How accurate are approximate quantum chemical methods at modelling solute–solvent interactions in solvated clusters?

Chen, Junbo; Chan, Bun; Shao, Yihan; Ho, Junming

doi:10.1039/c9cp06792b

Cited by 26 publications

(36 citation statements)

References 114 publications

(97 reference statements)

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“…Still, even when only a single conformer of a microsolvated cluster is considered, DFT was shown to be well suited to describe solute–solvent interactions, [ 100 ] supporting the added value of low-cost microsolvation approaches to approximately model (bulk) solvent effects.…”

Section: Discussionmentioning

confidence: 99%

Quantum Chemical Microsolvation by Automated Water Placement

et al. 2021

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We developed a quantitative approach to quantum chemical microsolvation. Key in our methodology is the automatic placement of individual solvent molecules based on the free energy solvation thermodynamics derived from molecular dynamics (MD) simulations and grid inhomogeneous solvation theory (GIST). This protocol enabled us to rigorously define the number, position, and orientation of individual solvent molecules and to determine their interaction with the solute based on physical quantities. The generated solute–solvent clusters served as an input for subsequent quantum chemical investigations. We showcased the applicability, scope, and limitations of this computational approach for a number of small molecules, including urea, 2-aminobenzothiazole, (+)-syn-benzotriborneol, benzoic acid, and helicene. Our results show excellent agreement with the available ab initio molecular dynamics data and experimental results.

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Section: Discussionmentioning

confidence: 99%

Quantum Chemical Microsolvation by Automated Water Placement

et al. 2021

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“…Yet, the impact of HTVS has been hampered by the difficulty in scaling the experimental confirmation of candidates, 1 as simulations feasible for high-throughput are still largely qualitative for condensed-phase properties. 41 A loose screen that accounts for computational inaccuracies minimizes false negatives, but the high cost of experimental validation means that almost all candidates must be rejected. The accuracy of computational screening can be maximized by implementing self-correcting filters such as checking whether simulations showed proper convergence catching false positives early on in the workflow.…”

Section: Methodsmentioning

confidence: 99%

Data-Driven Strategies for Accelerated Materials Design

et al. 2021

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Conspectus The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

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“…The most common solvent for these studies, both in the production of furfural and HMF, is dimethylsulfoxide (DMSO), and it is used from highly aqueous to water-free systems (Table 1). [112][113][114][115][116][117][118][119][120][121][122][123][124][125][126][127][128] Highly aqueous conditions (7:3 v-v water-DMSO monophasic systems), paired with microwave heating and a Lewis acid catalyst (i.e., CrCl3 or Al(NO3)3), resulted in a relatively high fructose-to-HMF selectivity (approx. 65 mol%), with a strong influence on conversion and selectivity also coming from the catalyst loading.…”

Section: Please Do Not Adjust Marginsmentioning

confidence: 99%

Production of furans from C₅ and C₆ sugars in the presence of polar organic solvents

Ricciardi

Verboom

Lange

et al. 2022

Sustainable Energy Fuels

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How accurate are approximate quantum chemical methods at modelling solute–solvent interactions in solvated clusters?

Abstract: In this paper, the performance of ab initio composite methods, and a wide range of DFT methods is assessed for the calculation of interaction energies of thermal clusters of a solute in water.

Cited by 26 publications

References 114 publications

Quantum Chemical Microsolvation by Automated Water Placement

Quantum Chemical Microsolvation by Automated Water Placement

Data-Driven Strategies for Accelerated Materials Design

Production of furans from C₅ and C₆ sugars in the presence of polar organic solvents

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How accurate are approximate quantum chemical methods at modelling solute–solvent interactions in solvated clusters?

Abstract: In this paper, the performance of ab initio composite methods, and a wide range of DFT methods is assessed for the calculation of interaction energies of thermal clusters of a solute in water.

Cited by 26 publications

References 114 publications

Quantum Chemical Microsolvation by Automated Water Placement

Quantum Chemical Microsolvation by Automated Water Placement

Data-Driven Strategies for Accelerated Materials Design

Production of furans from C5 and C6 sugars in the presence of polar organic solvents

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Product

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Production of furans from C₅ and C₆ sugars in the presence of polar organic solvents