A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

Afzal, Mohammad Atif Faiz; Sonpal, Aditya; Haghighatlari, Mojtaba; Schultz, Andrew J.; Hachmann, Johannes

doi:10.1039/c9sc02677k

Cited by 42 publications

(40 citation statements)

References 54 publications

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“…ChemML features other methodological innovation, for example, in the areas of physics‐infused neural network architectures, learned features, local domain models, training set design, on‐the‐fly assessment of learning curves, chemical pattern recognition, and so forth, that we will describe elsewhere.…”

Section: Methodological Innovation and Application Studiesmentioning

confidence: 99%

“…We have been employing ChemML in a number of real‐world application studies, both for the creation of data‐derived prediction models and chemical pattern recognition. These studies include discovery and design projects for new high‐refractive‐index polymers for optical applications, deep eutectic solvents for supercapacitors, and organic semiconductors for photovoltaics and other applications (using data of the Harvard Clean Energy Project).…”

Section: Methodological Innovation and Application Studiesmentioning

confidence: 99%

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ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

Haghighatlari

Vishwakarma

Altarawy

et al. 2020

WIREs Comput Mol Sci

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ChemML is an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under: Software > Simulation Methods Computer and Information Science > Chemoinformatics Structure and Mechanism > Computational Materials Science Software > Molecular Modeling

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Section: Methodological Innovation and Application Studiesmentioning

confidence: 99%

Section: Methodological Innovation and Application Studiesmentioning

confidence: 99%

ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

Haghighatlari

Vishwakarma

Altarawy

et al. 2020

WIREs Comput Mol Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…ChemML features other methodological innovation, e.g., in the areas of physics-infused neural network architectures, learned features, local domain models, training set design, on-the-fly assessment of learning curves [31], chemical pattern recognition [32], etc., that we will describe elsewhere.…”

Section: Prediction Models the Following Examples Are Three Highlmentioning

confidence: 99%

“…We have been employing ChemML in a number of realworld application studies, both for the creation of dataderived prediction models and chemical pattern recognition. These studies include discovery and design projects for new high-refractive-index polymers for optical applications [30][31][32][33][34][35], deep eutectic solvents for supercapacitors [36], and organic semiconductors for photovoltaics and other applications [37,38] (using data of the Harvard Clean Energy Project [39][40][41][42][43]).…”

Section: Prediction Models the Following Examples Are Three Highlmentioning

confidence: 99%

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data

Haghighatlari

Vishwakarma²,

Altarawy³

et al. 2019

Preprint

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<div>ChemML is an open machine learning and informatics program suite that is designed to support and advance the data-driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute machine learning workflows that are adapted specifically for the chemical and materials context. Key features are automation, general-purpose utility, versatility, and user-friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data-driven in silico research outlined in our recent publication1.</div>

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“…However, a quantitative model able to predict the size and morphology from HT synthesis conditions still does not exist. In this context Machine Learning (ML) is increasingly used for predicting the relationship between determined input parameters and resulting structures or properties from available datasets, without using first principles 28 – 31 . Although certain works were already carried out in several fields like synthesis, catalysis, molecular interactions, biology, engineering, etc.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles

Pellegrino

Isopescu

Pellutiè

et al. 2020

Sci Rep

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In the present work a series of design rules are developed in order to tune the morphology of TiO2 nanoparticles through hydrothermal process. Through a careful experimental design, the influence of relevant process parameters on the synthesis outcome are studied, reaching to the develop predictive models by using Machine Learning methods. The models, after the validation and training, are able to predict with high accuracy the synthesis outcome in terms of nanoparticle size, polydispersity and aspect ratio. Furthermore, they are implemented by reverse engineering approach to do the inverse process, i.e. obtain the optimal synthesis parameters given a specific product characteristic. For the first time, it is presented a synthesis method that allows continuous and precise control of NPs morphology with the possibility to tune the aspect ratio over a large range from 1.4 (perfect truncated bipyramids) to 6 (elongated nanoparticles) and the length from 20 to 140 nm.

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A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

Abstract: Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

Cited by 42 publications

References 54 publications

ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data

Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles

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