Phillip M. Maffettone

Phillip M. Maffettone

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Phillip M. Maffettone

National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973, USA

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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8 July 2020 in Nature
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Journal Article
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BBBenjamin Burger
Benjamin Burger
PMPhillip M. Maffettone
Phillip M. Maffettone
VGVladimir V. Gusev
Vladimir V. Gusev
CACatherine M. Aitchison
Catherine M. Aitchison
YBYang Bai
Yang Bai
ACAndrew I. Cooper
Andrew I. Cooper

17 January 2013 in Nano Letters
Verified
Journal Article
Closed access
FEFolarin Erogbogbo
Folarin Erogbogbo
TLTao Lin
Tao Lin
PTPhillip M. Tucciarone
Phillip M. Tucciarone
KLKrystal M. LaJoie
Krystal M. LaJoie
LLLarry Lai
Larry Lai
MSMak T Swihart
Mak T Swihart

19 April 2021 in Nature Computational Science
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Journal Article
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PMPhillip M. Maffettone
Phillip M. Maffettone
LBLars Banko
Lars Banko
PCPeng Cui
Peng Cui
YLYury Lysogorskiy
Yury Lysogorskiy
MLMarc A. Little
Marc A. Little
ACAndrew I. Cooper
Andrew I. Cooper

9 July 2021 in npj Computational Materials
Verified
Journal Article
Open access
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LBLars Banko
Lars Banko
PMPhillip M. Maffettone
Phillip M. Maffettone
DNDennis Naujoks
Dennis Naujoks
DODaniel Olds
Daniel Olds
ALAlfred Ludwig
Alfred Ludwig
We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

16 October 2023 in Digital Discovery
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Journal Article
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PMPhillip M. Maffettone
Phillip M. Maffettone
PFPascal Friederich
Pascal Friederich
SBSterling G. Baird
Sterling G. Baird
BBBen Blaiszik
Ben Blaiszik
KBKeith A. Brown
Keith A. Brown
SSShijing Sun
Shijing Sun
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery.

29 June 2018 in Physical Review Letters
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Journal Article
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MBMia Baise
Mia Baise
PMPhillip M. Maffettone
Phillip M. Maffettone
FTFabien Trousselet
Fabien Trousselet
NFNicholas P. Funnell
Nicholas P. Funnell
FCFrançois-Xavier Coudert
François-Xavier Coudert
AGAndrew L. Goodwin
Andrew L. Goodwin
We use a combination of x-ray diffraction, total scattering, and quantum mechanical calculations to determine the mechanism responsible for hydration-driven contraction in ZrW2O8. The inclusion of H2O molecules within the ZrW2O8 network drives the concerted formation of new W─O bonds to give one-dimensional (─W─O─)n strings. The topology of the ZrW2O8 network is such that there is no unique choice for the string trajectories: the same local changes in coordination can propagate with a large number of different periodicities. Consequently, ZrW2O8·H2O is heavily disordered, with each configuration of strings forming a dense aperiodic “spaghetti.” This new connectivity contracts the unit cell via large shifts in the Zr and W atom positions. Fluctuations of the undistorted parent structure towards this spaghetti phase emerge as the key negative thermal expansion (NTE) phonon modes in ZrW2O8 itself. The large relative density of NTE phonon modes in ZrW2O8 actually reflects the degeneracy of volume-contracting spaghetti excitations, itself a function of the particular topology of this remarkable material.