Chemical/material formulations are characterized by large numbers of components but also a diversity of interacting and competing forces that determine system properties, making them difficult to model. Most formulated products are complex, but even after decades of development they are still designed using a combination of experience and heuristics. Hierarchical machine learning (HML) was developed as a tool for designing these systems based on the small datasets that are common in research and development.

HML is both an algorithm for designing and a process for understanding complex chemical/material systems. The goal in machine learning is to generate a response surface that accurately relates the input variables with the output responses of a system. HML generates a second response surface that is parameterized by latent variables that represent the underlying forces and interactions. These latent variables are obtained from theoretical or empirical models or surrogate physical measurements and represent conceptual understanding of the system. However, in contrast with heuristic approaches that are often reductionist, HML retains the full complexity of the forces and interactions that govern the system properties. It provides a path to both optimized design based on input parameters and their constraints as well as conceptual understanding of how these systems work, thus serving as a tool for applied research and development.

Ansatz AI has applied HML to a diversity of industrial technologies with corporate clients, ranging from molecular engineering of polymeric elastomers, lubricants, and dielectrics to liquid formulations of paints, coatings and personal care products. In these technologies, the goals have ranged from increasing performance to minimizing costs to shifting to renewable feedstocks. This seminar will explain the HML approach to modeling complex chemical/material formulations and highlight some of the solutions that it has provided.