Logan Ward
Logan Ward
Argonne National Laboratory, Data Science and Learning Division
Verified email at anl.gov - Homepage
Cited by
Cited by
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
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
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
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
Atomistic calculations and materials informatics: A review
L Ward, C Wolverton
Current Opinion in Solid State and Materials Science 21 (3), 167-176, 2017
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
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
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
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
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
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
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
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
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
A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials 2
L Ward, A Agrawal, A Choudhary, C Wolverton
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
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
Automated crystal structure solution from powder diffraction data: Validation of the first-principles-assisted structure solution method
L Ward, K Michel, C Wolverton
Physical Review Materials 1 (6), 063802, 2017
Towards a hybrid human-computer scientific information extraction pipeline
RB Tchoua, K Chard, DJ Audus, LT Ward, J Lequieu, JJ De Pablo, ...
2017 IEEE 13th International Conference on e-Science (e-Science), 109-118, 2017
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
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