AI-enhanced computational chemistry
Armando de Rezende, Mahdi Malmali, Pavlo O. Dral, Hans Lischka, Daniel Tunega, Adelia A. J. Aquino, Machine Learning for Designing Mixed Metal Halides for Efficient Ammonia Separation and Storage. J. Phys. Chem. C, 2022, 126, 12184–12196. DOI: 10.1021/acs.jpcc.2c02586. Blog post ›
Peikun Zheng, Wudi Yang, Wei Wu, Olexandr Isayev, Pavlo O. Dral, Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods, J. Phys. Chem. Lett. 2022, 13, 3479–3491. DOI：10.1021/acs.jpclett.2c00734. Blog post › | Tutorial ›
Arif Ullah, Pavlo O. Dral, Speeding up Quantum Dissipative Dynamics of Open Systems with Kernel Methods. New J. Phys. 2021, 23, 113019. DOI: 10.1088/1367-2630/ac3261. Blog post ›
Max Pinheiro Jr, Fuchun Ge, Nicolas Ferré, Pavlo O. Dral, Mario Barbatti. Choosing the right molecular machine learning potential. Chem. Sci., 2021, 12, 14396–14413. DOI: 10.1039/D1SC03564A. Blog post › | Tutorial ›
Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5. Read more ›
Bao-Xin Xue, Mario Barbatti, Pavlo O. Dral, Machine Learning for Absorption Cross Sections, J. Phys. Chem. A 2020, 124, 7199–7210. DOI: 10.1021/acs.jpca.0c05310. Read more ›
P. O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004. Read more ›
P. O. Dral, M. Barbatti, W. Thiel, Nonadiabatic Excited-State Dynamics with Machine Learning. J. Phys. Chem. Lett. 2018, 9, 5660–5663. Read more ›
P. O. Dral, A. Owens, S. N. Yurchenko, W. Thiel, Structure-Based Sampling and Self-Correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels. J. Chem. Phys. 2017, 146, 244108. Read more ›
