AI-enhanced computational chemistry
If you feel lost in the ocean of various datasets to train your machine learning potential, a comprehensive overview can serve as the compass to help navigate through the resources available. Recently, we published a review on Machine Learning: Science …
MLST: Review on data sets for molecular ML potentials Read more »
Density functional theory (DFT) methods are by far the most popular approaches for electronic structure calculations. However, the “best” functional remains elusive despite the increasing variety of functionals and continuous efforts to improve their computational accuracy. In our work published in Advanced …
Adv. Sci.: The Best DFT Functional Is the Ensemble of Functionals Read more »
Recently, we published a paper in JOC about the surprising dynamics phenomena in the Diels–Alder reaction of fullerene C60. The AI-accelerated molecular dynamics uncovers that in a small fraction (10%) of reactive trajectories, the diene molecule (2,3-dimethyl-1,3-butadiene) is roaming around …
JOC: Surprising dynamics phenomena in the Diels–Alder reaction of C60 uncovered with AI Read more »
My review ‘AI in computational chemistry through the lens of a decade-long journey’ was published open access as an invited Feature Article in Chemical Communication. It gives a perspective on the progress of AI tools in computational chemistry through the …
Max Pinheiro Jr*†, Shuang Zhang†, Pavlo O. Dral, Mario Barbatti*. WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets. Sci. Data 2023, 10, 95. DOI: 10.1038/s41597-023-01998-3. Blog post ›
Yuming Su, Yiheng Dai, Yifan Zeng, Caiyun Wei, Yangtao Chen, Fuchun Ge, Peikun Zheng, Da Zhou*, Pavlo O. Dral*, Cheng Wang*. Interpretable Machine Learning of Two-Photon Absorption. Adv. Sci. 2023, 2204902. DOI: 10.1002/advs.202204902. Blog post ›
Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo Dral*. Benchmark of General-Purpose Machine Learning-Based Quantum Mechanical Method AIQM1 on Reaction Barrier Heights. J. Chem. Phys. 2023, in press. DOI: 10.1063/5.0137101. Blog post ›
Luis E. Herrera Rodríguez, Arif Ullah, Kennet J. Rueda Espinosa, Pavlo O. Dral*, Alexei A. Kananenka*. A comparative study of different machine learning methods for dissipative quantum dynamics. Mach. Learn. Sci. Technol. 2022, 3, 045016. DOI: 10.1088/2632-2153/ac9a9d. Blog post ›
Mario Barbatti*, Mattia Bondanza, Rachel Crespo-Otero, Baptiste Demoulin, Pavlo O. Dral, Giovanni Granucci, Fábris Kossoski, Hans Lischka, Benedetta Mennucci, Saikat Mukherjee, Marek Pederzoli, Maurizio Persico, Max Pinheiro Jr, Jiri Pittner, Felix Plasser, Eduarda Sangiogo Gil, Lijljana Stojanovic. The Newton-X platform: new software developments for surface hopping and nuclear ensembles. J. Chem. Theory Comput. 2022, ASAP. DOI: 10.1021/acs.jctc.2c00804. …
The Newton-X platform for surface hopping and nuclear ensembles Read more »
Quantum Chemistry in the Age of Machine Learning, Pavlo O. Dral, Ed. Elsevier: 2022. Paperback ISBN: 9780323900492. Companion website to the book with complementary materials, data, codes, etc. Blog post ›