Today we experience rapid development of a new field of chemical science -digital molecular technology (DMT). It is evident by the increasing number of publications in which synthetic and computational chemistry, or materials development are mixed with machine learning (ML), robotics and artificial intelligence (AI), for example in Refs. . DMT is promising to significantly expand the accessible chemical space and to reduce the price of access to new functional molecules and materials. These are to be achieved through enhanced capabilities in molecules and reactions discovery, and in process development and optimization. The key component of the new DMT methods is the increased volume and quality of chemical data obtained both through data mining, computational chemistry tools and robotic experiments, as lack of data renders ML and AI methods inaccurate and not very useful . Here we ask a question, is it possible to make use of the increased availability of experimental data to enhance our capability in inferring physical knowledge from data by means of algorithmic research? This is driven by the desire to develop predictive models of complex chemical processes, which could be used in optimal control. The approach that we seek to develop should not be based on selecting functional forms from a pre-defined set. This has already been demonstrated within DMT, for example, in selecting suitable kinetic expressions within an automated selfoptimization system . Our own interest is in the methods that are inherently not restricted to only the known functional forms and are, therefore, potentially capable of discovering new phenomena.