Ward L - Search Results

1

A High-Throughput Structural and Electrochemical Study of Metallic Glass Formation in Ni-Ti-Al.

Joress H, DeCost BL, Sarker S, Braun TM, Jilani S, Smith R, Ward L, Laws KJ, Mehta A, Hattrick-Simpers JR. Joress H, et al. Among authors: ward l. ACS Comb Sci. 2020 Jul 13;22(7):330-338. doi: 10.1021/acscombsci.9b00215. Epub 2020 Jun 24. ACS Comb Sci. 2020. PMID: 32496755 Free PMC article.

2

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments.

Ren F, Ward L, Williams T, Laws KJ, Wolverton C, Hattrick-Simpers J, Mehta A. Ren F, et al. Among authors: ward l. Sci Adv. 2018 Apr 13;4(4):eaaq1566. doi: 10.1126/sciadv.aaq1566. eCollection 2018 Apr. Sci Adv. 2018. PMID: 29662953 Free PMC article.

3

Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms.

Dandu N, Ward L, Assary RS, Redfern PC, Narayanan B, Foster IT, Curtiss LA. Dandu N, et al. Among authors: ward l. J Phys Chem A. 2020 Jul 16;124(28):5804-5811. doi: 10.1021/acs.jpca.0c01777. Epub 2020 Jul 2. J Phys Chem A. 2020. PMID: 32539388

4

Three new crystal structures in the Na-Pb system: solving structures without additional experimental input.

Ward L, Michel K, Wolverton C. Ward L, et al. Acta Crystallogr A Found Adv. 2015 Sep;71(Pt 5):542-8. doi: 10.1107/S2053273315012516. Epub 2015 Aug 28. Acta Crystallogr A Found Adv. 2015. PMID: 26317197

5

E_min: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability.

Lee AS, Elliott S, Harb H, Ward L, Foster I, Curtiss L, Assary RS. Lee AS, et al. Among authors: ward l. J Chem Inf Model. 2024 Feb 26;64(4):1277-1289. doi: 10.1021/acs.jcim.3c01583. Epub 2024 Feb 15. J Chem Inf Model. 2024. PMID: 38359461

6

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition.

Jha D, Ward L, Paul A, Liao WK, Choudhary A, Wolverton C, Agrawal A. Jha D, et al. Among authors: ward l. Sci Rep. 2018 Dec 4;8(1):17593. doi: 10.1038/s41598-018-35934-y. Sci Rep. 2018. PMID: 30514926 Free PMC article.

7

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl.

Sivaraman G, Guo J, Ward L, Hoyt N, Williamson M, Foster I, Benmore C, Jackson N. Sivaraman G, et al. Among authors: ward l. J Phys Chem Lett. 2021 May 6;12(17):4278-4285. doi: 10.1021/acs.jpclett.1c00901. Epub 2021 Apr 28. J Phys Chem Lett. 2021. PMID: 33908789

8

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models.

Ward L, Dandu N, Blaiszik B, Narayanan B, Assary RS, Redfern PC, Foster I, Curtiss LA. Ward L, et al. J Phys Chem A. 2021 Jul 15;125(27):5990-5998. doi: 10.1021/acs.jpca.1c01960. Epub 2021 Jun 30. J Phys Chem A. 2021. PMID: 34191512

9

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning.

Dandu NK, Ward L, Assary RS, Redfern PC, Curtiss LA. Dandu NK, et al. Among authors: ward l. J Phys Chem A. 2023 Jul 20;127(28):5914-5920. doi: 10.1021/acs.jpca.3c00823. Epub 2023 Jul 5. J Phys Chem A. 2023. PMID: 37406209

10

Enabling deeper learning on big data for materials informatics applications.

Jha D, Gupta V, Ward L, Yang Z, Wolverton C, Foster I, Liao WK, Choudhary A, Agrawal A. Jha D, et al. Among authors: ward l. Sci Rep. 2021 Feb 19;11(1):4244. doi: 10.1038/s41598-021-83193-1. Sci Rep. 2021. PMID: 33608599 Free PMC article.

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