フォロー
Logan Ward
Logan Ward
Argonne National Laboratory, Data Science and Learning Division
確認したメール アドレス: anl.gov - ホームページ
タイトル
引用先
引用先
A general-purpose machine learning framework for predicting properties of inorganic materials
L Ward, A Agrawal, A Choudhary, C Wolverton
npj Computational Materials 2, 16028, 2016
7532016
Matminer: An open source toolkit for materials data mining
L Ward, A Dunn, A Faghaninia, NER Zimmermann, S Bajaj, Q Wang, ...
Computational Materials Science 152, 60-69, 2018
3322018
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
F Ren, L Ward, T Williams, KJ Laws, C Wolverton, J Hattrick-Simpers, ...
Science advances 4 (4), eaaq1566, 2018
3122018
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
L Ward, R Liu, A Krishna, VI Hegde, A Agrawal, A Choudhary, ...
Physical Review B 96 (2), 024104, 2017
2362017
Elemnet: Deep learning the chemistry of materials from only elemental composition
D Jha, L Ward, A Paul, W Liao, A Choudhary, C Wolverton, A Agrawal
Scientific reports 8 (1), 1-13, 2018
2052018
Atomistic calculations and materials informatics: A review
L Ward, C Wolverton
Current Opinion in Solid State and Materials Science 21 (3), 167-176, 2017
1772017
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
B Meredig, E Antono, C Church, M Hutchinson, J Ling, S Paradiso, ...
Molecular Systems Design & Engineering 3 (5), 819-825, 2018
1352018
A machine learning approach for engineering bulk metallic glass alloys
L Ward, SC O'Keeffe, J Stevick, GR Jelbert, M Aykol, C Wolverton
Acta Materialia 159, 102-111, 2018
1272018
Structural evolution and kinetics in Cu-Zr metallic liquids from molecular dynamics simulations
L Ward, D Miracle, W Windl, ON Senkov, K Flores
Physical Review B 88 (13), 134205, 2013
862013
A data ecosystem to support machine learning in materials science
B Blaiszik, L Ward, M Schwarting, J Gaff, R Chard, D Pike, K Chard, ...
MRS Communications 9 (4), 1125-1133, 2019
782019
DLHub: Model and data serving for science
R Chard, Z Li, K Chard, L Ward, Y Babuji, A Woodard, S Tuecke, ...
2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2019
702019
Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds
K Kim, L Ward, J He, A Krishna, A Agrawal, C Wolverton
Physical Review Materials 2 (12), 123801, 2018
662018
The MolSSI QCArchive project: An open‐source platform to compute, organize, and share quantum chemistry data
DGA Smith, D Altarawy, LA Burns, M Welborn, LN Naden, L Ward, S Ellis, ...
Wiley Interdisciplinary Reviews: Computational Molecular Science 11 (2), e1491, 2021
462021
Simulation of discrete damage in composite overheight compact tension specimens
D Mollenhauer, L Ward, E Iarve, S Putthanarat, K Hoos, S Hallett, X Li
Composites Part A: Applied Science and Manufacturing 43 (10), 1667-1679, 2012
412012
Rapid production of accurate embedded-atom method potentials for metal alloys
L Ward, A Agrawal, KM Flores, W Windl
arXiv preprint arXiv:1209.0619, 2012
382012
Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations
L Ward, B Blaiszik, I Foster, RS Assary, B Narayanan, L Curtiss
MRS Communications 9 (3), 891-899, 2019
312019
Strategies for accelerating the adoption of materials informatics
L Ward, M Aykol, B Blaiszik, I Foster, B Meredig, J Saal, S Suram
MRS Bulletin 43 (9), 683-689, 2018
282018
Irnet: A general purpose deep residual regression framework for materials discovery
D Jha, L Ward, Z Yang, C Wolverton, I Foster, W Liao, A Choudhary, ...
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
272019
An embedded atom method potential of beryllium
A Agrawal, R Mishra, L Ward, KM Flores, W Windl
Modelling and Simulation in Materials Science and Engineering 21 (8), 085001, 2013
202013
Quantum-chemically informed machine learning: prediction of energies of organic molecules with 10 to 14 non-hydrogen atoms
N Dandu, L Ward, RS Assary, PC Redfern, B Narayanan, IT Foster, ...
The Journal of Physical Chemistry A 124 (28), 5804-5811, 2020
192020
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