From Data to Discovery

Funding Information: The Article Processing Charge for the publication of this research was funded by the Coordination for the Improvement of Higher Education Personnel - CAPES (ROR identifier: 00x0ma614). The authors would like to thank the funding agencies CNPq (441038/2020-4), CAPES-STINT (88881.304811/2018-01), FAPEG (202010267000272), FAPESP (Grants 2017/18611-7, 2021/06769-0, and 2021/06769-0), Instituto Serrapilheira (grant G-1709-16618), the Swedish Research Council (grants 2016-05627 and 2021-03667). J.V.B.B., L.T.F., L.C.S.A., M.L.M., J.C. received FAPESP fellowships (grants 2019/21854-4, 2019/02171-3, 2021/13809-9, 2023/07805-6, 2020/11060-8, 2018/24878-9). L.C.S.A. also received CNPq fellowship (Grants 162117/2018-3). M.P. acknowledges funding from the \u201Cla Caixa Foundation, grant HR21-848, and the European Union Horizon Europe programme (grant 101080744). CHA thanks the \u201CL\u2019Ore\u0301al-UNESCO-ABC Para Mulheres na Cie\u0301ncia\u201D and \u201CL\u2019Ore\u0301al-UNESCO International Rising Talents\u201D for the awards and fellowships received, which partially funded this work. C.H.A. and F.T.M.C. are CNPq research fellows. Publisher Copyright: © 2024 The Authors. Published by American Chemical Society.

Publication Details

Journal
Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)
Published
2024-08-08
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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From Data to Discovery

Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)
Machine Learning in Materials Science
article

From Data to Discovery

article en

Abstract

Funding Information: The Article Processing Charge for the publication of this research was funded by the Coordination for the Improvement of Higher Education Personnel - CAPES (ROR identifier: 00x0ma614). The authors would like to thank the funding agencies CNPq (441038/2020-4), CAPES-STINT (88881.304811/2018-01), FAPEG (202010267000272), FAPESP (Grants 2017/18611-7, 2021/06769-0, and 2021/06769-0), Instituto Serrapilheira (grant G-1709-16618), the Swedish Research Council (grants 2016-05627 and 2021-03667). J.V.B.B., L.T.F., L.C.S.A., M.L.M., J.C. received FAPESP fellowships (grants 2019/21854-4, 2019/02171-3, 2021/13809-9, 2023/07805-6, 2020/11060-8, 2018/24878-9). L.C.S.A. also received CNPq fellowship (Grants 162117/2018-3). M.P. acknowledges funding from the \u201Cla Caixa Foundation, grant HR21-848, and the European Union Horizon Europe programme (grant 101080744). CHA thanks the \u201CL\u2019Ore\u0301al-UNESCO-ABC Para Mulheres na Cie\u0301ncia\u201D and \u201CL\u2019Ore\u0301al-UNESCO International Rising Talents\u201D for the awards and fellowships received, which partially funded this work. C.H.A. and F.T.M.C. are CNPq research fellows. Publisher Copyright: © 2024 The Authors. Published by American Chemical Society.

Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa)
Openalex Percentile: Top 66%
Machine Learning in Materials Science
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From Data to Discovery · Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa) (2024) | TGRS Research Map | TGRS