The goal of this project is to develop a general predictive modeling framework for calculating mycotoxin incidence in US crop fields. Prediction and control of the most potent carcinogenic mycotoxin, aflatoxin, is a fundamental challenge for US grain industry, poultry producers, and makers of dairy products.

Machine learning models can help to reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, and overcome the shortcomings of linear scaling relations for more complex chemistries.

Exact Bayesian neural network methods are intractable and non-applicable for real-world applications. We propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights.