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pro vyhledávání: '"Qu, Chen"'
Autor:
Yang, Benhui, Qu, Chen, Bowman, J. M., Yang, Dongzheng, Guo, Hua, Balakrishnan, N., Forrey, R. C., Stancil, P. C.
The rovibrational level populations, and subsequent emission in various astrophysical environments, is driven by inelastic collision processes. The available rovibrational rate coefficients for water have been calculated using a number of approximati
Externí odkaz:
http://arxiv.org/abs/2411.08707
With increasing concerns and regulations on data privacy, fine-tuning pretrained language models (PLMs) in federated learning (FL) has become a common paradigm for NLP tasks. Despite being extensively studied, the existing methods for this problem st
Externí odkaz:
http://arxiv.org/abs/2409.00116
This short paper reports a study of the electronic dissociation energies, De, of water clusters from direct ab initio (mostly CCSD(T)) calculations and the q-AQUA and MB-pol potentials. These clusters range in size from 6-25 monomers. These are all i
Externí odkaz:
http://arxiv.org/abs/2408.05234
Conversational search supports multi-turn user-system interactions to solve complex information needs. Different from the traditional single-turn ad-hoc search, conversational search encounters a more challenging problem of context-dependent query un
Externí odkaz:
http://arxiv.org/abs/2407.20189
Autor:
Nandi, Apurba, Pandey, Priyanka, Houston, Paul L., Qu, Chen, Yu, Qi, Conte, Riccardo, Tkatchenko, Alexandre, Bowman, Joel M.
Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently,"$\Delta$-machine learning" has been used to elevate the qu
Externí odkaz:
http://arxiv.org/abs/2407.20050
Autor:
Ge, Fuchun, Wang, Ran, Qu, Chen, Zheng, Peikun, Nandi, Apurba, Conte, Riccardo, Houston, Paul L., Bowman, Joel M., Dral, Pavlo O.
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from
Externí odkaz:
http://arxiv.org/abs/2403.11216
Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine. However, the effectiveness of the conversational dense retrieval methods is limited by the scarcity of training
Externí odkaz:
http://arxiv.org/abs/2403.11335
Autor:
Pandey, Priyanka, Arandhara, Mrinal, Houston, Paul L., Qu, Chen, Conte, Riccardo, Bowman, Joel M., Ramesh, Sai G.
Here we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reporte
Externí odkaz:
http://arxiv.org/abs/2402.11158
Conversational search facilitates complex information retrieval by enabling multi-turn interactions between users and the system. Supporting such interactions requires a comprehensive understanding of the conversational inputs to formulate a good sea
Externí odkaz:
http://arxiv.org/abs/2401.16659
Autor:
Houston, Paul L., Qu, Chen, Yu, Qi, Pandey, Priyanka, Conte, Riccardo, Nandi, Apurba, Bowman, Joel M.
Publikováno v:
J. Chem. Theory Comput. 2024
Assessments of machine-learned (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutationally invariant polynomial (PIP) method for ethanol, using the widely
Externí odkaz:
http://arxiv.org/abs/2401.09316