machine learning

Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy
Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, …
Megan M. Seeley, Nicholas R. Vaughn, Brennon L. Shanks, Roberta E. Martin, Marcel Konig, Gregory P. Asner
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Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement
Deriving transferable pair potentials from experimental neutron and X-ray scattering measurements has been a longstanding challenge in …
Brennon L. Shanks, J. J. Potoff, M. P. Hoepfner
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Gaussian Process Interatomic Potentials
Gaussian processes (GPs) are a Bayesian regression technique with big upside for machine learning interatomic potentials. Unlike black-box methods like neural networks, GPs are interpretable, uncertainty-aware, and allow for the integration of physics-informed behavior through the use of carefully designed Bayesian priors.
Brennon L. Shanks
Gaussian Process Interatomic Potentials