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 Scientific Data Analysis and Visualization group of the Data Science and Systems Integration Program. He leads research projects focused on accelerating scientific discovery at user facilities by leveraging robotics, artificial intelligence, machine learning, and human-facility-algorithm interfaces. He is also the project leader for N3XTware, a software architecture project inside the NSLS-II Experimental Tools III (NEXT-III) Project for beamline development.
Expertise | Education | Publications | Highlights | Awards
Expertise
Working with a collaborative team, Phil built the brain on the world's first mobile robotic scientist, and 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, scientific software development, machine learning, automation, engineering, and skilled trades. This range enables effectiveness at managing complex projects and diverse teams.
Education
- DPhil in Inorganic Chemistry, University of Oxford, 2018
- B.S. in Chemical Engineering, University at Buffalo, 2014
Selected Publications
- 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
Research Highlights
An Internet of Things Approach to Enterprise Beamlines
Gamification of Beamline Science to Leverage Reinforcement Learning for Scientific Measurements
Artificial Intelligence Innovations for Crystallography
Awards & Recognition
-
Super Artificial Intelligence Leader (SAIL) Award
-
Marshall Scholarship
-
NSF Graduate Research Fellowship

Brookhaven National Laboratory
National Synchrotron Light Source II
Bldg. 741
P.O. Box 5000
Upton, NY 11973-5000
(631) 344-7604
pmaffetto@bnl.gov