A comprehensive electron wavefunction analysis toolbox for chemists, Multiwfn

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(8)

Published: Aug. 27, 2024

Analysis of electron wavefunction is a key component quantum chemistry investigations and indispensable for the practical research many chemical problems. After more than ten years active development, analysis program Multiwfn has accumulated very rich functions, its application scope covered numerous aspects theoretical research, including charge distribution, bond, localization delocalization, aromaticity, intramolecular intermolecular interactions, electronic excitation, response property. This article systematically introduces features functions latest version provides representative examples. Through this article, readers will be able to fully understand characteristics recognize unique value Multiwfn. The source code precompiled executable files Multiwfn, as well manual containing detailed introduction backgrounds tutorials, can all downloaded free from website (http://sobereva.com/multiwfn).

Language: Английский

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Best‐Practice DFT Protocols for Basic Molecular Computational Chemistry**

Angewandte Chemie International Edition, Journal Year: 2022, Volume and Issue: 61(42)

Published: Sept. 14, 2022

Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share these quantum-chemical applies density functional theory (DFT) evaluated in atomic-orbital basis sets. This work provides best-practice guidance on the numerous methodological technical aspects DFT three parts: Firstly, we set stage introduce a step-by-step decision tree to choose computational protocol that models experiment as closely possible. Secondly, present recommendation matrix guide choice depending task at hand. A particular focus is achieving an optimal balance between accuracy, robustness, efficiency through multi-level approaches. Finally, discuss selected representative examples illustrate recommended protocols effect choices.

Language: Английский

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Open Catalyst 2020 (OC20) Dataset and Community Challenges

ACS Catalysis, Journal Year: 2021, Volume and Issue: 11(10), P. 6059 - 6072

Published: May 4, 2021

Catalyst discovery and optimization is key to solving many societal energy challenges including solar fuels synthesis, long-term storage, renewable fertilizer production. Despite considerable effort by the catalysis community apply machine learning models computational catalyst process, it remains an open challenge build that can generalize across both elemental compositions of surfaces adsorbate identity/configurations, perhaps because datasets have been smaller in than related fields. To address this we developed OC20 dataset, consisting 1,281,040 Density Functional Theory (DFT) relaxations (~264,890,000 single point evaluations) a wide swath materials, surfaces, adsorbates (nitrogen, carbon, oxygen chemistries). We supplemented dataset with randomly perturbed structures, short timescale molecular dynamics, electronic structure analyses. The comprises three central tasks indicative day-to-day modeling comes pre-defined train/validation/test splits facilitate direct comparisons future model development efforts. applied state-of-the-art graph neural network (CGCNN, SchNet, Dimenet++) each these as baseline demonstrations for on. In almost every task, no upper limit on size was identified, suggesting even larger are likely improve initial results. provided resources, well public leader board encourage contributions solve important tasks.

Language: Английский

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Computational molecular spectroscopy

Nature Reviews Methods Primers, Journal Year: 2021, Volume and Issue: 1(1)

Published: May 27, 2021

Language: Английский

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Robust and Efficient Implicit Solvation Model for Fast Semiempirical Methods

Journal of Chemical Theory and Computation, Journal Year: 2021, Volume and Issue: 17(7), P. 4250 - 4261

Published: June 29, 2021

We present a robust and efficient method to implicitly account for solvation effects in modern semiempirical quantum mechanics force fields. A computationally yet accurate model based on the analytical linearized Poisson–Boltzmann (ALPB) is parameterized extended tight binding (xTB) density functional (DFTB) methods as well recently proposed GFN-FF general field. The perform over broad range of systems applications, from conformational energies transition-metal complexes large supramolecular association reactions charged species. For hydration free small molecules, GFN1-xTB(ALPB) reaching accuracy sophisticated explicitly solvated approaches, with mean absolute deviation only 1.4 kcal/mol compared experiment. Logarithmic octanol–water partition coefficients (log Kow) are computed about 0.65 using GFN2-xTB(ALPB) experimental values indicating consistent description differential solvent effects. Overall, more than twenty solvents each six tested. They readily available xtb dftb+ programs diverse computational applications.

Language: Английский

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Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Science, Journal Year: 2024, Volume and Issue: 384(6693)

Published: March 7, 2024

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids DNA bases with an atomic all other groups model assemblies that contain proteins, nucleic acids, small molecules, metals, covalent modifications, given their sequences chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion (RFdiffusionAA), builds structures around molecules. Starting from random distributions acid residues surrounding target designed experimentally validated, through crystallography binding measurements, proteins bind the cardiac disease therapeutic digoxigenin, enzymatic cofactor heme, light-harvesting molecule bilin.

Language: Английский

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 9927 - 10000

Published: July 14, 2021

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior metal-organic bond, while very tunable achieving target properties, is challenging to predict necessitates searching a wide complex space identify needles in haystacks applications. This review will focus on techniques that make high-throughput search transition-metal chemical feasible discovery with desirable properties. cover development, promise, limitations "traditional" computational chemistry (i.e., force field, semiempirical, density theory methods) as it pertains data generation inorganic molecular discovery. also discuss opportunities leveraging experimental sources. We how advances statistical modeling, artificial intelligence, multiobjective optimization, automation accelerate lead compounds rules. overall objective this showcase bringing together from diverse areas computer science have enabled rapid uncovering structure-property relationships chemistry. aim highlight unique considerations motifs bonding (e.g., variable spin oxidation state, strength/nature) set them their apart more commonly considered organic molecules. uncertainty relative scarcity motivate specific developments machine learning representations, model training, Finally, we conclude an outlook opportunity accelerated complexes.

Language: Английский

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Late-stage diversification of indole skeletons through nitrogen atom insertion

Science, Journal Year: 2022, Volume and Issue: 377(6610), P. 1104 - 1109

Published: Sept. 1, 2022

Compared with peripheral late-stage transformations mainly focusing on carbon-hydrogen functionalizations, reliable strategies to directly edit the core skeleton of pharmaceutical lead compounds still remain scarce despite recent flurry activity in this area. Herein, we report skeletal editing indoles through nitrogen atom insertion, accessing corresponding quinazoline or quinoxaline bioisosteres by trapping an electrophilic nitrene species generated from ammonium carbamate and hypervalent iodine. This reactivity relies strategic use a silyl group as labile protecting that can facilitate subsequent product release. The utility highly functional group-compatible methodology context several commercial drugs is demonstrated.

Language: Английский

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Efficient Quantum Chemical Calculation of Structure Ensembles and Free Energies for Nonrigid Molecules

The Journal of Physical Chemistry A, Journal Year: 2021, Volume and Issue: 125(19), P. 4039 - 4054

Published: March 10, 2021

The application of quantum chemical, automatic multilevel modeling workflows for the determination thermodynamic (e.g., conformation equilibria, partition coefficients, pKa values) and spectroscopic properties relatively large, nonrigid molecules in solution is described. Key points are computation rather complete structure (conformer) ensembles with extremely fast but still reasonable GFN2-xTB or GFN-FF semiempirical methods CREST searching approach subsequent refinement at a recently developed, accurate r2SCAN-3c DFT composite level. Solvation effects included all steps by continuum solvation models (ALPB, (D)COSMO-RS). Consistent inclusion thermostatistical contributions framework modified rigid-rotor-harmonic-oscillator approximation (mRRHO) based on xTB/FF computed PES also recommended.

Language: Английский

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Organic reactivity from mechanism to machine learning

Nature Reviews Chemistry, Journal Year: 2021, Volume and Issue: 5(4), P. 240 - 255

Published: March 16, 2021

Language: Английский

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