A human-machine interface for automatic exploration of chemical reaction networks

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 1, 2024

Abstract Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting networks are so vast that an all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while with completely pre-defined or hard-wired constraints, such as element-specific coordination numbers, not flexible enough for systems. Here, we introduce STEERING WHEEL guide otherwise unbiased automated exploration. The algorithm intuitive, generally applicable, and enables one focus on specific regions emerging network. It also allows guiding data generation in context mechanism exploration, catalyst design, other optimization challenges. demonstrated elucidation transition metal catalysts. We highlight how catalytic cycles reproducible way. objectives fully adjustable, allowing harness both structure-specific (accurate) calculations well broad high-throughput screening possible intermediates.

Language: Английский

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easyPARM: Automated, Versatile, and Reliable Force Field Parameters for Metal-Containing Molecules with Unique Labeling of Coordinating Atoms

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

The dynamics of metal centers are challenging to describe due the vast variety ligands, metals, and coordination spheres, hampering existence general databases transferable force field parameters for classical molecular simulations. Here, we present easyPARM, a Python-based tool that can calculate wide range complexes from routine frequency calculations with electronic structure methods. approach is based on unique labeling strategy, in which each ligand atom coordinates receives type. This design prevents parameter shortage, duplication, necessity post-process output files, even very complicated whose parametrization process remain automatic. program requires Cartesian Hessian matrix, geometry xyz file, atomic charges provide reliable force-field extensively benchmarked against density functional theory both gas condensed phases. procedure allows description at low computational cost an accuracy as good quality matrix obtained by quantum chemistry easyPARM v2.00 reads vibrational frequencies Gaussian (version 09 or 16) ORCA 5 6) format provides refined Amber format. These be directly used NAMD engines converted other formats. available free charge GitHub platform (https://github.com/Abdelazim-Abdelgawwad/easyPARM.git).

Language: Английский

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On the Stability Constants of Metal-Nitrate Complexes in Aqueous Solutions

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: 27(18), P. 9350 - 9368

Published: Jan. 1, 2025

Stability constants of simple reactions involving addition the NO3- ion to hydrated metal complexes, [M(H2O)x]n+ are calculated with a computational workflow developed using cloud computing resources. The performs conformational searches for complexes at both low and high levels theories in conjunction continuum solvation model (CSM). low-level theory is mainly used initial searches, which complemented high-level density functional CSM framework determine coordination chemistry relevant stability constant calculations. In this regard, lowest energy conformations found obtain reaction free energies one where M represents Fe(II), Fe(III), Sr(II), Ce(III), Ce(IV), U(VI), respectively. Structural analysis hundreds optimized geometries reveals that coordinates Fe(II) Fe(III) either monodentate or bidentate manner. Interestingly, lowest-energy metal-nitrate exhibit number 6 while seven-coordinated approximately 2 kcal mol-1 higher energy. Notably, configuration more stable than six-coordinated (monodentate bidentate) by few thermal units. contrast, U(VI) ions predominantly coordinate manner, exhibiting typical numbers 7, 9, 5, accordingly linear approaches account systematic errors good agreements obtained between available experimental data.

Language: Английский

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Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Transition-metal complexes serve as highly enantioselective homogeneous catalysts for various transformations, making them valuable in the pharmaceutical industry. Data-driven prediction models can accelerate high-throughput catalyst design but require computer-readable representations that account conformational flexibility. This is typically achieved through high-level conformer searches, followed by DFT optimization of transition-metal complexes. However, selection remains reliant on human assumptions, with no cost-efficient and generalizable workflow available. To address this, we introduce an automated approach to correlate CREST(GFN2-xTB//GFN-FF)-generated ensembles their DFT-optimized counterparts systematic selection. We analyzed 24 precatalyst structures, performing CREST full optimization. Three filtering methods were evaluated: (i) geometric ligand descriptors, (ii) PCA-based selection, (iii) DBSCAN clustering using RMSD energy. The proposed validated Rh-based featuring bisphosphine ligands, which are widely employed hydrogenation reactions. assess general applicability, both its corresponding acrylate-bound complex analyzed. Our results confirm overestimates flexibility, energy-based ineffective. failed distinguish conformers energy, while RMSD-based improved lacked tunability. provided most effective approach, eliminating redundancies preserving key configurations. method remained robust across data sets computationally efficient without requiring molecular descriptor calculations. These findings highlight limitations advantages structure-based approaches While a practical solution, parameters remain system-dependent. For high-accuracy applications, refined energy calculations may be necessary; however, DBSCAN-based offers accessible strategy rapid involving

Language: Английский

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Metallicious: Automated Force-Field Parameterization of Covalently Bound Metals for Supramolecular Structures

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(20), P. 9060 - 9071

Published: Oct. 7, 2024

Metal ions play a central, functional, and structural role in many molecular structures, from small catalysts to metal-organic frameworks (MOFs) proteins. Computational studies of these systems typically employ classical or quantum mechanical approaches combination both. Among models, only the covalent metal model reproduces both geometries charge transfer effects but requires time-consuming parameterization, especially for supramolecular containing repetitive units. To streamline this process, we introduce

Language: Английский

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metallicious: Automated force-field parametrization of covalently bound metals for supramolecular structures

Published: June 14, 2024

Metal ions play a central functional and structural role in many molecular structures, from small catalysts to metal-organic frameworks (MOFs) proteins. Computational studies of these systems typically employ classical or quantum mechanical approaches combination both. Among models, only the covalent metal model reproduces both geometries charge transfer effects but requires time-consuming parametrization, especially for supramolecular containing repetitive units. To streamline this process, we introduce metallicious, Python tool designed efficient force-field parametrization structures. metallicious has been tested on diverse systems, including cages, knots, MOFs. Our benchmarks demonstrate that parameters obtained accurately reproduce reference properties calculations crystal MD simulations generated structures consistently yield stable explicit solvent, contrast similar performed with non-bonded cationic dummy models. Overall, facilitates setup dynamics (MD) simulations, providing insights into their dynamic host-guest interactions. The is freely available GitHub (https://github.com/duartegroup/metallicious)

Language: Английский

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From Data to Chemistry: Revealing Causality and Reaction Coordinates through Interpretable Machine Learning in Supramolecular Transition Metal Catalysis

Published: June 25, 2024

Supramolecular transition metal catalysts with tailored reaction environments allow for the usage of abundant 3d metals as catalytic centres, leading to more sustainable chemical processes. However, such are large and flexible systems intricate interactions, resulting in complex coordinates. To capture their dynamic nature, we developed a broadly applicable, high-throughput workflow, leveraging quantum mechanics/molecular mechanics (QM/MM) molecular dynamics explicit solvent, investigate Cu(I)-calix[8]arene catalysed C-N coupling reaction. The system complexity high amount data generated from sampling require automated analyses. identify quantify coordinate noisy simulation trajectories, applied interpretable machine learning techniques (Lasso, Random Forest, Logistic Regression) consensus model, alongside dimensionality reduction methods (PCA, LDA, tICA). Leveraging Granger Causality go beyond traditional view coordinate, by defining it sequence motions that led up

Language: Английский

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Navigating chemical reaction space with a steering wheel

arXiv (Cornell University), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting networks are so vast that an all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while with completely pre-defined or hard-wired constraints, such as element-specific coordination numbers, not flexible enough for systems. Here, we introduce Steering Wheel guide otherwise unbiased automated exploration. The algorithm intuitive, generally applicable, and enables one focus on specific regions emerging network. It also allows guiding data generation in context mechanism exploration, catalyst design, other optimization challenges. demonstrated elucidation transition metal catalysts. We highlight how catalytic cycles reproducible way. objectives fully adjustable, allowing harness both structure-specific (accurate) calculations well broad high-throughput screening possible intermediates.

Language: Английский

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Comparison of Machine-Learning and Classical Force Fields in Simulating the Solvation of Small Organic Molecules in Acetonitrile

Published: Oct. 31, 2023

Machine learning force fields (MLFFs) have emerged as a new method for molecular simulation that combines the accuracy of ab initio approaches with computational efficiency classical fields. However, performance MLFFs in describing solvation configuration has yet to be explored. Here, we compare and contrast ANI-1ccx MLFF, GAFF field, dynamics (AIMD) simulating nine organic solutes acetonitrile solvents. We examine solvent-solute interaction described by these methods from four aspects: solute conformation landscape, shell structure, structure O-H⋯N hydrogen bond, first shell. For description, both yield minima agree density functional theory optimization rigid solutes. their results diverge flexible agrees better AIMD on location solvent than does. description bond formed between solute, generates stronger bonds shorter lengths, wider angles, longer lifetimes, agreeing DFT-optimized structure. also describes more frequent exchange molecules out GAFF. Our study demonstrates potential benefits utilizing MLFF solution-phase generating configurations.

Language: Английский

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Comparison of Machine-Learning and Classical Force Fields in Simulating the Solvation of Small Organic Molecules in Acetonitrile

Published: Nov. 1, 2023

Machine learning force fields (MLFFs) have emerged as a new method for molecular simulation that combines the accuracy of ab initio approaches with computational efficiency classical fields. However, performance MLFFs in describing solvation configuration has yet to be explored. Here, we compare and contrast ANI-1ccx MLFF, GAFF field, dynamics (AIMD) simulating nine organic solutes acetonitrile solvents. We examine solvent-solute interaction described by these methods from four aspects: solute conformation landscape, shell structure, structure O-H⋯N hydrogen bond, first shell. For description, both yield minima agree density functional theory optimization rigid solutes. their results diverge flexible agrees better AIMD on location solvent than does. description bond formed between solute, generates stronger bonds shorter lengths, wider angles, longer lifetimes, agreeing DFT-optimized structure. also describes more frequent exchange molecules out GAFF. Our study demonstrates potential benefits utilizing MLFF solution-phase generating configurations.

Language: Английский

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