Quantum mechanical simulations that include the effects of the liquid environment are highly relevant for the characterization of solid-liquid interfaces,which is crucial for the design of a wide range of devices.In t...Quantum mechanical simulations that include the effects of the liquid environment are highly relevant for the characterization of solid-liquid interfaces,which is crucial for the design of a wide range of devices.In this work we present a rigorous and systematic study of the band alignment of semiconductors in aqueous solutions by contrasting a range of hybrid explicit/implicit models against explicit atomistic simulations based on density-functional theory.We find that consistent results are obtained provided that the first solvation shell is treated explicitly.Interestingly,the first molecular layer of explicit water is only relevant for the pristine surfaces without dissociatively adsorbed water,hinting at the importance of saturating the surface with quantum mechanical bonds.By referencing the averaged electrostatic potentials of explicit and implicit water against vacuum,we provide absolute alignments,finding maximal differences of only~0.1–0.2 V.Furthermore,the implicit reference potential is shown to exhibit an intrinsic offset of−0.33 V with respect to vacuum,which is traced back to the absence of an explicit water surface in the implicit model.These results pave the way for accurate simulations of solid-liquid interfaces using minimalistic explicit/implicit models.展开更多
To the Editor:The current coronavirus disease-19(COVID-19)pandemic spurs the development of antiviral drugs for SARS-CoV-2,as the number of patients with viral infections continues to rise globally in the context of w...To the Editor:The current coronavirus disease-19(COVID-19)pandemic spurs the development of antiviral drugs for SARS-CoV-2,as the number of patients with viral infections continues to rise globally in the context of widespread vaccination.Targeting the interaction between the receptor binding domain(RBD)of SARS-CoV-2 spike protein and the host cell ACE2 is a promising therapeutic strategy to effectively inhibit viral entry.展开更多
The Bayesian Multi-Fidelity Surrogate(MFS)proposed by Kennedy and O’Hagan(KOH model)has been widely employed in engineering design,which builds the approximation by decomposing the high-fidelity function into a scale...The Bayesian Multi-Fidelity Surrogate(MFS)proposed by Kennedy and O’Hagan(KOH model)has been widely employed in engineering design,which builds the approximation by decomposing the high-fidelity function into a scaled low-fidelity model plus a discrepancy function.The scale factor before the low-fidelity function,ρ,plays a crucial role in the KOH model.This scale factor is always tuned by the Maximum Likelihood Estimation(MLE).However,recent studies reported that the MLE may sometimes result in MFS of bad accuracy.In this paper,we first present a detailed analysis of why MLE sometimes can lead to MFS of bad accuracy.This is because,the MLE overly emphasizes the variation of discrepancy function but ignores the function waviness when selectingρ.To address the above issue,we propose an alternative approach that choosesρby minimizing the posterior variance of the discrepancy function.Through tests on a one-dimensional function,two high-dimensional functions,and a turbine blade design problem,the proposed approach shows better accuracy than or comparable accuracy to MLE,and the proposed approach is more robust than MLE.Additionally,through a comparative test on the design optimization of a turbine endwall cooling layout,the advantage of the proposed approach is further validated.展开更多
基金The authors acknowledge partial financial support from the Swiss National Science Foundation(SNSF)through the NCCR MARVEL and the EU through the MAX CoE for e-infrastructureThis work was supported by a grant from the Swiss National Supercomputing Centre(CSCS)under project IDs s836 and s879 and the computing facilities of SCITAS,EPFL.
文摘Quantum mechanical simulations that include the effects of the liquid environment are highly relevant for the characterization of solid-liquid interfaces,which is crucial for the design of a wide range of devices.In this work we present a rigorous and systematic study of the band alignment of semiconductors in aqueous solutions by contrasting a range of hybrid explicit/implicit models against explicit atomistic simulations based on density-functional theory.We find that consistent results are obtained provided that the first solvation shell is treated explicitly.Interestingly,the first molecular layer of explicit water is only relevant for the pristine surfaces without dissociatively adsorbed water,hinting at the importance of saturating the surface with quantum mechanical bonds.By referencing the averaged electrostatic potentials of explicit and implicit water against vacuum,we provide absolute alignments,finding maximal differences of only~0.1–0.2 V.Furthermore,the implicit reference potential is shown to exhibit an intrinsic offset of−0.33 V with respect to vacuum,which is traced back to the absence of an explicit water surface in the implicit model.These results pave the way for accurate simulations of solid-liquid interfaces using minimalistic explicit/implicit models.
基金supported by the National Natural Science Foundation of China (22078314, 21878286, 21908216)Dalian Institute of Chemical Physics (DICPI202142, DICPI202006, DICPI201938, DICPZZBS201805, China)
文摘To the Editor:The current coronavirus disease-19(COVID-19)pandemic spurs the development of antiviral drugs for SARS-CoV-2,as the number of patients with viral infections continues to rise globally in the context of widespread vaccination.Targeting the interaction between the receptor binding domain(RBD)of SARS-CoV-2 spike protein and the host cell ACE2 is a promising therapeutic strategy to effectively inhibit viral entry.
基金the financial support from the National Science and Technology Major Project,China(No.2019-Ⅱ-0008-0028)Key Program of National Natural Science Foundation of China(No.51936008)。
文摘The Bayesian Multi-Fidelity Surrogate(MFS)proposed by Kennedy and O’Hagan(KOH model)has been widely employed in engineering design,which builds the approximation by decomposing the high-fidelity function into a scaled low-fidelity model plus a discrepancy function.The scale factor before the low-fidelity function,ρ,plays a crucial role in the KOH model.This scale factor is always tuned by the Maximum Likelihood Estimation(MLE).However,recent studies reported that the MLE may sometimes result in MFS of bad accuracy.In this paper,we first present a detailed analysis of why MLE sometimes can lead to MFS of bad accuracy.This is because,the MLE overly emphasizes the variation of discrepancy function but ignores the function waviness when selectingρ.To address the above issue,we propose an alternative approach that choosesρby minimizing the posterior variance of the discrepancy function.Through tests on a one-dimensional function,two high-dimensional functions,and a turbine blade design problem,the proposed approach shows better accuracy than or comparable accuracy to MLE,and the proposed approach is more robust than MLE.Additionally,through a comparative test on the design optimization of a turbine endwall cooling layout,the advantage of the proposed approach is further validated.
