Mo Li

Mo Li

orcid

Research Focus & Metrics

Academic Career Length

34Years (1993-Present)

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

Representative Publications

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28 January 2007 in Nature Nanotechnology
Verified
Journal Article
Closed access
MLMo Li
Mo Li
HTH. X. Tang
H. X. Tang
MRM. L. Roukes
M. L. Roukes

2 March 2015 in Nature Photonics
Verified
Journal Article
Closed access
PDF file
NYNathan Youngblood
Nathan Youngblood
CCChe Chen
Che Chen
SKSteven J. Koester
Steven J. Koester
MLMo Li
Mo Li

24 November 2020 in Nature Communications
Verified
Journal Article
Open access
PDF file
AKA. Gilad Kusne
A. Gilad Kusne
HYHeshan Yu
Heshan Yu
CWChangming Wu
Changming Wu
HZHuairuo Zhang
Huairuo Zhang
JHJason Hattrick-Simpers
Jason Hattrick-Simpers
ITIchiro Takeuchi
Ichiro Takeuchi
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

1 January 2012 in Nature Communications
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Journal Article
Open access
PDF file
WPW.H.P. Pernice
W.H.P. Pernice
CSC. Schuck
C. Schuck
OMO. Minaeva
O. Minaeva
MLMo Li
Mo Li
GGG.N. Goltsman
G.N. Goltsman
HTH.X. Tang
H.X. Tang
Ultrafast, high-efficiency single-photon detectors are among the most sought-after elements in modern quantum optics and quantum communication. However, imperfect modal matching and finite photon absorption rates have usually limited their maximum attainable detection efficiency. Here we demonstrate superconducting nanowire detectors atop nanophotonic waveguides, which enable a drastic increase of the absorption length for incoming photons. This allows us to achieve high on-chip single-photon detection efficiency up to 91% at telecom wavelengths, repeatable across several fabricated chips. We also observe remarkably low dark count rates without significant compromise of the on-chip detection efficiency. The detectors are fully embedded in scalable silicon photonic circuits and provide ultrashort timing jitter of 18 ps. Exploiting this high temporal resolution, we demonstrate ballistic photon transport in silicon ring resonators. Our direct implementation of a high-performance single-photon detector on chip overcomes a major barrier in integrated quantum photonics.

30 July 2018 in Nature Materials
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Journal Article
Closed access
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MDMahendra Dc
Mahendra Dc
RGRoberto Grassi
Roberto Grassi
JCJun-Yang Chen
Jun-Yang Chen
MJMahdi Jamali
Mahdi Jamali
DHDanielle Reifsnyder Hickey
Danielle Reifsnyder Hickey
JWJian-Ping Wang
Jian-Ping Wang

1 November 2008 in Nature
Verified
Journal Article
Closed access
PDF file
MLMo Li
Mo Li
WPW. H. P. Pernice
W. H. P. Pernice
CXC. Xiong
C. Xiong
TBT. Baehr-Jones
T. Baehr-Jones
MHM. Hochberg
M. Hochberg
HTH. X. Tang
H. X. Tang