Data Generation for Machine Learning Interatomic Potentials and Beyond

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(24), P. 13681 - 13714

Published: Nov. 21, 2024

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides ML-based interatomic potentials have paved the way accurate modeling diverse chemical structural at atomic level. key determinant defining MLIP reliability remains quality training data. A paramount challenge lies constructing sets that capture specific domains vast space. This Review navigates intricate landscape essential components integrity data ensure extensibility transferability resulting models. We delve into details active learning, discussing its various facets implementations. outline different types uncertainty quantification applied to atomistic acquisition correlations between estimated true error. role samplers generating informative structures highlighted. Furthermore, we discuss via modified surrogate potential energy surfaces as innovative approach diversify also provides a list publicly available cover

Language: Английский

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Directional multiobjective optimization of metal complexes at the billion-system scale

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(4), P. 263 - 273

Published: March 29, 2024

Language: Английский

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Molecular representations in bio-cheminformatics

Memetic Computing, Journal Year: 2024, Volume and Issue: 16(3), P. 519 - 536

Published: July 20, 2024

Abstract Molecular representations have essential roles in bio-cheminformatics as they facilitate the growth of machine learning applications numerous sub-domains biology and chemistry, especially drug discovery. These transform structural chemical information molecules into machine-readable formats that can be efficiently processed by computer programs. In this paper, we present a comprehensive review, providing readers with diverse perspectives on strengths weaknesses well-known molecular representations, along their respective categories implementation sources. Moreover, provide summary applicability these de novo design, property prediction, reactions. Besides, for macromolecules are discussed highlighted pros cons. By addressing aspects, aim to offer valuable resource significant role advancing its related domains.

Language: Английский

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Zinc metal complexes synthesized by a green method as a new approach to alter the structural and optical characteristics of PVA: new field for polymer composite fabrication with controlled optical band gap

RSC Advances, Journal Year: 2024, Volume and Issue: 14(36), P. 26362 - 26387

Published: Jan. 1, 2024

The current study employed a novel approach to design polymer composites with modified structural and declined optical band gaps. results obtained in the present work for can be considered an original method make new field research based on green chemistry. Natural dyes extracted from tea were mixed hydrated zinc acetate (Zn(CH

Language: Английский

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Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning

ChemCatChem, Journal Year: 2024, Volume and Issue: 16(10)

Published: Jan. 5, 2024

Abstract Significant progress has been made in recent years the use of AI and Machine Learning (ML) for catalyst discovery optimisation. The effectiveness ML data science techniques was demonstrated predicting optimising enantioselectivity regioselectivity catalytic reactions through optimisation ligands, counterions reaction conditions. Direct new catalysts/reactions is more difficult requires efficient exploration transition metal chemical space. A range computational descriptor generation, ranging from molecular mechanics to DFT methods, have successfully demonstrated, often conjunction with reduce cost associated TS calculations. Complex aspects reactions, such as solvent, temperature, etc., also incorporated into workflow.

Language: Английский

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Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes**

Angewandte Chemie International Edition, Journal Year: 2023, Volume and Issue: 63(10)

Published: Dec. 13, 2023

Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven be promising compounds, but number studied compounds is still low compared millions organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding design small molecules, potentially even low-data scenarios. For first time, we extend application ML discovery metal-based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff-base their antibacterial properties, series models were trained. The perform well are used predict activity 54 compounds. These displayed 5.7x higher hit-rate (53.7 %) against methicillin-resistant Staphylococcus aureus (MRSA) original library (9.4 %), demonstrating that can applied improve success-rates search metalloantibiotics. This work paves way more ambitious applications field drug discovery.

Language: Английский

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

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(3), P. 825 - 836

Published: Jan. 18, 2024

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

Language: Английский

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High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Metal-embedded complexes (MECs), including transition metal (TMCs) and metal-organic frameworks (MOFs), are important in catalysis, materials science, molecular devices due to their unique atom centrality complex coordination environments. However, modeling predicting properties accurately is challenging. A new attention (MA) framework for graph neural networks (GNNs) was proposed address the limitations of traditional methods, which fail differentiate core structures from ordinary covalent bonds. This MA converts heterogeneous graphs into homogeneous ones with distinct features by highlighting key metal-feature through hierarchical pooling a cross-attention. To assess its performance, 11 widely used GNN algorithms, three heterogeneous, were compared. Experimental results indicate significant improvements accuracy: an average 32.07% TMC up 23.01% MOF CO2 absorption. Moreover, tests on framework's robustness regarding data set size variation comparison larger non-MA model show that enhanced performance stems architecture, not merely increasing capacity. The potential offers potent statistical tool optimizing designing like catalysts gas storage systems.

Language: Английский

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A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes

JACS Au, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

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SMILES all around: structure to SMILES conversion for transition metal complexes

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: April 28, 2025

Language: Английский

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