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Journal of Physics. Condensed Matter : an Institute of Physics Journal
|
December 30, 2025
Machine-learned potentials for solvation modeling
Roopshree Banchode, Surajit Das, Shampa Raghunathan, et al.
Physical Chemistry Chemical Physics : PCCP
|
July 18, 2025
Correction: Influence of pseudo-Jahn-Teller activity on the singlet-triplet gap of azaphenalenes
Atreyee Majumdar, Komal Jindal, Surajit Das, et al.
Physical Chemistry Chemical Physics : PCCP
|
October 15, 2024
Influence of pseudo-Jahn-Teller activity on the singlet-triplet gap of azaphenalenes
Atreyee Majumdar, Komal Jindal, Surajit Das, et al.
The Journal of Chemical Physics
|
September 3, 2015
Electronic spectra from TDDFT and machine learning in chemical space
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, et al.
Scientific Data
|
May 16, 2015
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
Journal of Chemical Theory and Computation
|
November 18, 2015
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
The Journal of Physical Chemistry Letters
|
March 4, 2017
Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties
Nicholas J Browning, Raghunathan Ramakrishnan, O Anatole von Lilienfeld, et al.
Journal of Chemical Theory and Computation
|
July 17, 2018
Torsional Potentials of Glyoxal, Oxalyl Halides, and Their Thiocarbonyl Derivatives: Challenges for Popular Density Functional Approximations
Diana N Tahchieva, Dirk Bakowies, Raghunathan Ramakrishnan, et al.
The Journal of Chemical Physics
|
May 9, 2016
Fast and accurate predictions of covalent bonds in chemical space
K Y Samuel Chang, Stijn Fias, Raghunathan Ramakrishnan, et al.
Journal of Chemical Theory and Computation
|
March 27, 2018
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Julian J Kranz, Maximilian Kubillus, Raghunathan Ramakrishnan, et al.
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of 4
Search research articles
Search
Showing results (21-30 of 34) with videos related to
Sort By:
Page
of 4
Journal of Physics. Condensed Matter : an Institute of Physics Journal
|
December 30, 2025
Machine-learned potentials for solvation modeling
Roopshree Banchode, Surajit Das, Shampa Raghunathan, et al.
Physical Chemistry Chemical Physics : PCCP
|
July 18, 2025
Correction: Influence of pseudo-Jahn-Teller activity on the singlet-triplet gap of azaphenalenes
Atreyee Majumdar, Komal Jindal, Surajit Das, et al.
Physical Chemistry Chemical Physics : PCCP
|
October 15, 2024
Influence of pseudo-Jahn-Teller activity on the singlet-triplet gap of azaphenalenes
Atreyee Majumdar, Komal Jindal, Surajit Das, et al.
The Journal of Chemical Physics
|
September 3, 2015
Electronic spectra from TDDFT and machine learning in chemical space
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, et al.
Scientific Data
|
May 16, 2015
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
Journal of Chemical Theory and Computation
|
November 18, 2015
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
The Journal of Physical Chemistry Letters
|
March 4, 2017
Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties
Nicholas J Browning, Raghunathan Ramakrishnan, O Anatole von Lilienfeld, et al.
Journal of Chemical Theory and Computation
|
July 17, 2018
Torsional Potentials of Glyoxal, Oxalyl Halides, and Their Thiocarbonyl Derivatives: Challenges for Popular Density Functional Approximations
Diana N Tahchieva, Dirk Bakowies, Raghunathan Ramakrishnan, et al.
The Journal of Chemical Physics
|
May 9, 2016
Fast and accurate predictions of covalent bonds in chemical space
K Y Samuel Chang, Stijn Fias, Raghunathan Ramakrishnan, et al.
Journal of Chemical Theory and Computation
|
March 27, 2018
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Julian J Kranz, Maximilian Kubillus, Raghunathan Ramakrishnan, et al.
Page
of 4