Towards a sustainable energy future, it is essential to develop new catalysts with improved properties for key catalytic systems such as Haber-Bosch process, water electrolysis and fuel cell. Unfortunately, the current state-of-the-art catalysts still suffer from high cost of noble metals, insufficient catalytic activity and long-term stability. Furthermore, the current strategy to develop new catalysts relies on “trial-and-error” method, which could be time-consuming and inefficient. To tackle this challenge, atomic-level simulations have demonstrated the potential to facilitate catalyst discovery. For the past decades, the simulations have become reasonably accurate so that they can provide useful insights toward the origin of experimentally observed improvements in catalytic properties. In addition, with the exponential increase in computing power, high-throughput catalyst screening has become feasible. More excitingly, recent advances in machine learning have opened the possibility to further accelerate catalyst discovery. Herein, we introduce recent applications and challenges of computation and machine learning for catalyst discovery.

Naturally occurring asbestos (NOA) from disturbance of rocks and soils has been overlooked as a source of exposure that could potentially have a detrimental impact on human health. But, few researches on mineralogical characteristics of NOA occurred in soils have been reported in Korea. Therefore, the objective of this study was to investigate the mineralogical characteristics of NOA occurred in soils at Daero-ri area, Seosan, Chungnam Province, Korea. Sedimentation method was used for particle size separation of the asbestos-containing soils. XRD and PLM analyses were used to characterize mineralogical characteristics and mineral assemblages in soils. SEM-EDS and TEM-EDS analyses were used to characterize mineral morphology and chemical composition. Particle size analyses of the asbestos-containing soils showed they were composed of 26-93% sand, 4-23% silt and 3-70% clay. Soil texture of the soils was mainly sand, sandy loam, sandy clay, and clay. PLM analyses of the soil showed that most of the soil contained asbestiform tremolite and actinolite. The average content of asbestos in the soil was 1.5 wt. %. Therefore, the soil can be classified into asbestos-contaminated soils based on U. S. Environmental Protection Agency classification (content of asbestos in contaminated soil > 1%). Morphologically different types of tremolite such as long fibrous, needle-like, fiber bundle, bladed and prismatic forms co-existed. Prismatic tremolite was dominant in sand fraction and asbestiform tremolite was dominant in silt fraction. This study indicates that the prismatic form of tremolite transform gradually into a fibrous form of tremolite due to soil weathering because tremolite asbestos was mainly existed in silt fraction rather than sand fraction.