Simulation of Scientific Experiments with Generative Models

Abstract

Lab experiments are a crucial part of research in natural sciences. High-throughput screening is leveraged to generate hypotheses, by evaluating a wide range of experimental parameter values and accumulating a wealth of data on the corresponding experimental outcomes. The data is subsequently analyzed to design new rounds of experiments. While discriminative models have previously proven useful for screening data analytics, they do not account for randomness inherent to lab experiments, and do not have the capacity to capture the potentially high-dimensional relationship between the experiment input parameters and outcomes. Instead, we take a data-driven simulation perspective on the problem. Inspired by biomaterials research experiments, we consider a case where both the input parameter space and the outcome space have a high-dimensional (image) representation. We propose a deep generative model that serves simultaneously as a simulation model of the experiment, i.e. allows to generate potential outcomes conditioned on the experiment input, and as a tool for inverse design, i.e. generating instances of inputs that could lead to a given experiment outcome. A proof-of-concept evaluation on a synthetic dataset shows that the model is able to learn the embedded relationship between the properties of the input and of the output in a probabilistic manner and allows for experiment simulation and design application scenarios.