A mobile robotic chemist B Burger, PM Maffettone, VV Gusev, CM Aitchison, Y Bai, X Wang, X Li, ... Nature 583 (7815), 237-241, 2020 | 1085 | 2020 |
On-demand hydrogen generation using nanosilicon: splitting water without light, heat, or electricity F Erogbogbo, T Lin, PM Tucciarone, KM LaJoie, L Lai, GD Patki, ... Nano letters 13 (2), 451-456, 2013 | 207 | 2013 |
Accelerating bayesian optimization for biological sequence design with denoising autoencoders S Stanton, W Maddox, N Gruver, P Maffettone, E Delaney, P Greenside, ... International Conference on Machine Learning, 20459-20478, 2022 | 92 | 2022 |
Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules X Li, PM Maffettone, Y Che, T Liu, L Chen, AI Cooper Chemical Science 12 (32), 10742-10754, 2021 | 73 | 2021 |
Crystallography companion agent for high-throughput materials discovery PM Maffettone, L Banko, P Cui, Y Lysogorskiy, MA Little, D Olds, A Ludwig, ... Nature Computational Science 1 (4), 290-297, 2021 | 72 | 2021 |
Creating ligand-free silicon germanium alloy nanocrystal inks F Erogbogbo, T Liu, N Ramadurai, P Tuccarione, L Lai, MT Swihart, ... ACS nano 5 (10), 7950-7959, 2011 | 51 | 2011 |
Deep learning for visualization and novelty detection in large X-ray diffraction datasets L Banko, PM Maffettone, D Naujoks, D Olds, A Ludwig Npj Computational Materials 7 (1), 104, 2021 | 46 | 2021 |
Negative Hydration Expansion in : Microscopic Mechanism, Spaghetti Dynamics, and Negative Thermal Expansion M Baise, PM Maffettone, F Trousselet, NP Funnell, FX Coudert, ... Physical Review Letters 120 (26), 265501, 2018 | 35 | 2018 |
Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments PM Maffettone, AC Daly, D Olds Applied Physics Reviews 8 (4), 2021 | 24 | 2021 |
What is missing in autonomous discovery: open challenges for the community PM Maffettone, P Friederich, SG Baird, B Blaiszik, KA Brown, SI Campbell, ... Digital Discovery 2 (6), 1644-1659, 2023 | 22 | 2023 |
Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities PM Maffettone, JK Lynch, TA Caswell, CE Cook, SI Campbell, D Olds Machine Learning: Science and Technology 2 (2), 025025, 2021 | 22 | 2021 |
Effective surrogate models for protein design with bayesian optimization N Gruver, S Stanton, P Kirichenko, M Finzi, P Maffettone, V Myers, ... ICML Workshop on Computational Biology 183, 2021 | 20 | 2021 |
Machine learning enabling high-throughput and remote operations at large-scale user facilities T Konstantinova, PM Maffettone, B Ravel, SI Campbell, AM Barbour, ... Digital Discovery 1 (4), 413-426, 2022 | 14 | 2022 |
Ferromagnetic resonance in a topographically modulated permalloy film J Sklenar, P Tucciarone, RJ Lee, D Tice, RPH Chang, SJ Lee, ... Physical Review B 91 (13), 134424, 2015 | 9 | 2015 |
Self-driving multimodal studies at user facilities PM Maffettone, DB Allan, SI Campbell, MR Carbone, TA Caswell, ... arXiv preprint arXiv:2301.09177, 2023 | 8 | 2023 |
Advancing discovery with artificial intelligence and machine learning at NSLS-II A Barbour, S Campbell, T Caswell, M Fukuto, M Hanwell, A Kiss, ... Synchrotron Radiation News 35 (4), 44-50, 2022 | 8 | 2022 |
Delivering real-time multi-modal materials analysis with enterprise beamlines PM Maffettone, S Campbell, MD Hanwell, S Wilkins, D Olds Cell Reports Physical Science 3 (11), 2022 | 6 | 2022 |
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 | 6 | 2022 |
Inverse Protein Folding Using Deep Bayesian Optimization N Maus, Y Zeng, DA Anderson, P Maffettone, A Solomon, P Greenside, ... arXiv preprint arXiv:2305.18089, 2023 | 4 | 2023 |
Flexible formulation of value for experiment interpretation and design MR Carbone, HJ Kim, C Fernando, S Yoo, D Olds, H Joress, B DeCost, ... Matter 7 (2), 685-696, 2024 | 2 | 2024 |