[HTML][HTML] Reliable and explainable machine-learning methods for accelerated material discovery
Despite ML's impressive performance in commercial applications, several unique
challenges exist when applying ML in materials science applications. In such a context, the …
challenges exist when applying ML in materials science applications. In such a context, the …
[PDF][PDF] Reliable and explainable machine-learning methods for accelerated material discovery
B Kailkhura, B Gallagher, S Kim, A Hiszpanski… - scholar.archive.org
Motivation Driven by the success of machine learning (ML) in commercial applications (eg,
product recommendations and advertising), there are significant efforts to exploit these tools …
product recommendations and advertising), there are significant efforts to exploit these tools …
[PDF][PDF] Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
B Kailkhura, B Gallagher, S Kim, A Hiszpanski… - researchgate.net
Material scientists are increasingly adopting the use of machine learning (ML) for making
potentially important decisions, such as, discovery, development, optimization, synthesis …
potentially important decisions, such as, discovery, development, optimization, synthesis …
Reliable and explainable machine-learning methods for accelerated material discovery
B Kailkhura, B Gallagher, S Kim… - NPJ Computational …, 2019 - search.proquest.com
Despite ML's impressive performance in commercial applications, several unique
challenges exist when applying ML in materials science applications. In such a context, the …
challenges exist when applying ML in materials science applications. In such a context, the …
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
B Kailkhura, B Gallagher, S Kim, A Hiszpanski… - arXiv preprint arXiv …, 2019 - arxiv.org
Material scientists are increasingly adopting the use of machine learning (ML) for making
potentially important decisions, such as, discovery, development, optimization, synthesis …
potentially important decisions, such as, discovery, development, optimization, synthesis …
Reliable and explainable machine-learning methods for accelerated material discovery
B Kailkhura, B Gallagher, S Kim… - npj Computational …, 2019 - ui.adsabs.harvard.edu
Despite ML's impressive performance in commercial applications, several unique
challenges exist when applying ML in materials science applications. In such a context, the …
challenges exist when applying ML in materials science applications. In such a context, the …
Reliable and explainable machine-learning methods for accelerated material discovery
B Kailkhura, B Gallagher, S Kim, A Hiszpanski… - npj Computational …, 2019 - cir.nii.ac.jp
抄録< jats: title> Abstract</jats: title>< jats: p> Despite ML's impressive performance in
commercial applications, several unique challenges exist when applying ML in materials …
commercial applications, several unique challenges exist when applying ML in materials …
Reliable and explainable machine-learning methods for accelerated material discovery
B Kailkhura, B Gallagher, S Kim, A Hiszpanski… - npj Computational …, 2019 - osti.gov
Despite ML's impressive performance in commercial applications, several unique
challenges exist when applying ML in materials science applications. In such a context, the …
challenges exist when applying ML in materials science applications. In such a context, the …
Reliable and explainable machine-learning methods for accelerated material discovery.
B Kailkhura, B Gallagher, S Kim… - NPJ Computational …, 2019 - search.ebscohost.com
Despite ML's impressive performance in commercial applications, several unique
challenges exist when applying ML in materials science applications. In such a context, the …
challenges exist when applying ML in materials science applications. In such a context, the …
Reliable and explainable machine-learning methods for accelerated material discovery
B Kailkhura, B Gallagher, S Kim, A Hiszpanski… - npj Computational …, 2019 - osti.gov
Despite ML's impressive performance in commercial applications, several unique
challenges exist when applying ML in materials science applications. In such a context, the …
challenges exist when applying ML in materials science applications. In such a context, the …
