Advances of machine learning in molecular modeling and simulation

Haghighatlari, Mojtaba; Hachmann, Johannes

doi:10.1016/j.coche.2019.02.009

Cited by 107 publications

(91 citation statements)

References 45 publications

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“…A typical supervised ML scenario, which ChemML is designed to tackle, is the creation of a data‐derived prediction model . The latter can be thought of as a complex mathematical operation f : X → Y that learns a mapping from input x ∈ X onto an output y ∈ Y , where x in this context is the feature representation of a chemical or materials system and y is its target property (or other characteristic).…”

Section: Core Tasksmentioning

confidence: 99%

“…Data‐driven research and machine learning (ML) have emerged as promising new thrusts in materials and chemical research, in particular in the wake of the 2011 White House Materials Genome Initiative and the 2017 NSF Data‐Driven Discovery Science in Chemistry initiative, respectively. This new paradigm is causing much excitement for its unique potential to unravel hidden structure–property relationships that govern the behavior of chemical and materials systems, as well as for its ability to yield data‐derived surrogate models that are dramatically more efficient than traditional physics‐based models . However, as data‐driven research is still a young and less‐well‐established approach, there is a distinct infrastructure gap (especially in chemistry) .…”

Section: Introductionmentioning

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: Core Tasksmentioning

confidence: 99%

Section: Introductionmentioning

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

“…A typical supervised ML scenario, which ChemML is designed to tackle, is the creation of a data-derived prediction model [5]. The latter can be thought of as a complex mathematical operation f : X → Y that learns a mapping from input x ∈ X onto an output y ∈ Y , where x in this context is the feature representation of a chemical or materials system and y is its target property (or other characteristic).…”

Section: Core Tasksmentioning

confidence: 99%

“…Data-driven research and machine learning (ML) have emerged as promising new thrusts in materials and chemical research, in particular in the wake of the 2011 White House Materials Genome Initiative (MGI) [3] and the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative [4], respectively. This new paradigm is causing much excitement for its unique potential to unravel hidden structure-property relationships that govern the behavior of chemical and materials systems, as well as for its ability to yield dataderived surrogate models that are dramatically more efficient than traditional physics-based models [5]. However, as data-driven research is still a young and less-wellestablished approach, there is a distinct infrastructure gap (especially in chemistry) [6].…”

Section: Introductionmentioning

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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“…Illustration of materials design approaches. (a) ML-assisted materials screening (adapted from Reference[205] with permission, copyright 2018 Springer Nature); (b) high-throughput virtual screening integrated with ML models (adapted from Reference[206] with permission, copyright 2019 Elsevier) and (c) inverse molecular design by RL; (d) integration of various modules for design of insulating nanocomposites by Bayesian optimization (BO). ECFP = extended connectivity fingerprints.…”

mentioning

confidence: 99%

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

et al. 2020

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Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

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Advances of machine learning in molecular modeling and simulation

Cited by 107 publications

References 45 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-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

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