Materials synthesis and optimization are tedious and labor-intensive. Consequently, we need a paradigm shift toward the prediction of materials performance without the necessity to experimentally optimize them.

Since crop species have been domesticated to fit it to humanity’s needs, their genomes hold the secrets to ancient and modern agricultural practices, which can serve as an informative reference for future breeding practices. Now the world population is expected to reach 10 billion, and the agricultural community must manage to ensure a safe and sustainable food supply for our future generations.

Semiconductor nanotechnologies are among the most consequential ones for almost every aspect of modern society. Conventionally, the design towards desired technological specifications is by no means trivial. It comprises interactions of simulations and experiments that often translate into lengthy periods and high costs.

Multiphase flows in geological formation are often needed to be solved numerically in reservoir engineering, as the subsurface oil and gas reservoirs typically contain multiple phases and multiple chemical species with complex phase behaviors.

Aim: Bridge first-principles physical-chemical models with machine learning models.

Property Prediction: ANNs trained on quantum descriptors
Reactor Prediction and Control:Reinforcement learning for chemical process control
Stochastic Event Prediction:Deep learning to predict rare combustion events

Investigator: