| A kriging-based approach to autonomous experimentation with applications to x-ray scattering MM Noack, KG Yager, M Fukuto, GS Doerk, R Li, JA Sethian Scientific reports 9 (1), 11809, 2019 | 110 | 2019 |
| Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels MM Noack, GS Doerk, R Li, JK Streit, RA Vaia, KG Yager, M Fukuto Scientific reports 10, 2020 | 94 | 2020 |
| Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels MM Noack, GS Doerk, R Li, JK Streit, RA Vaia, KG Yager, M Fukuto arXiv preprint arXiv:2006.02489, 2020 | 94 | 2020 |
| Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities MM Noack, PH Zwart, DM Ushizima, M Fukuto, KG Yager, KC Elbert, ... Nature Reviews Physics 3 (10), 685-697, 2021 | 84 | 2021 |
| Advances in kriging-based autonomous x-ray scattering experiments MM Noack, GS Doerk, R Li, M Fukuto, KG Yager Scientific reports 10 (1), 1325, 2020 | 45 | 2020 |
| Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends GS Doerk, A Stein, S Bae, MM Noack, M Fukuto, KG Yager Science Advances 9 (2), eadd3687, 2023 | 32 | 2023 |
| Hybrid genetic deflated newton method for global optimisation MM Noack, SW Funke Journal of Computational and Applied Mathematics 325, 97-112, 2017 | 31 | 2017 |
| Instrumentation for In situ/Operando X-ray Scattering Studies of Polymer Additive Manufacturing Processes L Wiegart, GS Doerk, M Fukuto, S Lee, R Li, G Marom, MM Noack, ... Synchrotron Radiation News 32 (2), 20-27, 2019 | 29 | 2019 |
| Advanced stationary and nonstationary kernel designs for domain-aware gaussian processes MM Noack, JA Sethian Communications in Applied Mathematics and Computational Science 17 (1), 131-156, 2022 | 27 | 2022 |
| Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels MM Noack, H Krishnan, MD Risser, KG Reyes Scientific reports 13 (1), 3155, 2023 | 20 | 2023 |
| Autonomous scanning probe microscopy investigations over WS2 and Au{111} JC Thomas, A Rossi, D Smalley, L Francaviglia, Z Yu, T Zhang, S Kumari, ... npj Computational Materials 8 (1), 99, 2022 | 19 | 2022 |
| Autonomous x-ray scattering KG Yager, PW Majewski, MM Noack, M Fukuto Nanotechnology 34 (32), 322001, 2023 | 18 | 2023 |
| K-means-driven Gaussian process data collection for angle-resolved photoemission spectroscopy CN Melton, MM Noack, T Ohta, TE Beechem, J Robinson, X Zhang, ... Machine Learning: Science and Technology 1 (4), 045015, 2020 | 16 | 2020 |
| Statistical and machine learning-based durability-testing strategies for energy storage SJ Harris, MM Noack Joule 7 (5), 920-934, 2023 | 15 | 2023 |
| Mathematical nuances of Gaussian process-driven autonomous experimentation MM Noack, KG Reyes MRS Bulletin 48 (2), 153-163, 2023 | 10 | 2023 |
| Acoustic wave and eikonal equations in a transformed metric space for various types of anisotropy MM Noack, S Clark Heliyon 3 (3), 2017 | 8 | 2017 |
| Machine-learning for designing nanoarchitectured materials by dealloying C Zhao, CC Chung, S Jiang, MM Noack, JH Chen, K Manandhar, J Lynch, ... Communications Materials 3 (1), 86, 2022 | 7 | 2022 |
| A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation JC Thomas, W Chen, Y Xiong, BA Barker, J Zhou, W Chen, A Rossi, ... Nature communications 15 (1), 3556, 2024 | 6 | 2024 |
| A unifying perspective on non-stationary kernels for deeper Gaussian processes MM Noack, H Luo, MD Risser APL Machine Learning 2 (1), 2024 | 6 | 2024 |
| Methods and Applications of Autonomous Experimentation M Noack, D Ushizima CRC Press, 2024 | 6 | 2024 |
Kevin G. YagerCenter for Functional Nanomaterials, Brookhaven National LaboratoryVerified email at bnl.gov
