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This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

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Trichloroethylene (TCE) is a common environmental pollutant associated with adverse reproductive outcomes in humans. TCE intoxication occurs primarily through its biotransformation to bioactive metabolites, including S-(1,2-dichlorovinyl)-l-cysteine (DCVC). TCE induces oxidative stress and inflammation in the liver and kidney. Although the placenta is capable of xenobiotic metabolism and oxidative stress and inflammation in placenta have been associated with adverse pregnancy outcomes, TCE toxicity in the placenta remains poorly understood. We determined the effects of DCVC by using the human extravillous trophoblast cell line HTR-8/SVneo. Exposure to 10 and 20 μM DCVC for 10 h increased reactive oxygen species (ROS) as measured by carboxydichlorofluorescein fluorescence. Moreover, 10 and 20 μM DCVC increased mRNA expression and release of interleukin-6 (IL-6) after 24-h exposure, and these responses were inhibited by the cysteine conjugate beta-lyase inhibitor aminooxyacetic acid and by treatments with antioxidants (alpha-tocopherol and deferoxamine), suggesting that DCVC-stimulated IL-6 release in HTR-8/SVneo cells is dependent on beta-lyase metabolic activation and increased generation of ROS. HTR-8/SVneo cells exhibited decreased mitochondrial membrane potential at 5, 10, and 20 μM DCVC at 5, 10, and 24 h, showing that DCVC induces mitochondrial dysfunction in HTR-8/Svneo cells. The present study demonstrates that DCVC stimulated ROS generation in the human placental cell line HTR-8/SVneo and provides new evidence of mechanistic linkage between DCVC-stimulated ROS and increase in proinflammatory cytokine IL-6. Because abnormal activation of cytokines can disrupt trophoblast functions necessary for placental development and successful pregnancy, follow-up investigations relating these findings to physiologic outcomes are warranted.

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