On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach
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 gen...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8264297 |
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| _version_ | 1832564619107893248 |
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| author | Jianjun Wang Mohammad Mahdi Molla Jafari |
| author_facet | Jianjun Wang Mohammad Mahdi Molla Jafari |
| author_sort | Jianjun Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8346ef32abef4774ade58411771f9905 |
| institution | Kabale University |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-8346ef32abef4774ade58411771f99052025-02-03T01:10:37ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/8264297On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR ApproachJianjun Wang0Mohammad Mahdi Molla Jafari1School of Petroleum and PetrochemicalDepartment of Petroleum EngineeringMaterials 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.http://dx.doi.org/10.1155/2022/8264297 |
| spellingShingle | Jianjun Wang Mohammad Mahdi Molla Jafari On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach International Journal of Chemical Engineering |
| title | On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach |
| title_full | On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach |
| title_fullStr | On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach |
| title_full_unstemmed | On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach |
| title_short | On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach |
| title_sort | on the investigation of effective factors on electronic structure properties of transition metal complexes robust modeling using gpr approach |
| url | http://dx.doi.org/10.1155/2022/8264297 |
| work_keys_str_mv | AT jianjunwang ontheinvestigationofeffectivefactorsonelectronicstructurepropertiesoftransitionmetalcomplexesrobustmodelingusinggprapproach AT mohammadmahdimollajafari ontheinvestigationofeffectivefactorsonelectronicstructurepropertiesoftransitionmetalcomplexesrobustmodelingusinggprapproach |