Search Results - "Teichert, G.H."

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions by Teichert, G.H., Natarajan, A.R., Van der Ven, A., Garikipati, K.

“…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…”
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Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order–disorder transitions in LixCoO2 by Shojaei, M. Faghih, Holber, J., Das, S., Teichert, G.H., Mueller, T., Hung, L., Gavini, V., Garikipati, K.

“…LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for Li-ion batteries, whose performance is strongly governed by Li composition-dependent…”
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Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order–disorder transitions in Li x CoO 2 by Shojaei, M. Faghih, Holber, J., Das, S., Teichert, G.H., Mueller, T., Hung, L., Gavini, V., Garikipati, K.

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Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys by Teichert, G.H., Natarajan, A.R., Van der Ven, A., Garikipati, K.

“…The free energy plays a fundamental role in theories of phase transformations and microstructure evolution. It encodes the thermodynamic coupling between…”
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mechanoChemML: A software library for machine learning in computational materials physics by Zhang, X., Teichert, G.H., Wang, Z., Duschenes, M., Srivastava, S., Livingston, E., Holber, J., Shojaei, M. Faghih, Sundararajan, A., Garikipati, K.

“…We present mechanoChemML, a machine learning software library for computational materials physics. mechanoChemML is designed to function as an interface…”
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Modeling strength and failure variability due to porosity in additively manufactured metals by Khalil, M., Teichert, G.H., Alleman, C., Heckman, N.M., Jones, R.E., Garikipati, K., Boyce, B.L.

“…To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have…”
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A graph theoretic framework for representation, exploration and analysis on computed states of physical systems by Banerjee, R., Sagiyama, K., Teichert, G.H., Garikipati, K.

“…A graph theoretic perspective is taken for a range of phenomena in continuum physics in order to develop representations for analysis of large scale,…”
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Sensitivity of void mediated failure to geometric design features of porous metals by Teichert, G.H., Khalil, M., Alleman, C., Garikipati, K., Jones, R.E.

“…Material produced by current metal additive manufacturing processes is susceptible to variable performance due to imprecise control of internal porosity,…”
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