Pavlo O. Dral

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Data for "Machine Learning for Absorption Cross Sections"

Pavlo O. Dral, 2020 .

Quantum chemistry assisted by machine learning

Pavlo O. Dral, 2020 .

Big data analysis of ab Initio molecular integrals in the neglect of diatomic differential overlap approximation

Xin Wu, Walter Thiel, Pavlo O. Dral, 2018, J. Comput. Chem..

Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge

Bernard R. Brooks, Juyong Lee, Walter Thiel, 2016, Journal of Computer-Aided Molecular Design.

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

Walter Thiel, Pavlo O. Dral, O. Anatole von Lilienfeld, 2015, Journal of chemical theory and computation.

Choosing the right molecular machine learning potential

Mario Barbatti, Nicolas Ferré, Pavlo O. Dral, 2021, Chemical Science.

Speeding up quantum dissipative dynamics of open systems with kernel methods

Pavlo O. Dral, Arif Ullah, 2021 .

Quantum chemistry structures and properties of 134 kilo molecules

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

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.

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

Pavlo O. Dral, W. Thiel, Xin Wu, 2019, Journal of chemical theory and computation.

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties

Pavlo O. Dral, W. Thiel, Xin Wu, 2016, Journal of chemical theory and computation.

Machine Learning for Absorption Cross Sections

Pavlo O. Dral, M. Barbatti, Bao-Xin Xue, 2020, The journal of physical chemistry. A.

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.

Nonadiabatic Excited-State Dynamics with Machine Learning

Walter Thiel, Mario Barbatti, Pavlo O. Dral, 2018, The journal of physical chemistry letters.

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters

Walter Thiel, Pavlo O. Dral, Axel Koslowski, 2016, Journal of chemical theory and computation.

A Spherically Shielded Triphenylamine and Its Persistent Radical Cation

Pavlo O. Dral, Tobias A. Schaub, Milan Kivala, 2020, Chemistry.

Semiempirical UNO-CAS and UNO-CI: method and applications in nanoelectronics.

Pavlo O. Dral, T. Clark, 2011, The journal of physical chemistry. A.

Machine Learning for Absorption Cross Sections

Pavlo O. Dral, M. Barbatti, Bao-Xin Xue, 2020, The journal of physical chemistry. A.

A comparative study of different machine learning methods for dissipative quantum dynamics

Pavlo O. Dral, A. Ullah, Alexei A. Kananenka, 2022, 2207.02417.

Stability of Odd- Versus Even-Electron Gas-Phase (Quasi)Molecular Ions Derived from Pyridine-Substituted N-Heterotriangulenes.

Pavlo O. Dral, W. Thiel, T. Clark, 2017, ChemPlusChem.

Machine Learning for Designing Mixed Metal Halides for Efficient Ammonia Separation and Storage

Pavlo O. Dral, H. Lischka, A. Aquino, 2022, The Journal of Physical Chemistry C.

Speeding up quantum dissipative dynamics of open systems with kernel methods

Pavlo O. Dral, A. Ullah, 2021 .

One-Shot Trajectory Learning of Open Quantum Systems Dynamics.

Pavlo O. Dral, A. Ullah, 2022, The journal of physical chemistry letters.

Organic Electron Acceptors Comprising a Dicyanomethylene-Bridged Acridophosphine Scaffold: The Impact of the Heteroatom.

Pavlo O. Dral, Tobias A. Schaub, Milan Kivala, 2017, Chemistry.

Configurationally Stable Chiral Dithia-Bridged Hetero[4]helicene Radical Cation: Electronic Structure and Absolute Configuration.

Ana G. Petrovic, Pavlo O. Dral, N. Berova, 2017, Chemistry, an Asian journal.

Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles

Pavlo O. Dral, Ljiljana Stojanović, M. Barbatti, 2022, Journal of chemical theory and computation.

5,7,12,14-Tetraphenyl-Substituted 6,13-Diazapentacenes as Versatile Organic Semiconductors: Characterization in Field Effect Transistors

Pavlo O. Dral, U. Bunz, Milan Kivala, 2020, Organic Materials.

Ultra-Fast Semi-Empirical Quantum Chemistry for High-Throughput Computational Campaigns with Sparrow

Alain C. Vaucher, Pavlo O. Dral, M. Reiher, 2022, 2211.14392.

Multiply bonded metal(II) acetate (rhodium, ruthenium, and molybdenum) complexes with the trans-1,2-bis(N-methylimidazol-2-yl)ethylene ligand.

Pavlo O. Dral, T. Clark, N. Burzlaff, 2014, Inorganic chemistry.

The Impact of Aggregation on the Photophysics of Spiro-bridged Heterotriangulenes.

Pavlo O. Dral, M. Barbatti, R. Schröder, 2020, Angewandte Chemie.

Dithiafulvenyl-Extended N-Heterotriangulenes and Their Interaction with C60 : Cooperative Fluorescence.

Pavlo O. Dral, W. Thiel, D. Guldi, 2017, Chemistry.

1D Chains of Diruthenium Tetracarbonyl Sawhorse Complexes

Pavlo O. Dral, T. Clark, S. Maier, 2018 .

Oxygen-doped nanodiamonds: synthesis and functionalizations.

A. A. Fokin, Pavlo O. Dral, P. Schreiner, 2009, Organic letters.

Choosing the right molecular machine learning potential

Mario Barbatti, Nicolas Ferré, Pavlo O. Dral, 2021, Chemical science.

Artificial intelligence-enhanced quantum chemical method with broad applicability

Pavlo O. Dral, O. Isayev, Wéi Wú, 2021, Nature Communications.

On the feasibility of reactions through the fullerene wall: a theoretical study of NHx@C60.

Pavlo O. Dral, T. Clark, 2017, Physical chemistry chemical physics : PCCP.

Molecular excited states through a machine learning lens

Pavlo O. Dral, M. Barbatti, 2021, Nature Reviews Chemistry.

Quantum Chemistry in the Age of Machine Learning.

Pavlo O. Dral, Pavlo O Dral, 2020, The journal of physical chemistry letters.

Probing charge transfer in benzodifuran-C60 dumbbell-type electron donor-acceptor conjugates: ground- and excited-state assays.

Pavlo O. Dral, C. Yi, T. Clark, 2013, Chemphyschem : a European journal of chemical physics and physical chemistry.

Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods.

Pavlo O. Dral, O. Isayev, Wei Wu, 2022, The journal of physical chemistry letters.

Hierarchical machine learning of potential energy surfaces.

Gábor Csányi, Alexey Dral, Pavlo O. Dral, 2020, The Journal of chemical physics.

What is semiempirical molecular orbital theory approximating?

Johannes T. Margraf, Pavlo O. Dral, J. Margraf, 2019, Journal of Molecular Modeling.

VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces

Pavlo O. Dral, S. Yurchenko, A. Owens, 2022, Scientific data.

MLatom 2: An Integrative Platform for Atomistic Machine Learning

Pavlo O. Dral, M. Barbatti, Jianxing Huang, 2021, Topics in Current Chemistry.

Deep Learning for Nonadiabatic Excited-State Dynamics.

Wei-Hai Fang, Ganglong Cui, Pavlo O. Dral, 2018, The journal of physical chemistry letters.

A comparative study of different machine learning methods for dissipative quantum dynamics

Pavlo O. Dral, A. Ullah, Alexei A. Kananenka, 2022, Mach. Learn. Sci. Technol..

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels.

Walter Thiel, Pavlo O. Dral, Pavlo O Dral, 2017, The Journal of chemical physics.

The relationship between threshold voltage and dipolar character of self-assembled monolayers in organic thin-film transistors.

Timo Meyer-Friedrichsen, Andreas Hirsch, Michael Salinas, 2012, Journal of the American Chemical Society.

Issue Information

Pavlo O. Dral, D. Sharapa, 2019, Journal of Computational Chemistry.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow.

Alain C. Vaucher, Pavlo O. Dral, M. Reiher, 2022, The Journal of chemical physics.

Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

Pavlo O. Dral, A. Ullah, 2021, Nature Communications.

QD3SET-1: a database with quantum dissipative dynamics datasets

Pavlo O. Dral, A. Ullah, Alexei A. Kananenka, 2023, Frontiers in Physics.

MLQD: A package for machine learning-based quantum dissipative dynamics

Pavlo O. Dral, A. Ullah, 2023, 2303.01264.

MLatom: A program package for quantum chemical research assisted by machine learning

Pavlo O. Dral, Pavlo O Dral, 2019, J. Comput. Chem..

Doped polycyclic aromatic hydrocarbons as building blocks for nanoelectronics: a theoretical study.

Pavlo O. Dral, T. Clark, Milan Kivala, 2013, The Journal of organic chemistry.

A π-stacked porphyrin-fullerene electron donor-acceptor conjugate that features a surprising frozen geometry.

Pavlo O. Dral, T. Clark, D. Guldi, 2012, Chemistry.

N-Heterotriangulene chromophores with 4-pyridyl anchors for dye-sensitized solar cells

Pavlo O. Dral, D. Guldi, R. Costa, 2016 .

Influence of electron doping on the hydrogenation of fullerene C60 : a theoretical investigation.

Pavlo O. Dral, T. Clark, A. Hirsch, 2011, Chemphyschem : a European journal of chemical physics and physical chemistry.

Interpretable Machine Learning of Two‐Photon Absorption

Pavlo O. Dral, Yifan Zeng, Yuming Su, 2023, Advanced science.

Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces.

Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, 2023, Journal of chemical theory and computation.

WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets

Pavlo O. Dral, M. Barbatti, Max Pinheiro Jr, 2023, Scientific Data.

Energy-conserving molecular dynamics is not energy conserving.

Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, 2023, Physical chemistry chemical physics : PCCP.

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models.

Pavlo O. Dral, A. Ullah, Fuchun Ge, 2023, Journal of Physical Chemistry Letters.

Organic Electron Acceptors Comprising a Dicyanomethylene-Bridged Acridophosphine Scaffold: The Impact of the Heteroatom.

Pavlo O. Dral, Tobias A. Schaub, Milan Kivala, 2017, Chemistry.

MLQD: A package for machine learning-based quantum dissipative dynamics

Pavlo O. Dral, A. Ullah, 2023, Comput. Phys. Commun..

The relationship between threshold voltage and dipolar character of self-assembled monolayers in organic thin-film transistors.

Pavlo O. Dral, M. Halik, T. Clark, 2012, Journal of the American Chemical Society.

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties

Pavlo O. Dral, W. Thiel, Xin Wu, 2016, Journal of chemical theory and computation.

Tunable Macrocyclic Polyparaphenylene Nanolassos via Copper-Free Click Chemistry.

Pavlo O. Dral, L. Zakharov, D. Guldi, 2023, Chemistry.

Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine.

Pavlo O. Dral, Apurba Nandi, R. Conte, 2024, The journal of physical chemistry letters.