Accelerated screening of active sites on biochar for catalysis and adsorption via multidimensional fingerprint factor descriptors.

[Display omitted] • Combining machine learning and physical insights to study biochar active sites. • Internal and external characters constructed multidimensional fingerprint descriptor. • C-C/C=C, C=O and defect sites were all predicted with over 90 % accuracy. • The descriptors enable high-throughput screening of preparation parameters. Highly active biochar has great application potential in heterogeneous catalysis and adsorptive processes. The complexity of carbonization process makes it difficult to construct target active sites. This work put forward a reactive descriptor based on pyrolysis parameters and intrinsic composition of biomass. Results show that the model showed better predictive performance for C-C/C=C (R2 = 0.85), C=O (R2 = 0.85) and defect (R2 = 0.91) sites. The SHapley Additive exPlanation analysis shows that the pyrolysis parameters and the higher heating values are equally important for the active sites. The predictive performance and guiding role of the descriptor were validated by experiments. The descriptors proposed in this study integrated significant advantages of simplicity and easy accessibility, which would break the bottleneck of accurate construction of active sites and provide a theoretical basis for high-value resource utilization of biomass. [ABSTRACT FROM AUTHOR]