scholarly journals Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

2018 ◽  
Author(s):  
Aditya Nandy ◽  
Chenru Duan ◽  
Jon Paul Janet ◽  
Stefan Gugler ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
Open Shell ◽  
Frontier Molecular Orbital ◽  
Data Set ◽  
Homo Level ◽  
Homo Lumo

<p>Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. Here, we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO-LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO-LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20-30 features. Analysis of the essential descriptors for HOMO and HOMO-LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveals the paramount importance of non-local, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO-LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require. </p>

scholarly journals Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

2018 ◽  
Author(s):  
Aditya Nandy ◽  
Chenru Duan ◽  
Jon Paul Janet ◽  
Stefan Gugler ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
Open Shell ◽  
Frontier Molecular Orbital ◽  
Data Set ◽  
Homo Level ◽  
Homo Lumo

<p>Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. Here, we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO-LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO-LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20-30 features. Analysis of the essential descriptors for HOMO and HOMO-LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveals the paramount importance of non-local, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO-LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require. </p>

scholarly journals Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

2020 ◽  
Author(s):  
Fang Liu ◽  
Chenru Duan ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
High Throughput ◽  
Strong Correlation ◽  
High Throughput Screening ◽  
Density Functional ◽  
Chemical Space ◽  
Occupation Number ◽  
Open Shell ◽  
Homo Lumo

<p>Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3<i>d</i> transition metals that can be expected to have strong multi-reference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO–LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO–LUMO gaps and FON-based diagnostics reveals differences in metal- and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character. </p>

scholarly journals Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

2020 ◽  
Author(s):  
Fang Liu ◽  
Chenru Duan ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
High Throughput ◽  
Strong Correlation ◽  
High Throughput Screening ◽  
Density Functional ◽  
Chemical Space ◽  
Occupation Number ◽  
Open Shell ◽  
Homo Lumo

<p>Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3<i>d</i> transition metals that can be expected to have strong multi-reference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO–LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO–LUMO gaps and FON-based diagnostics reveals differences in metal- and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character. </p>

scholarly journals Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

2020 ◽  
Author(s):  
Fang Liu ◽  
Chenru Duan ◽  
Heather Kulik
Keyword(s):  
Machine Learning ◽  
Transition Metal ◽  
High Throughput ◽  
Strong Correlation ◽  
High Throughput Screening ◽  
Density Functional ◽  
Chemical Space ◽  
Occupation Number ◽  
Open Shell ◽  
Homo Lumo

<p>Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many materials targets, such as those containing open-shell, 3<i>d</i> transition metals that can be expected to have strong multireference (MR) character. For DFT workflows to be predictive, we must incorporate automated, low cost methods that can distinguish the regions of chemical space where DFT should be applied and where it should not. We curate over 4,800 open shell transition metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO-LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. We train independent machine learning (ML) models to predict HOMO-LUMO gaps and FON-based diagnostics. ML model analysis reveals differences in metal- and ligand-sensitivity of the two quantities, suggesting opportunities to minimize MR character while tailoring the gap. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and discovering small HOMO-LUMO gap complexes with low MR character.</p>

Density Functional Calculations of EPR Parameter g Tensors of Some Transition Metal Complexes

2011 ◽  
Vol 2 (2) ◽  
pp. 139-141
Author(s):  
Vinita Prajapati ◽  
◽  
P.L.Verma P.L.Verma ◽  
Dhirendra Prajapati ◽  
B.K.Gupta B.K.Gupta
Keyword(s):  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional Calculations ◽  
Density Functional

scholarly journals 1,3,5-Triaza-7-Phosphaadamantane (PTA) as a 31P NMR Probe for Organometallic Transition Metal Complexes in Solution

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1390 ◽  
Author(s):  
Ilya G. Shenderovich
Keyword(s):  
Chemical Shift ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
Molecular Structures ◽  
Basis Sets ◽  
Functional Theory ◽  
Non Covalent Interactions ◽  
Covalent Interactions

Due to the rigid structure of 1,3,5-triaza-7-phosphaadamantane (PTA), its 31P chemical shift solely depends on non-covalent interactions in which the molecule is involved. The maximum range of change caused by the most common of these, hydrogen bonding, is only 6 ppm, because the active site is one of the PTA nitrogen atoms. In contrast, when the PTA phosphorus atom is coordinated to a metal, the range of change exceeds 100 ppm. This feature can be used to support or reject specific structural models of organometallic transition metal complexes in solution by comparing the experimental and Density Functional Theory (DFT) calculated values of this 31P chemical shift. This approach has been tested on a variety of the metals of groups 8–12 and molecular structures. General recommendations for appropriate basis sets are reported.

CO2 reduction catalysis by tunable square-planar transition-metal complexes: a theoretical investigation using nitrogen-substituted carbon nanotube models

2017 ◽  
Vol 19 (43) ◽  
pp. 29068-29076 ◽  
Author(s):  
Yu-Te Chan ◽  
Ming-Kang Tsai
Keyword(s):  
Density Functional Theory ◽  
Carbon Nanotube ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Density Functional ◽  
Co2 Reduction ◽  
Functional Theory ◽  
Square Planar ◽  
Planar Transition

The CO2 reduction capabilities of transition-metal-chelated nitrogen-substituted carbon nanotube models (TM-4N2v-CNT, TM = Fe, Ru, Os, Co, Rh, Ir, Ni, Pt or Cu) are characterized by density functional theory.

Sign in / Sign up

Export Citation Format

Share Document