Publications
PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method
Journal of Chemical Theory and Computation 21 (2): 678–690 (2025)
Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction. The method demonstrates superior performance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML’s accuracy and robustness. Its practical application is facilitated by a direct interface to MOPAC. The code and parameters are available at github.com.
SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes
Nature Communications 15: 1127 (2024)
Heats of formation on the way from B2H6 to B20H16: thermochemical consequences of multicenter bonding in ab initio and DFT methods
Dalton Transactions 54 (1): 239–246 (2025)
Cationic nido-Tricarbollide μ-10,11-H-10,11-H2-7-Me3N-nido-7,8,9-C3B8H8+ and Its Halogenated Derivatives: The Slow Iodination Process Occurring through Cationic Iodonium Tricarbollides
Inorganic Chemistry 64 (2): 796–800 (2025)
The synthesis and structural aspects of the perbromo-functionalised thiaboranes closo-SBnBrn (n = 5, 9, 11): the solid-state structure of the octahedral closo-SB5Br5, governed by strong dihalogen contacts
Chemical Communications 2025: Early View
All publications of Jan Řezáč
A complete list of publications can be found at: