Applications in Materials Science

Le, Tu C.; Winkler, David A.

doi:10.1002/9783527806539.ch12

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…Finally, we used machine learning methods to explore the contributions of the parameters (model type, partial charge, Lennard-Jones parameters, bond length and angle) to the surface tension, dielectric constant, and self-diffusion coefficient. Machine learning can be used to predict a wide variety of material, [86][87][88][89] chemical, [90][91][92][93] and biological properties, [94][95][96][97] with several commercial and non-commercial open-source platforms that can be used to develop machine learning algorithms such as Schrödinger, 98 SYBYL, 99 TensorFlow (Google), 100 and BioPPSy. 101…”

Section: Name Typementioning

confidence: 99%

A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations

Pathirannahalage

Meftahi

Elbourne

et al. 2021

Preprint

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Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

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Section: Name Typementioning

confidence: 99%

A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations

Pathirannahalage

Meftahi

Elbourne

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, we used machine learning methods to explore the contributions of the parameters (model type, partial charge, Lennard-Jones parameters, bond length, and angle) to the surface tension, dielectric constant, and self-diffusion coefficient. Machine learning can be used to predict a wide variety of material, − chemical, − and biological properties, − with several commercial and noncommercial open-source platforms that can be used to develop machine learning algorithms such as Schrödinger, SYBYL, TensorFlow (Google), and BioPPSy …”

Section: Introductionmentioning

confidence: 99%

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

Pathirannahalage

Meftahi

Elbourne

et al. 2021

J. Chem. Inf. Model.

Self Cite

138

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Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter−property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

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“…The properties that are investigated range from properties of nanomaterials, materials from regenerative medicine, solar cells, homogeneous or heterogeneous catalysts, electrocatalysts, phase diagrams, ceramics, or the properties of supercritical solvents, and a few reviews have appeared. [80,81] In most cases, the chemical structure of the material investigated is not known and therefore other types of descriptors have to be chosen to represent a material for a QSAR study. Materials could be represented by physical properties such as refractive index or melting point, spectra, the components or the conditions for the production of the material, etc.…”

Section: Materials Sciencementioning

confidence: 99%

Chemistry in Times of Artificial Intelligence

Gasteiger

2020

ChemPhysChem

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Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.

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Applications in Materials Science

Cited by 6 publications

References 36 publications

A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations

A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

Chemistry in Times of Artificial Intelligence

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