Entropy-based active learning of graph neural network surrogate models for materials properties.
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| Title: | Entropy-based active learning of graph neural network surrogate models for materials properties. |
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| Authors: | Allotey J; School of Physics, University of Bristol, Bristol BS8 1TL, United Kingdom., Butler KT; Scientific Machine Learning Research Group, Scientific Computing Department, Rutherford Appleton Laboratory, Science and Technology Facilities Council, Didcot OX11 0DQ, United Kingdom., Thiyagalingam J; Scientific Machine Learning Research Group, Scientific Computing Department, Rutherford Appleton Laboratory, Science and Technology Facilities Council, Didcot OX11 0DQ, United Kingdom. |
| Source: | The Journal of chemical physics [J Chem Phys] 2021 Nov 07; Vol. 155 (17), pp. 174116. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: American Institute of Physics Country of Publication: United States NLM ID: 0375360 Publication Model: Print Cited Medium: Internet ISSN: 1089-7690 (Electronic) Linking ISSN: 00219606 NLM ISO Abbreviation: J Chem Phys Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1089-7690 |
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| DOI: | 10.1063/5.0065694 |
