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Phil Maffettone

Associate Computational Scientist, Data Science and Systems Integration Program, National Synchrotron Light Source II

Brookhaven National Laboratory

National Synchrotron Light Source II
Bldg. 741
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-7604
pmaffetto@bnl.gov

Pronouns: he/him

Phil is a Computational Scientist in the Data Analysis and Workflow Integration group of the Data Science and Systems Integration Division. He leads research projects focused on accelerating scientific discovery at user facilities by leveraging robotics, artificial intelligence, and human-facility-algorithm interfaces. He is also the project leader for N3XTware, building next-generation software infrastructure for new beamlines.

Expertise

Prior to joining NSLS-II, Phil built the brain on the world's first mobile robotic scientist with a collborative team the University of Liverpool. He has advanced similar initiatives here, building software for autonomous multimodal and multibeamline experiment orchestration. He is passionate about the democratization of scientific tools and open source software. With over a decade of experience in R&D across science and engineering, Phil thrives with new challenges at the intersections of disciplines. His breadth of experience includes start-ups, software development, artificial intelligence, automation, engineering, and skilled trades. This range enables effectiveness at managing complex projects and diverse teams.

Research Activities

ILLUMINE - Intelligent Learning for Light Source and Neutron Source User Measurements including Navigation and Experiment Steering
N3XTware - Software architecture project for NSLS-II Experimental Tools III (NEXT-III) Project for beamline development.

Education

DPhil in Inorganic Chemistry, University of Oxford, 2018
B.S. in Chemical Engineering, University at Buffalo, 2014

Selected Publications

Fernando C, Olds D, Campbell SI, Maffettone PM (2024) Facile Integration of Robots into Experimental Orchestration at Scientific User Facilities. 2024 IEEE International Conference on Robotics and Automation (ICRA) 9578–9584. https://doi.org/10.1109/icra57147.2024.10611706
Maffettone PM, Allan DB, Barbour A, et al (2023) Artificial Intelligence Driven Experiments at User Facilities. Methods and Applications of Autonomous Experimentation 121–151. https://doi.org/10.1201/9781003359593-8
Carbone MR, Kim HJ, Fernando C, et al (2024) Flexible formulation of value for experiment interpretation and design. Matter 7:685–696. https://doi.org/10.1016/j.matt.2023.11.012
Maffettone PM, Friederich P, Baird SG, et al (2023) What is missing in autonomous discovery: open challenges for the community. Digital Discovery 2:1644–1659. https://doi.org/10.1039/d3dd00143a
Maffettone PM, Campbell S, Hanwell MD, et al (2022) Delivering real-time multi-modal materials analysis with enterprise beamlines. Cell Reports Physical Science 3:101112. https://doi.org/10.1016/j.xcrp.2022.101112
Maffettone PM, Banko L, Cui P, Lysogorskiy Y, Little MA, Olds D, Ludwig A, Cooper AI (2021) Crystallography companion agent for high-throughput materials discovery. Nature Computational Science 1:290–297. doi: 10.1038/s43588-021-00059-2
Maffettone PM, Lynch JK, Caswell TA, Cook CE, Campbell SI, Olds D (2021) Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities. Machine Learning: Science and Technology 2:025025. doi: 10.1088/2632-2153/abc9fc
Burger B, Maffettone PM, Gusev VV, Aitchison CM, Bai Y, Wang X, Li X, Alston BM, Li B, Clowes R, Rankin N, Harris B, Sprick RS, Cooper AI (2020) A mobile robotic chemist. Nature 583:237–241. doi: 10.1038/s41586-020-2442-2