Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning

• URL: http://arxiv.org/abs/2107.14280v1
• Date: Thu, 29 Jul 2021 19:01:56 GMT
• Title: Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning
• Authors: Michael G. Taylor, Aditya Nandy, Connie C. Lu, and Heather J. Kulik
• Abstract summary: We train a regression model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. Our work provides guidance for rational bimetallic design, suggesting that properties including the formal ratio should be transferable from one period to another.
• Score: 0.856335408411906
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rational tailoring of transition metal complexes is necessary to address outstanding challenges in energy utilization and storage. Heterobimetallic transition metal complexes that exhibit metal-metal bonding in stacked "double decker" ligand structures are an emerging, attractive platform for catalysis, but their properties are challenging to predict prior to laborious synthetic efforts. We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for rational bimetallic complex design. We tailor graph-based representations of the metal-local environment for these heterobimetallic complexes for use in training of multiple linear regression and kernel ridge regression (KRR) models. Focusing on oxidation potentials, we obtain a set of 28 experimentally characterized complexes to develop a multiple linear regression model. On this training set, we achieve good accuracy (mean absolute error, MAE, of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We trained a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of complexes. Our work provides guidance for rational bimetallic design, suggesting that properties including the formal shortness ratio should be transferable from one period to another.

Related papers

• Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes [0.0]
  We present a modification to autocorrelation for machine learning various spin state dependent properties of octa transition metal complexes (TMCs) The new strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. Because the new approach incorporates insights from electronic structure theory, these models exhibit systematic generalization from homoleptic to heteroleptic complexes.
  arXiv  Detail & Related papers  (2024-10-12T21:54:22Z)
• Virtual Foundry Graphnet for Metal Sintering Deformation Prediction [87.44136293798721]
  We use a graph-based deep learning approach to predict the part deformation. Running a well-trained Metal Sintering inferencing engine only takes a range of seconds to obtain the final sintering deformation value.
  arXiv  Detail & Related papers  (2024-04-17T21:11:12Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Quantum Dynamics of Photoactive Transition Metal Complexes. A Case Study of Model Reduction [0.0]
  Quantum dynamics simulations using quantum chemically determined model Hamiltonians may provide such details. We performed quantum dynamics simulations for a recently studied iron(II) homoleptic complex.
  arXiv  Detail & Related papers  (2023-05-12T10:24:59Z)
• Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials [9.457954280246286]
  We train Machine Learning Interaction Potentials (MLIPs) on Density Functional Theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab-initio results. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants and various surface properties inaccessible using DFT. We establish that there exists a Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets.
  arXiv  Detail & Related papers  (2023-04-24T20:09:07Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties [0.4306143768014156]
  Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. We leverage low-cost machine learning (ML) models to predict the excited state properties of iridium complexes.
  arXiv  Detail & Related papers  (2022-09-18T16:24:07Z)
• Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
  Graph neural networks (GNNs) have drawn more and more attention from material scientists. We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
  arXiv  Detail & Related papers  (2022-09-15T13:52:15Z)
• Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery [0.7695660509846216]
  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.
  arXiv  Detail & Related papers  (2021-06-20T22:34:04Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.