Figure 1.EGFR activation promotes TLR4 phosphorylation and cell surface expression of TLR4 in response to LPS.(A and B)BMDM were treated with LPS(1μg/mL)for 6,12,or 24 h in the presence or absence of pretreatment of ...Figure 1.EGFR activation promotes TLR4 phosphorylation and cell surface expression of TLR4 in response to LPS.(A and B)BMDM were treated with LPS(1μg/mL)for 6,12,or 24 h in the presence or absence of pretreatment of PD or TAPI-1.(A)Flow cytometry analysis of cell surface TLR4 intensity in BMDM.(B)Flow cytometry analysis of cell surface TLR4 intensity in BMDM.(C and D)WT(C57BL76)mice were treated with LPS(10 mg/kg,i.p.).In some groups,mice were pretreated with erlotinib(100 mg/kg,gavage administration)at 30 min prior to LPS i.p.展开更多
Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in...Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.展开更多
文摘Figure 1.EGFR activation promotes TLR4 phosphorylation and cell surface expression of TLR4 in response to LPS.(A and B)BMDM were treated with LPS(1μg/mL)for 6,12,or 24 h in the presence or absence of pretreatment of PD or TAPI-1.(A)Flow cytometry analysis of cell surface TLR4 intensity in BMDM.(B)Flow cytometry analysis of cell surface TLR4 intensity in BMDM.(C and D)WT(C57BL76)mice were treated with LPS(10 mg/kg,i.p.).In some groups,mice were pretreated with erlotinib(100 mg/kg,gavage administration)at 30 min prior to LPS i.p.
文摘Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.
