Examination of genetic programming paradigm for high-throughput experimentation and heterogeneous catalysis

Abstract

The strong feature dependencies that exist in catalyst description do not permit using common algorithms while not loosing crucial information. Data treatments are restricted by the form of input data making the full use of the experimental information impossible, confining the experimentation studies, and reducing one of the primary goals of HTE: to enlarge the search space. Consequently, an advanced representation of the catalytic data supporting the intrinsic complexity of heterogeneous catalyst data structure is proposed. Likewise, an optimization strategy that can manipulate efficiently such data type, permitting a valuable connection between algorithms, high-throughput (HT) apparatus, and databases, is depicted. Such a new methodology enables the integration of domain knowledge through its configuration considering the study to be investigated. For the first time in heterogeneous catalysis, a conceptual examination of genetic programming (GP) is achieved.