已发表成果:
WOK 论文 40 篇;其它论文 1 篇;图书及章节 2 本;
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
Modern semiempirical electronic structure methods
MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows
AI-enhanced on-the-fly simulation of nonlinear time-resolved spectra
MLQD: A package for machine learning-based quantum dissipative dynamics
Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra
AI in computational chemistry through the lens of a decade-long journey
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
Energy-conserving molecular dynamics is not energy conserving
Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models
Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models
Energy-conserving molecular dynamics is not energy conserving
Themed collection on Insightful Machine Learning for Physical Chemistry
Photo-Driven Aerobic Methane Nitration
MLQD: A package for machine learning-based quantum dissipative dynamics
Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights
QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets
Interpretable Machine Learning of Two-Photon Absorption
Tunable Macrocyclic Polyparaphenylene Nanolassos via Copper-Free Click Chemistry
QD3SET-1: a database with quantum dissipative dynamics datasets
Energy-conserving molecular dynamics is not energy conserving
Ultra-Fast Semi-Empirical Quantum Chemistry for High-Throughput Computational Campaigns with Sparrow
Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles
One-Shot Trajectory Learning of Open Quantum Systems Dynamics
A comparative study of different machine learning methods for dissipative quantum dynamics
Machine Learning for Designing Mixed Metal Halides for Efficient Ammonia Separation and Storage
Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics
VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces
Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods
Artificial intelligence-enhanced quantum chemical method with broad applicability
Choosing the right molecular machine learning potential
Speeding up quantum dissipative dynamics of open systems with kernel methods
MLatom 2: An Integrative Platform for Atomistic Machine Learning
Molecular excited states through a machine learning lens
Machine Learning for Absorption Cross Sections
Hierarchical machine learning of potential energy surfaces
Quantum chemistry assisted by machine learning
Quantum Chemistry in the Age of Machine Learning
A Spherically Shielded Triphenylamine and Its Persistent Radical Cation
The Impact of Aggregation on the Photophysics of Spiro-Bridged Heterotriangulenes
5,7,12,14-Tetraphenyl-Substituted 6,13-Diazapentacenes as Versatile Organic Semiconductors: Characterization in Field Effect Transistors
Organic Materials,,2020.Chapter 3 Pages 67-92, Chapter 8 Pages 183-204, Chapter 9 Pages 205-232,Chapter 13 Pages 295-312,Chapter 15 Pages 329-353,Chapter 20 Pages 467-488,Chapter 21 Pages 491-507,Chapter 24 Pages 559-575//Pavlo O. Dral.Quantum Chemistry in the Age of Machine Learning. (专著). Elsevier, 2022-09-23.
Chapter 11, Pages 291-324//Kenneth Ruud, Erkki J. Brändas.Quantum chemistry assisted by machine learning. (专著). Elsevier, 2020.