Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg⁰ sensing materials

The detrimental impact of urban airborne Hg⁰ from fossil fuel utilization has necessitated the discovery and development of Hg⁰ sensing materials for effective Hg⁰ detection and mitigation of the pollutant. Earlier studies have hypothetically and experimentally supported 2-dimensional transition-metal dichalcogenides (2D TMDCs), particularly MoS₂ to have excellent performance for Hg⁰ removal. However, the potential of other TMDCs is yet to be investigated for Hg⁰ sensor application. In this study, a total of 28 transition metals within periods 4–6 of the periodic table, excluding the lanthanides series, were examined. To ensure proper data management flow, a high-throughput data mining approach with integrated machine learning and cheminformatics simulation approaches is developed. The systemic approach integrates the Pymatgen, Factsage, Aflow and density functional theory simulation tools for accelerated discovery of suitable TMDCs from raw data via the chemical vapour reaction route. Predicted results showed that TiS₂, NiS₂, ZrS₂, MoS₂, PdS₂ and WS₂ exhibited TMDCs characteristics. Furthermore, first-principles calculation shows Hg-uptake capacity is in the order NiS₂ > PdS₂ > TiS₂ > ZrS₂ > WS₂ > MoS₂, while Hg sensing response is in the order PdS₂ > MoS₂ > WS₂ > ZrS₂ > NiS₂ > TiS₂. Accordingly, PdS₂ depicted to be the most suitable TMDCs for airborne Hg⁰ sensor application. The proposed systemic approach is an initial platform for materials discovery using integrated machine learning approaches and is well-suited for the screening and the discovery of new materials based on component-oriented structures.