Abstract: A catalyst development engine (CDE) provides a rapid approach to the rational development of scalable heterogeneous catalysts and of high-performance solid materials. The CDE includes three main components: the testing cycle, the knowledge cycle, and the knowledge repository or database. The knowledge cycle generates working hypotheses relating performance to key catalyst properties via machine learning methods, computation chemistry and micro-kinetic modeling. Such an approach accelerates development and scale-up of new materials without the impediments introduced by conventional combinatorial approaches based on randomly selected materials.

Abstract: A method and system for the development of materials includes a high-throughput data-generation cycle, a knowledge-generation cycle, and a knowledge repository or database. The data-generation cycle generates high-quality data for the virtual and experimental evaluation of new catalytic materials. The knowledge-generation cycle generates working hypotheses, relating performance to catalyst properties, from analysis and modeling of experimental and virtual data and data from literature, from correlations generated from experimental, theoretical, and/or modeling findings, and optionally from computational chemistry modeling investigations, in order to identify better materials.

Abstract: A catalyst development engine (CDE) provides a rapid approach to the rational development of scalable heterogeneous catalysts and of high-performance solid materials. The CDE includes three main components: the testing cycle, the knowledge cycle, and the knowledge repository or database. The knowledge cycle generates working hypotheses relating performance to key catalyst properties via machine learning methods, computation chemistry and micro-kinetic modeling. Such an approach accelerates development and scale- up of new materials without the impediments introduced by conventional combinatorial approaches based on randomly selected materials.

Abstract: A catalyst development engine for providing a rapid approach to the rational development of scalable heterogeneous catalysts and of high-performance solid materials. The catalyst development engine includes three main components: the high-throughput data generation cycle, the knowledge generation cycle, and the knowledge repository or database. The high-throughput data generation cycle rapidly generates high quality data in a high-throughput mode for the virtual and experimental evaluation of new catalytic materials and also generates data on well-characterized systems for use in the knowledge generation cycle. The knowledge generation cycle generates working hypotheses, relating performance to key catalyst properties, which guide the search for better catalysts, and generates fundamental structure-property relationships required in order to select catalysts for further experimental evaluation in the high-throughput cycle.