Pavlo O. Dral

Solid State Physics Laboratory — India

Materials Science · Physical Sciences

30h-index6.6kcitations241works4.12yr avg
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Research Topics

Machine Learning in Materials Science(85), Spectroscopy and Quantum Chemical Studies(45), Computational Drug Discovery Methods(39), Advanced Chemical Physics Studies(37), Quantum, superfluid, helium dynamics(21)

Publications241 total

Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics
Journal of Chemical Theory and Computation·2026
Photochemical N‐dealkylation of Tertiary Amines Coupled With Photocharging of Poly(Heptazine Imides)
Angewandte Chemie·2026Open Access
SOT-RM: An equivariant shortcut and optimal transportation-based flow model for exploring chemical reaction mechanisms
2026Open Access
Photochemical N‐dealkylation of Tertiary Amines Coupled With Photocharging of Poly(Heptazine Imides)
Angewandte Chemie International Edition·2026Open Access
AIQM-PBSA: Integrating Machine Learning InteratomicPotentials with MMPBSA for Accurate Protein–Ligand Binding Free Energy Calculations
2026Open Access
Quantum-inspired Chemical Rule for Discovering Topological Materials
arXiv (Cornell University)·2025Open Access
Gradients not needed: ML-driven propagation ofnonadiabatic molecular dynamics without reference gradients
2025
Breaking the Scalability Barrier: Diversifiable Pd-Catalyzed Domino Reaction En Route to Dibenzo[ <i>b,d</i> ]azepines
Organic Letters·2025
MELTS: Fully Automated Active Learning for Fewest-Switches Surface Hopping Dynamics
Journal of Chemical Theory and Computation·2025· 3 citedOpen Access
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
The Journal of Physical Chemistry Letters·2025· 3 cited
Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space
2025· 1 citedOpen Access
AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements
2025Open Access
OMNI-P2x: A Universal Neural Network Potential for Excited-State Simulations
2025
One to Rule Them All: A Universal Interatomic Potential Learning across Quantum Chemical Levels
Journal of Chemical Theory and Computation·2025· 1 cited
Ultrafast Correlation Energy Estimator
The Journal of Physical Chemistry A·2025· 1 citedOpen Access
One To Rule Them All: A Universal Interatomic Potential Learning Across Quantum Chemical Levels
2025· 2 citedOpen Access
MELTS: Fully Automated Active Learning for Fewest-Switches Surface Hopping Dynamics
2025Open Access
MELTS: Fully Automated Active Learning for Fewest-Switches Surface Hopping Dynamics
2025Open Access
ECTS: An ultra-fast diffusion model for exploring chemical reactions with equivariant consistency
2025Open Access
Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations
2025Open Access
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Frequent Co-authors

Mario Barbatti(11), Fuchun Ge(8), Walter Thiel(8), Arif Ullah(7), Milan Kivala(7), Max Pinheiro(6), Peikun Zheng(6), Timothy Clark(5), Yi-Fan Hou(5), Lina Zhang(5), Tobias A. Schaub(5), Dirk M. Guldi(5), Yuxinxin Chen(4), Frank Hampel(4), O. Anatole von Lilienfeld(3), Xin Wu(3), Olexandr Isayev(3), Raghunathan Ramakrishnan(2), Matthias Rupp(2), Lasse Spörkel(2)