Quantum Chemistry Assisted by Machine Learning
Tipo:
Plenária
Categoria:
Plenária
Local:
Sala virtual 11/11 manhã
Data e hora:
13:40 até 14:30 em 11/11/2021
Machine learning (ML) is establishing itself as a useful tool for assisting quantum chemical (QC) research in a variety of ways.[1-2] In our research we exploit ML to improve the accuracy of low-level quantum chemical (QC) method via, e.g., Δ-learning [3] and improving the semiempirical QC Hamiltonian.[4] We also use and develop ML potentials and approaches for computationally-efficient construction of the training sets, e.g., for accelerating simulations of rovibrational spectra with spectroscopic accuracy.[5-6] Currently, our efforts also targeting ML of electronic excited-state properties[7] for performing absorption spectrum (see Figure)[8] and nonadiabatic dynamics simulations.[9-10] To facilitate the use of ML for a variety of computational chemistry tasks we develop a user-friendly integrative platform MLatom program package.[11-13]
References
[1] P. O. Dral, J. Phys. Chem. Lett. 2020, 11, 2336–2347.
[2] P. O. Dral, Quantum Chemistry Assisted by Machine Learning. In Advances in Quantum Chemistry: Chemical Physics and Quantum Chemistry, 1st ed.; Ruud, K.; Brändas, E. J., Eds. Academic Press: 2020; Vol. 81, pp. 291–324.
[3] R. Ramakrishnan, P. O. Dral, M. Rupp, O. A. von Lilienfeld, J. Chem. Theory Comput. 2015, 11, 2087–2096.
[4] P. O. Dral, O. A. von Lilienfeld, W. Thiel, J. Chem. Theory Comput. 2015, 11, 2120–2125.
[5] P. O. Dral, A. Owens, A. Dral, G. Csányi, J. Chem. Phys. 2020, 152, 204110.
[6] P. O. Dral, A. Owens, S. N. Yurchenko, W. Thiel, J. Chem. Phys. 2017, 146, 244108.
[7] P. O. Dral, M. Barbatti, Nat. Rev. Chem. 2021, 5, 388–405.
[8] B.-X. Xue, M. Barbatti, P. O. Dral, J. Phys. Chem. A 2020, 124, 7199–7210.
[9] W.-K. Chen, X.-Y. Liu, W. Fang, P. O. Dral, G. Cui, J. Phys. Chem. Lett. 2018, 9, 6702–6708.
[10] P. O. Dral, M. Barbatti, W. Thiel, J. Phys. Chem. Lett. 2018, 9, 5660–5663.
[11] P. O. Dral, B.-X. Xue, F. Ge, Y.-F. Hou, M. Pinheiro Jr, MLatom: A Package for Atomistic Simulations with Machine Learning, Xiamen University, Xiamen, China, http://MLatom.com, 2013–2021.
[12] P. O. Dral, J. Comput. Chem. 2019, 40, 2339–2347.
[13] P. O. Dral, F. Ge, B.-X. Xue, Y.-F. Hou, M. Pinheiro Jr, J. Huang, M. Barbatti, Top. Curr. Chem. 2021, 379, 27.