机器学习在材料设计方面的研究进展

[1] Predicting the thermodynamic stability of perovskite oxides using machine learning models[J] . Wei Li,Ryan Jacobs,Dane Morgan.Computational Materials Science . 2018

[2] Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing[J] . Juan-Pablo Correa-Baena,Kedar Hippalgaonkar,Jeroen van Duren,Shaffiq Jaffer,Vijay R. Chandrasekhar,Vladan Stevanovic,Cyrus Wadia,Supratik Guha,Tonio Buonassisi.Joule . 2018

[3] Materials discovery and design using machine learning[J] . Yue Liu,Tianlu Zhao,Wangwei Ju,Siqi Shi.Journal of Materiomics . 2017

[4] Public （Q）SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development[J] . Igor V. Tetko,Uko Maran,Alexander Tropsha.Molecular Informatics . 2017 (3)

[5] Role of materials data science and informatics in accelerated materials innovation [J]. MRS BULLETIN, 2016, 41 (08) : 596 - 602

[6] Materials science with large-scale data and informatics: Unlocking new opportunities[J] . Joanne Hill,Gregory Mulholland,Kristin Persson,Ram Seshadri,Chris Wolverton,Bryce Meredig.MRS Bulletin . 2016 (5)

[7] Human-level concept learning through probabilistic program induction [J]. SCIENCE, 2015, 350 (6266) : 1332 - 1338

[8] Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles[J] . Wladislaw Waag,Christian Fleischer,Dirk Uwe Sauer.Journal of Power Sources . 2014

[9] AFLOW: An automatic framework for high-throughput materials discovery[J] . Stefano Curtarolo,Wahyu Setyawan,Gus L.W. Hart,Michal Jahnatek,Roman V. Chepulskii,Richard H. Taylor,Shidong Wang,Junkai Xue,Kesong Yang,Ohad Levy,Michael J. Mehl,Harold T. Stokes,Denis O. Demchenko,Dane Morgan.Computational Materials Science . 2012

[10] The Cambridge Structural Database: a quarter of a million crystal structures and rising[J] . Frank H.Allen.Acta Cryst. B . 2008 (3‐1)

[1] Predicting the thermodynamic stability of perovskite oxides using machine learning models[J] . Wei Li,Ryan Jacobs,Dane Morgan.Computational Materials Science . 2018

[2] Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing[J] . Juan-Pablo Correa-Baena,Kedar Hippalgaonkar,Jeroen van Duren,Shaffiq Jaffer,Vijay R. Chandrasekhar,Vladan Stevanovic,Cyrus Wadia,Supratik Guha,Tonio Buonassisi.Joule . 2018

[3] Materials discovery and design using machine learning[J] . Yue Liu,Tianlu Zhao,Wangwei Ju,Siqi Shi.Journal of Materiomics . 2017

[4] Public （Q）SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development[J] . Igor V. Tetko,Uko Maran,Alexander Tropsha.Molecular Informatics . 2017 (3)

[5] Role of materials data science and informatics in accelerated materials innovation [J]. MRS BULLETIN, 2016, 41 (08) : 596 - 602

[6] Materials science with large-scale data and informatics: Unlocking new opportunities[J] . Joanne Hill,Gregory Mulholland,Kristin Persson,Ram Seshadri,Chris Wolverton,Bryce Meredig.MRS Bulletin . 2016 (5)

[7] Human-level concept learning through probabilistic program induction [J]. SCIENCE, 2015, 350 (6266) : 1332 - 1338

[8] Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles[J] . Wladislaw Waag,Christian Fleischer,Dirk Uwe Sauer.Journal of Power Sources . 2014

[9] AFLOW: An automatic framework for high-throughput materials discovery[J] . Stefano Curtarolo,Wahyu Setyawan,Gus L.W. Hart,Michal Jahnatek,Roman V. Chepulskii,Richard H. Taylor,Shidong Wang,Junkai Xue,Kesong Yang,Ohad Levy,Michael J. Mehl,Harold T. Stokes,Denis O. Demchenko,Dane Morgan.Computational Materials Science . 2012

[10] The Cambridge Structural Database: a quarter of a million crystal structures and rising[J] . Frank H.Allen.Acta Cryst. B . 2008 (3‐1)