Understanding the role of intramolecular ion-pair interactions in conformational stability using an ab initio thermodynamic cycle
Sabyasachi Chakraborty, Kalyaneswar Mandal, Raghunathan Ramakrishnan
Journal of Physical Chemistry B (2023) accepted.
Supplementary Information: Raw I/O files, Jupyter notebooks
The Resolution-vs.-Accuracy Dilemma in Machine Learning Modeling of Electronic Excitation Spectra
Prakriti Kayastha, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
Digital Discovery, 1 (2022) 689-702.
bigQM7w dataset
Raw input/output files on NOMAD
Data-mining platform on Moldis
Machine learning model
Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design
Amit Gupta, Sabyasachi Chakraborty, Debashree Ghosh, Raghunathan Ramakrishnan
The Journal of Chemical Physics, 155 (2021) 244102.
BODIPYs dataset
Web-based QML model for querying on 253 Billion BODIPY molecules
JCP Special Topic: Chemical Design by Artificial Intelligence
Machine Learning Modeling of Materials with a Group-Subgroup Structure
Prakriti Kayastha, Raghunathan Ramakrishnan
Machine Learning: Science and Technology, 2 (2021) 035035.
FriezeRMQ1D dataset
Raw input/output files on NOMAD
Troubleshooting Unstable Molecules in Chemical Space
Salini Senthil, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
Chemical Science 12 (2021) 5566.
Features in 2021 Chemical Science Editor’s Choice
Supplementary Information: PDF file
ConnGO code
Curated QM9 dataset
Revving up 13C NMR shielding predictions across chemical space: Benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules
Amit Gupta, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
Machine Learning: Science and Technology, 2 (2021) 035010.
Supplementary Information: PDF file
High-Throughput Design of Peierls and Charge Density Wave Phases in Q1D Organometallic Materials
Prakriti Kayastha, Raghunathan Ramakrishnan
The Journal of Chemical Physics, 154 (2021) 061102.
Supplementary Information: PDF file
MolDis data-mining platform
Raw input/output files on NOMAD
JCP Special Topic: Computational Materials Discovery
Critical Benchmarking of the G4(MP2) Model, the Correlation Consistent Composite Approach and Popular Density Functional Approximations on a Probabilistically Pruned Benchmark Dataset of Formation Enthalpies
Sambit Kumar Das, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
The Journal of Chemical Physics, 154 (2021) 044113.
prunedHOF dataset
Quantum Interference in Real-Time Electron-Dynamics: Gaining Insights from Time-Dependent Configuration Interaction Simulations
Raghunathan Ramakrishnan
The Journal of Chemical Physics, 152 (2020) 194111.
Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase
Ardita Kilaj, Hong Gao, Diana Nikolaeva Tahchieva, Raghunathan Ramakrishnan, Daniel G Bachmann, Dennis Gillingham, Anatole von Lilienfeld, Jochen Küpper, Stefan Willitsch
Physical Chemistry Chemical Physics, 22 (2020) 13431-13439.
Machine learning modeling of Wigner intracule functionals for two electrons in one dimension
Rutvij Vihang Bhavsar, Raghunathan Ramakrishnan
The Journal of Chemical Physics,150 (2019) 144114.
The Chemical Space of B,N-substituted Polycyclic Aromatic Hydrocarbons: Combinatorial Enumeration and High-Throughput First-Principles Modeling
Sabyasachi Chakraborty, Prakriti Kayastha, Raghunathan Ramakrishnan
The Journal of Chemical Physics,150 (2019) 114106.
JCP Featured Article
Features in 2019 JCP Editors’ Choice
BNPAH dataset
Exact separation of radial and angular correlation energies in two-electron atoms
Anjana R Kammath, Raghunathan Ramakrishnan
Chemical Physics Letters, 720 (2019) 93–96.
Torsional potentials of glyoxal, oxalyl halides and their thiocarbonyl derivatives: Challenges for DFT
Diana Tahchieva, Dirk Bakowies, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
Journal of Chemical Theory and Computation, 14 (2018) 4806-4817.
Generalized DFTB repulsive potentials from unsupervised machine learning
J. J. Kranz, M. Kubillus, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, M. Elstner
Journal of Chemical Theory and Computation, 14 (2018) 2341-2352.
Genetic optimization of training sets for improved machine learning models of molecular properties
Nicholas J. Browning, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, Ursula Röthlisberger
Journal of Physical Chemistry Letters, 8 (2017) 1351-1359.
Machine learning, quantum mechanics, chemical compound space
Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
Reviews in Computational Chemistry, Vol.30, 225-250 (2017).
Fast and accurate predictions of covalent bonds in chemical space
K. Y. Samuel Chang, Stijn Fias, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
The Journal of Chemical Physics, 144 (2016) 174110.
Electronic spectra from TDDFT and machine learning in chemical space
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, O. Anatole von Lilienfeld
The Journal of Chemical Physics, 143 (2015) 084111.
Machine learning for quantum mechanical properties of atoms in molecules
Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
Journal of Physical Chemistry Letters, 6 (2015) 3309-3313.
Machine learning predictions of molecular properties: Accurate many-body potentials and non-locality in chemical space
Katja Hansen, Franziska Biegler, Raghunathan Ramakrishnan, Wiktor Pronobis, O. Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko
Journal of Physical Chemistry Letters, 6 (2015) 2326–2331.
Big data meets quantum chemistry approximations: The delta-machine learning approach
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld
Journal of Chemical Theory and Computation, 11 (2015) 2087–2096.
Semi-quartic force fields retrieved from multi-mode expansions: Accuracy, scaling behavior and approximations
Raghunathan Ramakrishnan, Guntram Rauhut
The Journal of Chemical Physics, 142 (2015) 154118.
Many molecular properties from one kernel in chemical space
Raghunathan Ramakrishnan, O. Anatole von Lilienfeld
Chimia, 69 (2015) 182-186.
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, Aaron Knoll
International Journal of Quantum Chemistry, 115 (2015) 1084-1093.
Charge transfer dynamics from adsorbates to surfaces with single active electron and configuration interaction based approaches
Raghunathan Ramakrishnan, Mathias Nest
Chemical Physics, 446 (2015) 24-29.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp, O. Anatole von Lilienfeld
Scientific Data 1, Article number: 140022 (2014).
Vibrational energy levels of difluorodioxirane computed with variational and perturbative methods from a hybrid force field
Raghunathan Ramakrishnan, Tucker Carrington, Jr.
Spectrochimica Acta A, 119 (2014) 107–112.
Electron dynamics across molecular wires: A time-dependent configuration interaction study
Raghunathan Ramakrishnan, Shampa Raghunathan, Mathias Nest
Chemical Physics, 420 (2013) 44–49.
A simple Hückel molecular orbital plotter
Raghunathan Ramakrishnan
Journal of Chemical Education, 90 (2013) 132–133.
Control and analysis of single-determinant electron dynamics
Raghunathan Ramakrishnan, Mathias Nest
Physics Review A, 85 (2012) 054501.
Coherent control time-dependent methods for determining eigenvalues of Hermitian matrices with applications to electronic structure computations
Raghunathan Ramakrishnan, Mathias Nest, Eli Pollak
Molecular Physics, 110 (2012) 861–873.
Self-interaction artifacts on structural features of uranyl monohydroxide from Kohn-Sham calculations
Raghunathan Ramakrishnan, Alexei V. Matveev, Sven Krüger, Notker Rösch
Theoretical Chemistry Accounts, 130 (2011) 361–369.
Effects of the self-interaction error in Kohn-Sham calculations: A DFT + U case study on pentaaqua uranyl(VI)
Raghunathan Ramakrishnan, Alexei V. Matveev, Notker Rösch
Computational and Theoretical Chemistry, 963 (2011) 337–343.
The DFT + U method in the linear combination of Gaussian-type orbitals framework: Role of 4f orbitals in the bonding of LuF3
Raghunathan Ramakrishnan, Alexei V. Matveev, Notker Rösch
Chemical Physics Letters, 468 (2009) 158–161.
Manifestation of diamagnetic chemical shifts of proton NMR signals by an anisotropic shielding effect of nitrate anions
Himansu Sekhar Sahoo, Dillip Kumar Chand, S. Mahalakshmi, Md. Hedayetullah Mir, Raghunathan Ramakrishnan
Tetrahedron letters, 48 (2007) 761–765.