Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials.

ABSTRACT: Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimenta

SUBMITTER: Lo Dico G 

PROVIDER: S-EPMC8278955 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature