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Logan Ward
Logan Ward
Argonne National Laboratory, Data Science and Learning Division
Verified email at anl.gov - Homepage
Title
Cited by
Cited by
Year
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
12052016
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
6782018
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
4752018
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), 17593, 2018
4052018
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
3662017
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
2382018
Atomistic calculations and materials informatics: A review
L Ward, C Wolverton
Current Opinion in Solid State and Materials Science 21 (3), 167-176, 2017
2342017
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
2142018
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
1492019
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
1052018
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
992019
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—Condensed Matter and Materials Physics 88 (13), 134205, 2013
962013
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
832021
Feature engineering for machine learning enabled early prediction of battery lifetime
NH Paulson, J Kubal, L Ward, S Saxena, W Lu, SJ Babinec
Journal of Power Sources 527, 231127, 2022
742022
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
KM Jablonka, Q Ai, A Al-Feghali, S Badhwar, JD Bocarsly, AM Bran, ...
Digital Discovery 2 (5), 1233-1250, 2023
672023
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
572019
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
M Zvyagin, A Brace, K Hippe, Y Deng, B Zhang, CO Bohorquez, A Clyde, ...
The International Journal of High Performance Computing Applications 37 (6 …, 2023
522023
Enabling deeper learning on big data for materials informatics applications
D Jha, V Gupta, L Ward, Z Yang, C Wolverton, I Foster, W Liao, ...
Scientific reports 11 (1), 4244, 2021
522021
Principles of the battery data genome
L Ward, S Babinec, EJ Dufek, DA Howey, V Viswanathan, M Aykol, ...
Joule 6 (10), 2253-2271, 2022
512022
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
502019
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