Machine Learning of a Double-Valued Function
We describe an algorithm for learning objects in quantum chemistry such as nonadiabatic coupling vectors, which are double-valued near concial intersections.
Some questions have more than one answer, such as, "what is the square root of 4?". Both 2 and -2 are equally good answers. Functions which return two answers are called double-valued.
In quantum chemistry, nonadiabatic coupling vectors (which mediate transitions between different electronic states) can be double valued in the presence of conical intersections. This makes learning them directly with machine-learning tools very difficult.
In our new paper, we describe an algorithm that transforms this task into a standard machine-learning problem.