R. Ramakrishnan

发表

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

K. Müller, A. Tkatchenko, O. A. von Lilienfeld, 2015, The journal of physical chemistry letters.

Quantum chemistry structures and properties of 134 kilo molecules

Pavlo O. Dral, M. Rupp, O. A. von Lilienfeld, 2014, Scientific Data.

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

M. Rupp, R. Ramakrishnan, O. A. V. Lilienfeld, 2013, 1307.2918.

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

Pavlo O. Dral, M. Rupp, R. Ramakrishnan, 2015, Journal of chemical theory and computation.

Electronic spectra from TDDFT and machine learning in chemical space.

O. A. von Lilienfeld, R. Ramakrishnan, Mia Hartmann, 2015, The Journal of chemical physics.

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

M. Rupp, O. A. von Lilienfeld, R. Ramakrishnan, 2015, 1505.00350.

Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules

R. Ramakrishnan, Amit Gupta, S. Chakraborty, 2020, Mach. Learn. Sci. Technol..

Machine Learning, Quantum Chemistry, and Chemical Space

O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, R. Ramakrishnan, 2017 .

Coherent control time-dependent methods for determining eigenvalues of Hermitian matrices with applications to electronic structure computations

R. Ramakrishnan, E. Pollak, M. Nest, 2012 .

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

Matthias Rupp, Pavlo O. Dral, Pavlo O Dral, 2015, Journal of chemical theory and computation.

Many Molecular Properties from One Kernel in Chemical Space.

Raghunathan Ramakrishnan, O Anatole von Lilienfeld, O. A. von Lilienfeld, 2015, Chimia.

Fast and accurate predictions of covalent bonds in chemical space.

Stijn Fias, Raghunathan Ramakrishnan, O Anatole von Lilienfeld, 2015, The Journal of chemical physics.

Resolution-vs.-Accuracy Dilemma in Machine Learning Modeling of Electronic Excitation Spectra

R. Ramakrishnan, S. Chakraborty, Prakriti Kayastha, 2021, 2110.11798.

The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling.

R. Ramakrishnan, S. Chakraborty, Prakriti Kayastha, 2019, The Journal of chemical physics.

Quantum Machine Learning Transition Probabilities in Electronic Excitation Spectra across Chemical Space: The Resolution-vs.-Accuracy Dilemma

R. Ramakrishnan, Prakriti Kayastha, 2021, 2110.11798.

Machine Learning Methods for the Sampling of Chemical Space From First Principles

Raghu Ramakrishnan, R. Ramakrishnan, 2015 .

Understanding the role of intramolecular ion-pair interactions in conformational stability using an ab initio thermodynamic cycle

R. Ramakrishnan, S. Chakraborty, K. Mandal, 2022, 2203.03478.

Crystal-melt kinetic coefficients of Ni3Al

R. Ramakrishnan, R. Sankarasubramanian, 2017 .

Understanding the Role of Intramolecular Ion-Pair Interactions in Conformational Stability Using an Ab Initio Thermodynamic Cycle.

R. Ramakrishnan, S. Chakraborty, K. Mandal, 2022, The journal of physical chemistry. B.

Machine Learning, Quantum Mechanics, and Chemical Compound Space

O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, R. Ramakrishnan, 2015, 1510.07512.

Torsional Potentials of Glyoxal, Oxalyl Halides, and Their Thiocarbonyl Derivatives: Challenges for Popular Density Functional Approximations.

O. A. von Lilienfeld, R. Ramakrishnan, D. Bakowies, 2018, Journal of chemical theory and computation.

Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules

Sabyasachi Chakraborty, Raghunathan Ramakrishnan, Amit Gupta, 2020, Mach. Learn. Sci. Technol..

Vibrational energy levels of difluorodioxirane computed with variational and perturbative methods from a hybrid force field.

T. Carrington, R. Ramakrishnan, 2014, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

Charge-transfer selectivity and quantum interference in real-time electron dynamics: Gaining insights from time-dependent configuration interaction simulations.

R. Ramakrishnan, 2019, The Journal of chemical physics.

Critical benchmarking of popular composite thermochemistry models and density functional approximations on a probabilistically pruned benchmark dataset of formation enthalpies.

Sabyasachi Chakraborty, Raghunathan Ramakrishnan, Sambit Kumar Das, 2021, The Journal of chemical physics.

Effects of the self-interaction error in Kohn–Sham calculations: A DFT + U case study on penta-aqua uranyl(VI)

N. Rösch, R. Ramakrishnan, A. Matveev, 2011 .

The DFT+U Method in the Framework of the Parallel Density Functional Code ParaGauss

Raghunathan Ramakrishnan, R. Ramakrishnan, 2011 .

Troubleshooting unstable molecules in chemical space†

R. Ramakrishnan, S. Chakraborty, Salini Senthil, 2020, Chemical science.

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties.

Raghunathan Ramakrishnan, Ursula Roethlisberger, Nicholas J Browning, 2016, The journal of physical chemistry letters.

Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning.

Marcus Elstner, Raghunathan Ramakrishnan, O Anatole von Lilienfeld, 2018, Journal of chemical theory and computation.

A Simple Hückel Molecular Orbital Plotter

R. Ramakrishnan, 2013 .

Semi-quartic force fields retrieved from multi-mode expansions: Accuracy, scaling behavior, and approximations.

Guntram Rauhut, Raghunathan Ramakrishnan, R. Ramakrishnan, 2015, The Journal of chemical physics.

Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design.

R. Ramakrishnan, Amit Gupta, S. Chakraborty, 2021, The Journal of chemical physics.

Exact separation of radial and angular correlation energies in two-electron atoms

R. Ramakrishnan, Anjana R Kammath, 2018, Chemical Physics Letters.

Machine learning modeling of Wigner intracule functionals for two electrons in one-dimension.

R. Ramakrishnan, Rutvij Bhavsar, 2018, The Journal of chemical physics.

Machine learning modeling of materials with a group-subgroup structure

R. Ramakrishnan, Prakriti Kayastha, 2020, Mach. Learn. Sci. Technol..

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.

O. A. von Lilienfeld, R. Ramakrishnan, Dennis G. Gillingham, 2020, Physical chemistry chemical physics : PCCP.

High-throughput design of Peierls and charge density wave phases in Q1D organometallic materials.

R. Ramakrishnan, Prakriti Kayastha, 2020, The Journal of chemical physics.

Electron dynamics across molecular wires: A time-dependent configuration interaction study

R. Ramakrishnan, Shampa Raghunathan, M. Nest, 2013 .

The DFT + U method in the linear combination of Gaussian-type orbitals framework: Role of 4f orbitals in the bonding of LuF3

Notker Rösch, Alexei V. Matveev, Raghunathan Ramakrishnan, 2009 .

Variational augmentation of Gaussian continuum basis sets for calculating atomic higher harmonic generation spectra

R. Ramakrishnan, Sai Vijay Bhaskar Mocherla, 2023, 2307.00732.

Signatures of Quantum Interference in Electron-Dynamics Selectivity: Gaining Insights from Time-Dependent Configuration Interaction Simulations

R. Ramakrishnan, 2019 .

Vibrational energy levels of difluorodioxirane computed with variational and perturbative methods from a hybrid force field.

T. Carrington, R. Ramakrishnan, 2014, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.