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Journal of the American Chemical Society
|
November 7, 2002
Electroluminescence in ruthenium(II) complexes
Stefan Bernhard, Jason A Barron, Paul L Houston, et al.
Journal of Chemical Theory and Computation
|
February 21, 2024
Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer
Paul L Houston, Chen Qu, Qi Yu, et al.
The Journal of Physical Chemistry Letters
|
April 16, 2024
Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine
Fuchun Ge, Ran Wang, Chen Qu, et al.
Nature Computational Science
|
April 14, 2025
Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials
Qi Yu, Ruitao Ma, Chen Qu, et al.
Applied and Environmental Microbiology
|
July 8, 2005
Determination of spatial distributions of zinc and active biomass in microbial biofilms by two-photon laser scanning microscopy
Zhiqiang Hu, Gabriela Hidalgo, Paul L Houston, et al.
Environmental Science & Technology
|
March 3, 2007
Spatial distributions of copper in microbial biofilms by scanning electrochemical microscopy
Zhiqiang Hu, Jing Jin, Héctor D Abruña, et al.
Physical Chemistry Chemical Physics : PCCP
|
January 5, 2022
Electronic relaxation and dissociation dynamics in formaldehyde: pump wavelength dependence
Tomoyuki Endo, Simon P Neville, Philippe Lassonde, et al.
Journal of the American Chemical Society
|
April 20, 2023
Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands
Apurba Nandi, Gabriel Laude, Subodh S Khire, et al.
Science (New York, N.Y.)
|
November 27, 2020
Capturing roaming molecular fragments in real time
Tomoyuki Endo, Simon P Neville, Vincent Wanie, et al.
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of 7
Search research articles
Search
Showing results (61-70 of 69) with videos related to
Sort By:
Page
of 7
You have reached the last page of results.
This site can display upto 69 results.
Journal of the American Chemical Society
|
November 7, 2002
Electroluminescence in ruthenium(II) complexes
Stefan Bernhard, Jason A Barron, Paul L Houston, et al.
Journal of Chemical Theory and Computation
|
February 21, 2024
Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer
Paul L Houston, Chen Qu, Qi Yu, et al.
The Journal of Physical Chemistry Letters
|
April 16, 2024
Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine
Fuchun Ge, Ran Wang, Chen Qu, et al.
Nature Computational Science
|
April 14, 2025
Extending atomic decomposition and many-body representation with a chemistry-motivated approach to machine learning potentials
Qi Yu, Ruitao Ma, Chen Qu, et al.
Applied and Environmental Microbiology
|
July 8, 2005
Determination of spatial distributions of zinc and active biomass in microbial biofilms by two-photon laser scanning microscopy
Zhiqiang Hu, Gabriela Hidalgo, Paul L Houston, et al.
Environmental Science & Technology
|
March 3, 2007
Spatial distributions of copper in microbial biofilms by scanning electrochemical microscopy
Zhiqiang Hu, Jing Jin, Héctor D Abruña, et al.
Physical Chemistry Chemical Physics : PCCP
|
January 5, 2022
Electronic relaxation and dissociation dynamics in formaldehyde: pump wavelength dependence
Tomoyuki Endo, Simon P Neville, Philippe Lassonde, et al.
Journal of the American Chemical Society
|
April 20, 2023
Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands
Apurba Nandi, Gabriel Laude, Subodh S Khire, et al.
Science (New York, N.Y.)
|
November 27, 2020
Capturing roaming molecular fragments in real time
Tomoyuki Endo, Simon P Neville, Vincent Wanie, et al.
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of 7