Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties

Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from an accuracy and a computational cost perspective, complicating high throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models to predict the excited state properties of photoactive iridium complexes. We use experimental data of 1,380 iridium complexes to train and evaluate the ML models and identify the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional theory tight binding calculations. Using these models, we predict the three excited state properties considered, mean emission energy of phosphorescence, excited state lifetime, and emission spectral integral, with accuracy competitive with or superseding TDDFT. We conduct feature importance analysis to identify which iridium complex attributes govern excited state properties and we validate these trends with explicit examples. As a demonstration of how our ML models can be used for HTVS and the acceleration of chemical discovery, we curate a set of novel hypothetical iridium complexes and identify promising ligands for the design of new phosphors

Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery

Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets like open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (ca. 1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the periodic table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the effective nuclear charge alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data is limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the periodic table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the periodic table, a property we expect to be broadly useful for other materials domains

Pengembangan Konsep Desain dan Fabrikasi Mesin Penyortir Buah Duku (Lansium Parasiticum)

Pengembangan konsep desain mesin penyortir buah duku (Lansium Parasiticum) dilakukan menggunakan metode Five Step Method. Diperoleh tiga konsep mesin yang kemudian dipilih satu dari tiga konsep terbaik menggunakan metode product champion dilanjutkan dengan pembuatan desain 3D dan Finite Element Analysis (FEA) menggunakan software autodesk inventor 2017. Analisis FEA menunjukkan nilai von Misses stress sebesar 18 MPa bernilai lebih kecil dibanding yield strength material penyusun rangka sebesar 207 MPa, displacement yang terjadi sebesar 0,99 mm dengan nilai safety factor 15. Fabrikasi dan pengujian mesin menunjukkan nilai persentase keberhasilan proses penyortiran pada mesin lebih dari 85% dengan kapasitas sortir 400 kg/jam

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

The design of selective and active C–H activation catalysts for direct methane-to-methanol conversion is challenging. Bioinspired complexes that form high valent metal-oxo intermediates capable of hydrogen abstraction and rebound hydroxylation are promising candidates. This promise has made them a target for computational high-throughput screening, typically simplified through the use of linear free energy relationships (LFERs). However, their mid-row transition-metal centers have numerous accessible spin and oxidation states that increase the combinatorial scale of design efforts. Here, we carry out a computational design screen of over 2,500 mid-row 3d transition-metal complexes with four metals in numerous spin and oxidation states. We demonstrate the importance of spin/oxidation state in dictating design principles, limiting the generalization of strategies derived for widely studied high-spin Fe(II) catalysts to other metals or spin/oxidation states. Combined assessment of the effect of ligand field tuning on reaction step energetics and on the identity of the ground state allows us to propose refined design strategies for spin-allowed methane-to-methanol catalysis. We observed weak coupling of energetics and design principles between reaction steps (e.g., oxo-formation vs methanol release), meaning that LFERs do not generalize across our larger catalyst set. To rationalize relative reactivity in known catalysts, we instead compute independent reaction energies and propose strategies for further improvements in catalyst design

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

© 2020 American Chemical Society. The design of selective and active C−H activation catalysts for direct methane-to-methanol conversion is challenging. Bioinspired complexes that form high-valent metal−oxo intermediates capable of hydrogen abstraction and rebound hydroxylation are promising candidates. This promise has made them a target for computational high-throughput screening, typically simplified through the use of linear free energy relationships (LFERs). However, their mid-row transition-metal centers have numerous accessible spin and oxidation states that increase the combinatorial scale of design efforts. Here, we carry out a computational design screen of over 2500 mid-row 3d transition-metal complexes with four metals in numerous spin and oxidation states. We demonstrate the importance of spin/oxidation state in dictating design principles, limiting the generalization of strategies derived for widely studied high-spin Fe(II) catalysts to other metals or spin/oxidation states. Combined assessment of the effect of ligand-field tuning on reaction step energetics and on the identity of the ground state allows us to propose refined design strategies for spin-allowed methane-to-methanol catalysis. We observe weak coupling of energetics and design principles between reaction steps (e.g., oxo formation vs methanol release), meaning that LFERs do not generalize across our larger catalyst set. To rationalize relative reactivity in known catalysts, we instead compute independent reaction energies and propose strategies for further improvements in catalyst design

Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes

We survey over 230,000 crystallized mononuclear transition metal complexes (TMCs) to identify trends in preferred geometric structure and metal coordination. While we observe d-filling to influence coordination preference, with late TMCs preferring lower coordination number, we also note exceptions. We also observe that 4d and 5d transition metals and 3p-coordinating ligands are systematically undersampled. For the roughly one third of the set of mononuclear TMCs that are octahedral, analysis of the 67 symmetry classes of their ligand environments reveals that complexes most commonly contain monodentate ligands that may likely be removable to leave an open site amenable to catalysis. Due to their frequent use in transition metal catalysts, we analyze trends in coordination by tetradentate ligands in terms of the capacity to support multiple metals and the variability of coordination geometry. We identify promising tetradentate ligands that co-occur in crystallized complexes with labile monodentate ligands, indicating their ability to generate reactive sites. Literature mining suggests that many of these tetradentate ligands are untapped as ligands in catalytic complexes, motivating proposal of a promising octa-functionalized porphyrin in this set as a candidate ligand for catalysis

The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

High-throughput computational catalyst studies are typically carried out using density functional theory (DFT) with a single, approximate exchange-correlation functional. In open shell transition metal complexes (TMCs) that are promising for challenging reactions (e.g., C–H activation), the predictive power of DFT has been challenged, and properties are known to be strongly dependent on the admixture of Hartree-Fock (HF) exchange. We carry out a large-scale study of the effect of HF exchange on the predicted catalytic properties of over 1,200 mid-row (i.e., Cr, Mn, Fe, Co) 3d TMCs for direct methane-to-methanol conversion. Reaction energetic sensitivities across this set depend both on the catalytic rearrangement and ligand chemistry of the catalyst. These differences in sensitivities change both the absolute energetics predicted for a catalyst and its relative performance. Previous observations of the poor performance of global linear free energy relationships (LFERs) hold with both semi-local DFT widely employed in heterogeneous catalysis and hybrid DFT. Narrower metal/oxidation/spin-state specific LFERs perform better and are less sensitive to HF exchange than absolute reaction energetics, except in the case of some intermediate/high-spin states. Importantly, the interplay between spin-state dependent reaction energetics and exchange effects on spin-state ordering means that the choice of DFT functional strongly influences whether the minimum energy pathway is spin-conserved. Despite these caveats, LFERs involving catalysts that can be expected to have closed shell intermediates and low-spin ground states retain significant predictive power.</p