Sergei Manzhos

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Prashant SonarProfessor & ARC Future Fellow, Organic and Printed Electronic Research Group @QUTVerified email at qut.edu.au
T CarringtonQueen's UniversityVerified email at queensu.ca
Krishna Feronkrishna.feron@newcastle.edu.auVerified email at newcastle.edu.au
Daniel KochResearch Associate, German Aerospace Center (DLR)Verified email at dlr.de
Yingqian ChenResearch Fellow, National University of SingaporeVerified email at u.nus.edu
Hong Duc PhamPhD, Postdoctoral Research Fellow (Novel Energy Technologies), Centre for Future Materials @UniSQVerified email at unisq.edu.au
Oleksandr I. MalyiInverse Materials Design groupVerified email at e.ntu.edu.sg
John BellUniversity of Southern QueenslandVerified email at unisq.edu.au
Qian LIU (刘谦)ARC DECRA Fellow at Queensland University of Technology (QUT)Verified email at qut.edu.au
Il JeonAssociate Professor & Vice Provost, Sungkyunkwan University (SKKU)Verified email at spc.oxon.org
Johann LüderNational Sun Yat-sen UniversityVerified email at mail.nsysu.edu.tw
Steven BottleChemistry QUT Queensland University of TechnologyVerified email at qut.edu.au
Hiroshi SegawaVerified email at mail.ecc.u-tokyo.ac.jp
Teck Leong TanInstitute of High Performance Computing SingaporeVerified email at ihpc.a-star.edu.sg
Vadym KulishNational University of SingaporeVerified email at nus.edu.sg
Hans-Peter LoockUniversity of Victoria (Queen's University 1999-2019)Verified email at uvic.ca
Yutaka MatsuoNagoya UniversityVerified email at chem.material.nagoya-u.ac.jp
Shigeo MaruyamaProfessor of Mechanical Engineering, The University of TokyoVerified email at photon.t.u-tokyo.ac.jp
Hao-Sheng LinAssistant Professor, Nagoya UniversityVerified email at material.nagoya-u.ac.jp
Amrita PalNUS - National University of SingaporeVerified email at nus.edu.sg

Sergei Manzhos

Associate Professor, Ihara-Manzhos laboratory, Tokyo Institute of Technology

Verified email at m.titech.ac.jp - Homepage

machine learning solar cells fuel cells computational spectroscopy large-scale ab initio


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A random-sampling high dimensional model representation neural network for building potential energy surfaces S Manzhos, T Carrington The Journal of chemical physics 125 (8), 2006	283	2006
Neural network potential energy surfaces for small molecules and reactions S Manzhos, T Carrington Jr Chemical Reviews 121 (16), 10187-10217, 2020	250	2020
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy A Kamath, RA Vargas-Hernández, RV Krems, T Carrington, S Manzhos The Journal of chemical physics 148 (24), 2018	224	2018
Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces S Manzhos, R Dawes, T Carrington International Journal of Quantum Chemistry 115 (16), 1012-1020, 2015	214	2015
A nested molecule-independent neural network approach for high-quality potential fits S Manzhos, X Wang, R Dawes, T Carrington The Journal of Physical Chemistry A 110 (16), 5295-5304, 2006	214	2006
Using neural networks to represent potential surfaces as sums of products S Manzhos, T Carrington The Journal of chemical physics 125 (19), 2006	200	2006
Organic interfacial materials for perovskite-based optoelectronic devices HD Pham, L Xianqiang, W Li, S Manzhos, AKK Kyaw, P Sonar Energy & Environmental Science 12 (4), 1177-1209, 2019	199	2019
Molecular engineering using an anthanthrone dye for low‐cost hole transport materials: a strategy for dopant‐free, high‐efficiency, and stable perovskite solar cells HD Pham, TT Do, J Kim, C Charbonneau, S Manzhos, K Feron, WC Tsoi, ... Advanced Energy Materials 8 (16), 1703007, 2018	166	2018
Phenothiazine and carbazole substituted pyrene based electroluminescent organic semiconductors for OLED devices JK Salunke, FL Wong, K Feron, S Manzhos, MF Lo, D Shinde, A Patil, ... Journal of Materials Chemistry C 4 (5), 1009-1018, 2016	131	2016
Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022	129	2022
In search of high performance anode materials for Mg batteries: Computational studies of Mg in Ge, Si, and Sn OI Malyi, TL Tan, S Manzhos Journal of Power Sources 233, 341-345, 2013	123	2013
Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface S Manzhos, T Carrington The Journal of chemical physics 129 (22), 2008	122	2008
Using redundant coordinates to represent potential energy surfaces with lower-dimensional functions S Manzhos, T Carrington The Journal of chemical physics 127 (1), 2007	119	2007
On the charge state of titanium in titanium dioxide D Koch, S Manzhos The journal of physical chemistry letters 8 (7), 1593-1598, 2017	110	2017
A computational study of Na behavior on graphene OI Malyi, K Sopiha, VV Kulish, TL Tan, S Manzhos, C Persson Applied Surface Science 333, 235-243, 2015	109	2015
Insertion energetics of lithium, sodium, and magnesium in crystalline and amorphous titanium dioxide: A comparative first-principles study F Legrain, O Malyi, S Manzhos Journal of Power Sources 278, 197-202, 2015	104	2015
Photofragment image analysis using the Onion-Peeling Algorithm S Manzhos, HP Loock Computer physics communications 154 (1), 76-87, 2003	102	2003
A comparative computational study of structures, diffusion, and dopant interactions between Li and Na insertion into Si OI Malyi, TL Tan, S Manzhos Applied Physics Express 6 (2), 027301, 2013	98	2013
Machine learning for the solution of the Schrödinger equation S Manzhos Machine Learning: Science and Technology 1 (1), 013002, 2020	94	2020
A computational study of the insertion of Li, Na, and Mg atoms into Si (111) nanosheets O Malyi, VV Kulish, TL Tan, S Manzhos Nano Energy 2 (6), 1149-1157, 2013	90	2013

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