries. Since the fine scale physics harbors complexity that emerges through the free energy in coarser-grained descriptions, the IDNN represents a framework for scale bridging in materials systems.

This paper explores the deflection and buckling of fixed-guided beams used in compliant mechanisms. The paper’s main contributions include the addition of an axial deflection model to existing beam bending models, the exploration of the deflection domain of a fixed-guided beam, and the demonstration that nonlinear finite element models typically incorrectly predict a beam’s buckling mode unless unrealistic constraints are placed on the beam. It uses an analytical model for predicting the reaction forces, moments, and buckling modes of a fixed-guided beam undergoing large deflections. The model for the bending behavior of the beam is found using elliptic integrals. A model for the axial deflection of the buckling beam is also developed. These two models are combined to predict the performance of a beam undergoing large deflections including higher order buckling modes. The force versus displacement predictions of the model are compared to the experimental force versus deflection data of a bistable mechanism and a thermomechanical in-plane microactuator (TIM). The combined models show good agreement with the force versus deflection data for each device.

We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al. in Comput Methods Appl Mech Eng 356:44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.

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This paper presents a biological microelectromechanical system for injecting foreign particles into thousands of cells simultaneously. The system inserts an array of microneedles into a monolayer of cells, and the foreign particles enter the cells by diffusion. The needle array is fabricated using a series of deep reactive ion etches and produces about 4 million needles that average 1 μm in diameter and 8 μm in length with 10 μm spacing. The insertion of the needles is controlled through a compliant suspension. The compliant suspension was designed to provide for needle motion into the cells while restraining rotations or transverse motions that could result in tearing of the cell membranes. Testing was performed using propidium iodide, a membrane impermeable dye, injected into HeLa cells. Average cell survivability was found to be 97.7%, and up to 97.9% of the surviving cells received the propidium iodide.

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The Center for Predictive Integrated Structural Materials Science (PRISMS Center) is creating a unique framework for accelerated predictive materials science and rapid insertion of the latest scientific knowledge into next-generation ICME tools. There are three key elements of this framework. The first is a suite of high-performance, open-source integrated multi-scale computational tools for predicting microstructural evolution and mechanical behavior of structural metals. Specific modules include statistical mechanics, phase field, crystal plasticity simulation and real-space DFT codes. The second is the Materials Commons, a collaboration platform and information repository for the materials community. The third element of the PRISMS framework is a set of integrated scientific ''Use Cases'' in which these computational methods are linked with experiments to demonstrate the ability for improving our predictive understanding of magnesium alloys, in particular, the influence of microstructure on monotonic and cyclic mechanical behavior. This paper reviews progress toward these goals and future plans.