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1	Material Type: Article	Strelka2: fast and accurate calling of germline and somatic variants Nature methods, 2018-08, Vol.15 (8), p.591-594 [Peer Reviewed Journal] Copyright Nature Publishing Group Aug 2018 ;ISSN: 1548-7091 ;EISSN: 1548-7105 ;DOI: 10.1038/s41592-018-0051-x ;PMID: 30013048 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
2	Material Type: Article	OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features The Journal of chemical physics, 2020-09, Vol.153 (12), p.124111-124111 [Peer Reviewed Journal] Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/5.0021955 ;CODEN: JCPSA6 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
3	Material Type: Article	Accelerating high-throughput virtual screening through molecular pool-based active learning Chemical science (Cambridge), 2021-04, Vol.12 (22), p.7866-7881 [Peer Reviewed Journal] This journal is © The Royal Society of Chemistry. ;Copyright Royal Society of Chemistry 2021 ;This journal is © The Royal Society of Chemistry 2021 The Royal Society of Chemistry ;ISSN: 2041-6520 ;EISSN: 2041-6539 ;DOI: 10.1039/d0sc06805e ;PMID: 34168840 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
4	Material Type: Article	A perspective on scaling up quantum computation with molecular spins Applied physics letters, 2021-06, Vol.118 (24) [Peer Reviewed Journal] Author(s) ;2021 Author(s). Published under an exclusive license by AIP Publishing. ;ISSN: 0003-6951 ;EISSN: 1077-3118 ;DOI: 10.1063/5.0053378 ;CODEN: APPLAB Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
5	Material Type: Article	Machine learning-accelerated computational fluid dynamics Proceedings of the National Academy of Sciences - PNAS, 2021-05, Vol.118 (21), p.1 [Peer Reviewed Journal] Copyright © 2021 the Author(s). Published by PNAS. ;Copyright National Academy of Sciences May 25, 2021 ;Copyright © 2021 the Author(s). Published by PNAS. 2021 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2101784118 ;PMID: 34006645 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
6	Material Type: Article	Machine-learned potentials for next-generation matter simulations Nature materials, 2021-06, Vol.20 (6), p.750-761 [Peer Reviewed Journal] Springer Nature Limited 2021. ;ISSN: 1476-1122 ;EISSN: 1476-4660 ;DOI: 10.1038/s41563-020-0777-6 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
7	Material Type: Article	Low-Depth Quantum Simulation of Materials Physical review. X, 2018-03, Vol.8 (1), p.011044, Article 011044 [Peer Reviewed Journal] 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2160-3308 ;EISSN: 2160-3308 ;DOI: 10.1103/PhysRevX.8.011044 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
8	Material Type: Article	Efficient calculation of carrier scattering rates from first principles Nature communications, 2021-04, Vol.12 (1), p.2222-2222, Article 2222 [Peer Reviewed Journal] The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-021-22440-5 ;PMID: 33850113 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
9	Material Type: Article	Hierarchical machine learning of potential energy surfaces The Journal of chemical physics, 2020-05, Vol.152 (20), p.204110-204110 [Peer Reviewed Journal] Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/5.0006498 ;CODEN: JCPSA6 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
10	Material Type: Article	Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series The Astronomical journal, 2017-12, Vol.154 (6), p.220 [Peer Reviewed Journal] 2017. The American Astronomical Society. All rights reserved. ;Copyright IOP Publishing Dec 2017 ;ISSN: 0004-6256 ;EISSN: 1538-3881 ;DOI: 10.3847/1538-3881/aa9332 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
11	Material Type: Article	Deep learning enabled smart mats as a scalable floor monitoring system Nature communications, 2020-09, Vol.11 (1), p.4609-4609, Article 4609 [Peer Reviewed Journal] The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-18471-z ;PMID: 32929087 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
12	Material Type: Article	Predicting materials properties without crystal structure: deep representation learning from stoichiometry Nature communications, 2020-12, Vol.11 (1), p.6280-6280, Article 6280 [Peer Reviewed Journal] The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-19964-7 ;PMID: 33293567 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
13	Material Type: Article	Machine Learning a General-Purpose Interatomic Potential for Silicon Physical review. X, 2018-12, Vol.8 (4), p.041048, Article 041048 [Peer Reviewed Journal] 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2160-3308 ;EISSN: 2160-3308 ;DOI: 10.1103/PhysRevX.8.041048 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
14	Material Type: Article	Choosing the right molecular machine learning potential Chemical science (Cambridge), 2021-11, Vol.12 (43), p.14396-14413 [Peer Reviewed Journal] This journal is © The Royal Society of Chemistry. ;Copyright Royal Society of Chemistry 2021 ;Distributed under a Creative Commons Attribution 4.0 International License ;This journal is © The Royal Society of Chemistry 2021 The Royal Society of Chemistry ;ISSN: 2041-6520 ;EISSN: 2041-6539 ;DOI: 10.1039/d1sc03564a ;PMID: 34880991 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
15	Material Type: Article	Review on the research and practice of deep learning and reinforcement learning in smart grids CSEE Journal of Power and Energy Systems, 2018-09, Vol.4 (3), p.362-370 [Peer Reviewed Journal] 2018. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://ieeexplore.ieee.org/Xplorehelp/#/accessing-content/open-access. ;ISSN: 2096-0042 ;EISSN: 2096-0042 ;DOI: 10.17775/CSEEJPES.2018.00520 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
16	Material Type: Article	What Limits the Simulation of Quantum Computers? Physical review. X, 2020-11, Vol.10 (4), p.041038, Article 041038 [Peer Reviewed Journal] 2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 2160-3308 ;EISSN: 2160-3308 ;DOI: 10.1103/PhysRevX.10.041038 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
17	Material Type: Article	Machine learning accurate exchange and correlation functionals of the electronic density Nature communications, 2020-07, Vol.11 (1), p.3509-3509, Article 3509 [Peer Reviewed Journal] The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-17265-7 ;PMID: 32665540 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
18	Material Type: Article	Speeding Up MCMC by Efficient Data Subsampling Journal of the American Statistical Association, 2019-04, Vol.114 (526), p.831-843 [Peer Reviewed Journal] 2018 The Authors. Published with License by Taylor and Francis 2018 ;ISSN: 0162-1459 ;EISSN: 1537-274X ;DOI: 10.1080/01621459.2018.1448827 Digital Resources/Online E-Resources View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
19	Material Type: Article	Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost Nature communications, 2020-05, Vol.11 (1), p.2328-2328, Article 2328 [Peer Reviewed Journal] The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-16201-z ;PMID: 32393773 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by
20	Material Type: Article	Machine learned features from density of states for accurate adsorption energy prediction Nature communications, 2021-01, Vol.12 (1), p.88-88, Article 88 [Peer Reviewed Journal] The Author(s) 2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-20342-6 ;PMID: 33398014 Full text available View Online Details Recommendations Reviews Times Cited External Links Citations Cited by

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Results 1 - 20 of 7,337 for All Library Resources

Copyright Nature Publishing Group Aug 2018 ;ISSN: 1548-7091 ;EISSN: 1548-7105 ;DOI: 10.1038/s41592-018-0051-x ;PMID: 30013048

Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/5.0021955 ;CODEN: JCPSA6

This journal is © The Royal Society of Chemistry. ;Copyright Royal Society of Chemistry 2021 ;This journal is © The Royal Society of Chemistry 2021 The Royal Society of Chemistry ;ISSN: 2041-6520 ;EISSN: 2041-6539 ;DOI: 10.1039/d0sc06805e ;PMID: 34168840

Author(s) ;2021 Author(s). Published under an exclusive license by AIP Publishing. ;ISSN: 0003-6951 ;EISSN: 1077-3118 ;DOI: 10.1063/5.0053378 ;CODEN: APPLAB

Copyright © 2021 the Author(s). Published by PNAS. ;Copyright National Academy of Sciences May 25, 2021 ;Copyright © 2021 the Author(s). Published by PNAS. 2021 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2101784118 ;PMID: 34006645

Springer Nature Limited 2021. ;ISSN: 1476-1122 ;EISSN: 1476-4660 ;DOI: 10.1038/s41563-020-0777-6

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2160-3308 ;EISSN: 2160-3308 ;DOI: 10.1103/PhysRevX.8.011044

Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/5.0006498 ;CODEN: JCPSA6

2017. The American Astronomical Society. All rights reserved. ;Copyright IOP Publishing Dec 2017 ;ISSN: 0004-6256 ;EISSN: 1538-3881 ;DOI: 10.3847/1538-3881/aa9332

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2160-3308 ;EISSN: 2160-3308 ;DOI: 10.1103/PhysRevX.8.041048

2018. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://ieeexplore.ieee.org/Xplorehelp/#/accessing-content/open-access. ;ISSN: 2096-0042 ;EISSN: 2096-0042 ;DOI: 10.17775/CSEEJPES.2018.00520

2018 The Authors. Published with License by Taylor and Francis 2018 ;ISSN: 0162-1459 ;EISSN: 1537-274X ;DOI: 10.1080/01621459.2018.1448827

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