Building a Library for Catalysts Research Using High‐Throughput Approaches

Abstract

The development of new catalysts is an urgent task in chemistry research because of the increasing demands for higher productivity and efficiency in modern industrial chemistry. High-throughput (HT) technologies have emerged as powerful accelerators for the discovery of new catalytic materials. Generally, HT techniques involve HT computational screening of catalytic materials and HT experimental approaches. Significant progress has been made in discovering and optimizing catalysts, which demonstrates the speed and economic advantages of HT methods in a matter of process within days and even hours, in contrast with traditional time-consuming trial and error experiments. From this perspective, the key processes of HT technologies for building a catalysts library are highlighted, including the virtual lab construction based on informatics tools and experimental data lab involving the following processes: 1) design of experiments; 2) reactants prepared manually or automatically; 3) constructing reactor that can well control the variations including materials diversity and reaction conditions (e.g., temperature and reaction time); 4) parallel characterization of fabricated catalysts. Moreover, the correlation between virtual lab and experimental data is discussed to inspire explorations in this field. Furthermore, several remaining challenges of HT technologies accompanied by possible research directions are presented to promote efforts in catalysts library creation.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

“…Miniaturization of the batch size is advantageous for expensive materials and enables rapid screening of a large number of samples in high-throughput experiments, but with the limitations discussed above in terms of transport kinetics. 11,17,21,23,26 Furthermore, the density of the sample libraries can be increased in this way, i.e., more samples can be prepared and thus the step size can be reduced when varying a parameter. 21 However, scaling up synthesis approaches is difficult and not successful in every case, so that some hits from high-throughput studies will never have a chance of technical implementation.…”

“…11,17,21,23,26 Furthermore, the density of the sample libraries can be increased in this way, i.e., more samples can be prepared and thus the step size can be reduced when varying a parameter. 21 However, scaling up synthesis approaches is difficult and not successful in every case, so that some hits from high-throughput studies will never have a chance of technical implementation. So, these experiments and the corresponding investments in materials and working time will not pay off.…”

“…Progress in this field has been summarized in numerous review articles. [10][11][12][13]16,17,19,21,[94][95][96][97][98][99] Libraries have been built with a large number of catalysts for different reactions using both high-speed array approaches and classical parallel synthesis and testing strategies with catalyst masses in mg or g scale. Initially, methods from combinatorial drug discovery were adapted for the design of the starting library, such as the split & pool method, which involves the combinatorial permutation of parameters and thus enables the generation of arbitrary combinations of variables in large parameter spaces.…”

“…In general, the testing of catalysts is largely automated in many laboratories or commercial solutions are available. 13,21 Therefore, the focus in the next section will be on developments for automated synthesis and characterization of catalysts. A shortcoming of many highthroughput screening experiments for functional analysis of catalysts, however, is that the reaction parameter space is not sufficiently large and its structure not diverse enough.…”

“…16 Currently, the field is rapidly evolving towards enhanced integration of highthroughput experimentation and data-driven science. [18][19][20][21] The autonomous discovery of new catalysts, inorganic compounds and functional materials by robots without the intervention of the scientist is seen as an exciting prospect for advancing catalysis research and materials science. 20,[22][23][24][25][26][27][28][29][30][31] The beneficial use of a toolkit like this requires full integration of unit operations in catalyst development such as automated synthesis of catalyst libraries, (highthroughput) characterization and testing, data analysis with machine learning algorithms, 18,20,22,[32][33][34] and design of new experiments by active learning and automated reasoning 20 in closed feedback loops.…”