Abstract
Materials discovery is usually done using high-throughput computational screening. The use of costly and complex direct density functional theory (DFT) simulation methods has been commonly used to determine subtle trends in spin-state ordering and inorganic bonding of inorganic materials and, in general, to predict the electronic structure properties of transition metal complexes. A Gaussian process regression (GPR) framework consisting of four kernel functions is introduced for spin-state splitting estimation through inorganic chemistry-appropriate empirical inputs. To this end, the present study reviewed an extensive range of data values from earlier works. According to statistical analysis, the GPR model showed very good performance. The coefficients of determination were calculated to be 0.986 for the exponential and Matern kernel functions, suggesting the highest predictive power of these methods. Moreover, the sensitivity of output to inputs was measured. Artificial intelligence (AI) helped accurately predict the target values through various input ranges.
Highlights
Novel compounds, catalysts [1], and materials [2] are routinely discovered via high-throughput computer screening [3, 4]
Numerous screening and recognition experiments still rely on first-principles modeling, but the increased computational expense simulation means that only a narrow subset of the chemical domain can be explored [5, 6]
Computational chemists have recently discovered a broad range of uses for artificial neural networks (ANNs) [8,9,10]. e versatility of machine-learning methods to potential energy surfaces and, force field simulations were first recognized
Summary
Catalysts [1], and materials [2] are routinely discovered via high-throughput computer screening [3, 4]. Numerous screening and recognition experiments still rely on first-principles modeling, but the increased computational expense simulation means that only a narrow subset of the chemical domain can be explored [5, 6]. Molecular or heterogeneous catalyst and substance exploration have lately been studied in exchangecorrelation functional advancement [8, 14], common Schrodinger equation strategies [15], functional hypothesis for orbital-free density [16, 17], numerous body expansions [18], dynamics velocity [19, 20], and band-gap estimation [21, 22] among others. Ere are just a handful of force fields [29] for transition metal combinations covering the whole spectrum of inorganic chemical bonding interactions [30]. More rigorous construction of qualifiers is needed to accurately anticipate the characteristics of open-shell transition metal combinations since spin state and coordination setting influence binding [31]
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