Architector for high-throughput cross-periodic table 3D complex building

Partial to Total Generation of 3D Transition-Metal Complexes

The design of transition-metal complexes (TMCs) has drawn much attention over the years because of their important applications as metallodrugs and functional materials. In this work, we present an extension of our recently reported approach, LigandDiff [Jin et al. J. Chem. Theory Comput. 20, 4377(2024)]. The new model, which we call multi-LigandDiff, is more flexible and greatly outperforms its predecessor. This scaffold-based diffusion model allows de novo ligand design with either existing ligands or without any ligand. Moreover, it allows users to predefine the denticity of the generated ligand. Our results indicate that multi-LigandDiff can generate well-defined ligands and is transferable to multiple transition metals and coordination geometries. In terms of its application, multi-LigandDiff successfully designed 338 Fe(II) spin-crossover (SCO) complexes from only 47 experimentally validated SCO complexes. And these generated complexes are configurationally diverse and structurally reasonable. Overall, the results show that multi-LigandDiff is an ideal tool to design novel TMCs from scratch.

Extension of the D3 and D4 London dispersion corrections to the full actinides series

Efficient dispersion corrections are an indispensable component of modern density functional theory, semi-empirical quantum mechanical, and even force field methods. In this work, we extend the well established D3 and D4 London dispersion corrections to the full actinides series, francium, and radium. To keep consistency with the existing versions, the original parameterization strategy of the D4 model was only slightly modified. This includes improved reference Hirshfeld atomic partial charges at the ωB97M-V/ma-def-TZVP level to fit the required electronegativity equilibration charge (EEQ) model. In this context, we developed a new actinide data set called AcQM, which covers the most common molecular actinide compound space. Furthermore, the efficient calculation of dynamic polarizabilities that are needed to construct CAB6 dispersion coefficients was implemented into the ORCA program package. The extended models are assessed for the computation of dissociation curves of actinide atoms and ions, geometry optimizations of crystal structure cutouts, gas-phase structures of small uranium compounds, and an example extracted from a small actinide complex protein assembly. We found that the novel parameterizations perform on par with the computationally more demanding density-dependent VV10 dispersion correction. With the presented extension, the excellent cost-accuracy ratio of the D3 and D4 models can now be utilized in various fields of computational actinide chemistry and, e.g., in efficient composite DFT methods such as r2SCAN-3c. They are implemented in our freely available standalone codes (dftd4, s-dftd3) and the D4 version will be also available in the upcoming ORCA 6.0 program package.

Identifying and embedding transferability in data-driven representations of chemical space

Transferability, especially in the context of model generalization, is a paradigm of all scientific disciplines. However, the rapid advancement of machine learned model development threatens this paradigm, as it can be difficult to understand how transferability is embedded (or missed) in complex models developed using large training data sets. Two related open problems are how to identify, without relying on human intuition, what makes training data transferable; and how to embed transferability into training data. To solve both problems for ab initio chemical modelling, an indispensable tool in everyday chemistry research, we introduce a transferability assessment tool (TAT) and demonstrate it on a controllable data-driven model for developing density functional approximations (DFAs). We reveal that human intuition in the curation of training data introduces chemical biases that can hamper the transferability of data-driven DFAs. We use our TAT to motivate three transferability principles; one of which introduces the key concept of transferable diversity. Finally, we propose data curation strategies for general-purpose machine learning models in chemistry that identify and embed the transferability principles.

Insights into coordination and ligand trends of lanthanide complexes from the Cambridge Structural Database

Understanding lanthanide coordination chemistry can help develop new ligands for more efficient separation of lanthanides for critical materials needs. The Cambridge Structural Database (CSD) contains tens of thousands of single crystal structures of lanthanide complexes that can serve as a training ground for both fundamental chemical insights and future machine learning and generative artificial intelligence models. This work aims to understand the currently available structures of lanthanide complexes in CSD by analyzing the coordination shell, donor types, and ligand types, from the perspective of rare-earth element (REE) separations. We obtain four sets of lanthanide complexes from CSD: Subset 1, all Ln-containing complexes (49472 structures); Subset 2, mononuclear Ln complexes (27858 structures); Subset 3, mononuclear Ln complexes without cyclopentadienyl ligands (Cp) (26156 structures); Subset 4, Ln complexes with at least one 1,10-phenanthroline (phen) or its derivative as a coordinating ligand (2226 structures). The subsequent analysis of lanthanide complexes in these subsets examines the trends in coordination numbers and first shell distances as well as identifies and characterizes the ligands and donor groups. In addition, examples of Ln-complexes with commercially available complexants and phen-based ligands are interrogated in detail. This systematic investigation lays the groundwork for future data-driven ligand designs for REE separations based on the structural insights into the lanthanide coordination chemistry.

Insights into the Mechanism of Neptunium Oxidation to the Heptavalent State

Neptunium can exist in multiple oxidation states, including the rare and poorly understood heptavalent form. In this work, we monitored the formation of heptavalent neptunium [Np(VII)O4(OH)2]3- during ozonolysis of aqueous MOH (M=Li, Na, K) solutions using a combined experimental and theoretical approach. All experimental reactions were closely monitored via absorption and vibrational spectroscopy to follow both the oxidation state and the speciation of neptunium guided by the calculated vibrational frequencies for various neptunium species. The mechanism of the reaction partly involves oxidative dissolution of transient Np(VI) oxide/hydroxide solid phases, the identity of which are dependent on the co-precipitating counter-cation Li+/Na+/K+. Additional calculations suggest that the most favorable energetic pathway occurs through the reaction of a [Np(V)O2(OH)4]3- with the hydroxide radical to form [Np(VI)O2(OH)4]2-, followed by an additional oxidation with HO⋅ to create [Np(VII)O4(OH)2]3-.

MACE: Automated Assessment of Stereochemistry of Transition Metal Complexes and Its Applications in Computational Catalysis

Computational chemistry pipelines typically commence with geometry generation, well-established for organic compounds but presenting a considerable challenge for transition metal complexes. This paper introduces MACE, an automated computational workflow for converting chemist SMILES/MOL representations of the ligands and the metal center to 3D coordinates for all feasible stereochemical configurations for mononuclear octahedral and square planar complexes directly suitable for quantum chemical computations and implementation in high-throughput computational chemistry workflows. The workflow is validated through a structural screening of a data set of transition metal complexes extracted from the Cambridge Structural Database. To further illustrate the power and capabilities of MACE, we present the results of a model DFT study on the hemilability of pincer ligands in Ru, Fe, and Mn complexes, which highlights the utility of the workflow for both focused mechanistic studies and larger-scale high-throughput pipelines.

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify

Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.

Hybrid DFT Geometries and Properties for 17k Lanthanoid Complexes─The LnQM Data Set

The unique properties of lanthanoids and their diverse applications make them an indispensable part of modern research and industry. While the field has garnered attention, there remains a gap in available molecule data sets that facilitate both classical quantum chemistry calculations and the burgeoning field of machine learning in data science applications. This research addresses the need for a comprehensive data set that allows for a comparative analysis of various lanthanoids. The herein presented, curated data set includes 17269 monolanthanoid complexes derived from 1205 distinct ligand motifs. Structures encompass all 15 lanthanoids in the +3 oxidation state and exhibit molecular charges ranging from -1 to +3, including structures with a high spin multiplicity up to 8. Starting from lanthanum complexes, samples were processed with a permutation of the central lanthanoid atom, resulting in highly comparable subsets, facilitating comparative studies in which the influence of the lanthanoid can be investigated independently of ligand effects. The data set provides a broad range of features such as PBE0-D4/def2-SVP optimized geometries and optimization trajectories, while also covering ωB97M-V/def2-SVPD energies, rotational constants, dipole moments, highest occupied molecular orbital-lowest-unoccupied molecular orbital (HOMO-LUMO) energies, and Mulliken, Löwdin, and Hirshfeld population analyses. Additionally, coordination numbers, polarizabilities, and partial charges from D4, electronegativity equilibration (EEQ), GFN2-xTB, and charge extended Hückel (CEH) calculations are included. The data set is openly accessible and may serve as a basis for further investigations into the properties of lanthanoids.