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

Haghighatlari, Mojtaba; Vishwakarma, Gaurav; Altarawy, Doaa; Subramanian, R.; Kota, Bhargava Urala; Sonpal, Aditya; Setlur, Srirangaraj; Hachmann, Johannes

doi:10.26434/chemrxiv.8323271.v1

Cited by 30 publications

(34 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are, however, interdependent and often multiple iterations of each step are required to create a successful machine learning system. Specialized software frameworks have been developed to aid the set‐up and build and management of machine learning models.…”

Section: Data Analyticsmentioning

confidence: 99%

Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

View full text Add to dashboard Cite

Data‐driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high‐throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data‐driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data‐driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

show abstract

Section: Data Analyticsmentioning

confidence: 99%

Data‐Driven Materials Science: Status, Challenges, and Perspectives

et al. 2019

View full text Add to dashboard Cite

show abstract

“…We note that this and other trained density models as well as the corresponding ML workflows are available as part of the ChemML package's template and model collection ( e.g. , for retraining, customization, or transfer learning) 51. Finally, we give detailed definitions of all statistical metrics employed in this work.…”

mentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…These resources provide support for data preprocessing, visualisations, and the implementation of different frameworks. We provide a list in Table IV. ChemML [108] ChemML is a machine learning and informatics program that support and advance the data-driven research in the domain of chemical and materials.…”

Section: Uncover Covid-19mentioning

confidence: 99%

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

View full text Add to dashboard Cite

<div>COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.</div><div>As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.</div>

show abstract

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

Cited by 30 publications

References 0 publications

Data‐Driven Materials Science: Status, Challenges, and Perspectives

Data‐Driven Materials Science: Status, Challenges, and Perspectives

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

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Contact Info

Product

Resources

About