activity
20182021
most citedMachine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

113 citations · 123 across the 4 of their papers we have counts for

collaborators

8 papers

physics.app-ph2021

Sensitivity of void mediated failure to geometric design features of porous metals

Gregory H. Teichert, Mohammad Khalil, Coleman Alleman +2

Material produced by current metal additive manufacturing processes is susceptible to variable performance due to imprecise control of internal porosity, surface roughness, and con…

physics.bio-ph2021

System inference via field inversion for the spatio-temporal progression of infectious diseases: Studies of COVID-19 in Michigan and Mexico

Zhenlin Wang, Mariana Carrasco Teja, Xiaoxuan Zhang +2

We present an approach to studying and predicting the spatio-temporal progression of infectious diseases. We treat the problem by adopting a partial differential equation (PDE) ver…

cond-mat.mtrl-sci20213 cited

LiCoO phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories

Gregory H. Teichert, Sambit Das, Muratahan Aykol +3

LiO (TM={Ni, Co, Mn}) are promising cathodes for Li-ion batteries, whose electrochemical cycling performance is strongly governed by crystal structure and phase stability…

q-bio.PE20203 cited

System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19

Zhenlin Wang, Xiaoxuan Zhang, Gregory Teichert +2

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…

cond-mat.mtrl-sci20194 cited

Modeling strength and failure variability due to porosity in additively manufactured metals

M. Khalil, G. H. Teichert, C. Alleman +4

To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quanti…

cond-mat.mtrl-sci2019113 cited

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

G. H. Teichert, A. R. Natarajan, A. Van der Ven +1

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 wo…