On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Je, Lisa; Huber, George W.; Lehn, Reid C. Van; Zavala, Ví­ctor M.

doi:10.1016/j.coche.2022.100796

Cited by 5 publications

(6 citation statements)

References 45 publications

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“…Because these calculations do not require computationally expensive polymer–solvent simulations, our model can potentially be used to screen the solubility of representative lignin oligomers in DES and DES–water mixtures, thereby substantially reducing experimental effort. We envision combining this solubility model with prior machine-learning methods to predict solvent effects on liquid-phase reactions to identify optimal DES–water mixtures for lignin depolymerization. ,,, Future avenues of research will also address challenges facing the application of this model. One challenge is the dependence on experimental data; more than half of the experimental data points used in this work were based on a single DES system (2:1 PA:U) and its aqueous mixtures, and only four model compounds were considered.…”

Section: Discussionmentioning

confidence: 99%

“…We envision combining this solubility model with prior machine-learning methods to predict solvent effects on liquid-phase reactions to identify optimal DES−water mixtures for lignin depolymerization. 55,57,74,75 Future avenues of research will also address challenges facing the application of this model. One challenge is the dependence on experimental data; more than half of the experimental data points used in this work were based on a single DES system (2:1 PA:U) and its aqueous mixtures, and only four model compounds were considered.…”

Section: ■ Conclusionmentioning

confidence: 99%

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Data-Centric Development of Lignin Structure–Solubility Relationships in Deep Eutectic Solvents Using Molecular Simulations

Sumer

Lehn

2022

ACS Sustainable Chem. Eng.

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Lignin is a natural source of aromatic chemicals with significant potential as an abundant, renewable feedstock for value-added products. Deep eutectic solvents (DES)�solvents composed of a hydrogen bond donor (HBD) and acceptor (HBA) in varying ratios�have emerged as a highly tunable class of solvents for lignin solubilization. However, the variety of possible DES compositions and limited molecular-scale understanding of lignin solubility makes solvent selection a challenge without laborious trial-and-error experimentation. To address these challenges, we use classical molecular dynamics (MD) simulations to study the interactions of lignin model compounds with various DES−water systems. Quantitative parameters (descriptors) were calculated by postprocessing the MD results and used to train a regression model that predicts experimentally determined solubilities of lignin model compounds. This approach revealed that the most important descriptors of solubility are the system temperature, solute hydrophilicity, and metrics quantifying hydrogen bonding. Maximizing the interactions between solute−HBD (hydrophobic group), water−HBD (hydrophilic group), and water−HBA molecules led to the highest model compound solubility. Our results support a hydrotropic mechanism in which extensive DES− water hydrogen bonding and favorable HBD interactions with the solute promote high solubility. We applied the regression model derived using model compounds to predict the solubility of representative lignin oligomers. The model predicted lignin oligomers' solubilities in good agreement with experiments, indicating that the simulations of model compounds can be extended to predict the solubility of larger lignin compounds across a range of solvent compositions and temperatures. These findings provide new molecular-scale insight into lignin solubilization mechanisms and a new method for computationally screening potential solvent systems for lignin valorization.

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Section: Discussionmentioning

confidence: 99%

Section: ■ Conclusionmentioning

confidence: 99%

Data-Centric Development of Lignin Structure–Solubility Relationships in Deep Eutectic Solvents Using Molecular Simulations

Sumer

Lehn

2022

ACS Sustainable Chem. Eng.

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“…8 However, the analysis of MD data sets is challenging due to both their size and complexity; specifically, MD simulations can produce terabytes of data that require computationally efficient and scalable analysis methods, while the complexity of the data requires methods that are generalizable to a broad range of systems and that are robust to data heterogeneity and noise. 9 Quantification and reduction of molecular simulation data has been traditionally conducted via order parameters and summarizing statistics such as radial distribution functions and correlation fields, particularly for condensed-phase systems. 10 These descriptors are usually computationally efficient, physically interpretable, and are derived from principles of physics and statistical mechanics.…”

Section: ■ Introductionmentioning

confidence: 99%

“…These techniques are also employed in the study of soft materials such as proteins and polymers, − and in the design of self-assembled colloidal systems . However, the analysis of MD data sets is challenging due to both their size and complexity; specifically, MD simulations can produce terabytes of data that require computationally efficient and scalable analysis methods, while the complexity of the data requires methods that are generalizable to a broad range of systems and that are robust to data heterogeneity and noise …”

Section: Introductionmentioning

confidence: 99%

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

Smith

Runde

Chew

et al. 2023

J. Chem. Theory Comput.

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Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.

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“…8 However, the analysis of MD datasets is challenging due to both their size and complexity; specifically, MD simulations can produce terabytes of data that require computationally efficient and scalable analysis methods, while the complexity of the data requires methods that are generalizable to a broad range of systems and that are robust to data heterogeneity and noise. 9 Quantification and reduction of molecular simulation data has been traditionally conducted via order parameters and summarizing statistics such as radial distribution functions and correlation fields, particularly for condensed-phase systems. 10 These descriptors are usually computationally efficient, physically interpretable, and are derived from principles of physics and statistical mechanics.…”

Section: Introductionmentioning

confidence: 99%

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

Smith

Runde

Chew

et al. 2022

Preprint

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Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex datasets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD datasets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.

show abstract

On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Cited by 5 publications

References 45 publications

Data-Centric Development of Lignin Structure–Solubility Relationships in Deep Eutectic Solvents Using Molecular Simulations

Data-Centric Development of Lignin Structure–Solubility Relationships in Deep Eutectic Solvents Using Molecular Simulations

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

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