Phillip M. Maffettone
Phillip M. Maffettone
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A mobile robotic chemist
B Burger, PM Maffettone, VV Gusev, CM Aitchison, Y Bai, X Wang, X Li, ...
Nature 583 (7815), 237-241, 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
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
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
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
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
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
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
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
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
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
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
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
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
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
Self-driving multimodal studies at user facilities
PM Maffettone, DB Allan, SI Campbell, MR Carbone, TA Caswell, ...
arXiv preprint arXiv:2301.09177, 2023
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
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
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
Optimizing high-throughput capabilities by leveraging reinforcement learning methods with the Bluesky suite
D Olds, DB Allan, TA Caswell, J Lynch, PM Maffettone, SI Campbell
2021 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing …, 2021
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