Exploring fracture of H-BN and graphene by neural network force fields.

Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture often lead to catastrophic failure of materials and structures. Understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Atomistic simulations offer 'first-principles' tools to resolve the progressive microstructural changes at crack fronts and are widely used to explore the underlying processes of mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g. kinking, branching). Empirical force fields developed based on atomic-level structural descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, especially for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of bond-breaking and (re)formation events during fracture, which, unfortunately, have not been taken into account in either the state-of-the-art empirical or machine-learning force fields. Based on data generated by density functional theory calculations, we report a neural network-based force field for fracture (NN-F3) constructed by using the end-to-end symmetry preserving framework of deep potential-smooth edition (DeepPot-SE). The workflow combines pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F3is demonstrated by studying the rupture of hexagonal boron nitride (h-BN) and twisted bilayer graphene as model problems. The simulation results elucidate the roughening physics of fracture defined by the lattice asymmetry in h-BN, explaining recent experimental findings, and predict the interaction between cross-layer cracks in twisted graphene bilayers, which leads to a toughening effect.

Corrigendum: Integrated chemical interpretation and network pharmacology analysis to reveal the anti-liver fibrosis effect of Penthorum chinense.

[This corrects the article DOI: 10.3389/fphar.2022.788388.].

Integrated Chemical Interpretation and Network Pharmacology Analysis to Reveal the Anti-Liver Fibrosis Effect of Penthorum chinense.

Liver fibrosis is a disease with complex pathological mechanisms. Penthorum chinense Pursh (P. chinense) is a traditional Chinese medicine (TCM) for liver injury treatment. However, the pharmacological mechanisms of P. chinense on liver fibrosis have not been investigated and clarified clearly. This study was designed to investigate the chemicals in P. chinense and explore its effect on liver fibrosis. First, we developed a highly efficient method, called DDA-assisted DIA, which can both broaden mass spectrometry (MS) coverage and MS2 quality. In DDA-assisted DIA, data-dependent acquisition (DDA) and data-independent acquisition (DIA) were merged to construct a molecular network, in which 1,094 mass features were retained in Penthorum chinense Pursh (P. chinense). Out of these, 169 compounds were identified based on both MS1 and MS2 analysis. After that, based on a network pharmacology study, 94 bioactive compounds and 440 targets of P. chinense associated with liver fibrosis were obtained, forming a tight compound-target network. Meanwhile, the network pharmacology experimental results showed that multiple pathways interacted with the HIF-1 pathway, which was first identified involved in P. chinense. It could be observed that some proteins, such as TNF-α, Timp1, and HO-1, were involved in the HIF-1 pathway. Furthermore, the pharmacological effects of P. chinense on these proteins were verified by CCl4-induced rat liver fibrosis, and P. chinense was found to improve liver functions through regulating TNF-α, Timp1, and HO-1 expressions. In summary, DDA-assisted DIA could provide more detailed compound information, which will help us to annotate the ingredients of TCM, and combination with computerized network pharmacology provided a theoretical basis for revealing the mechanism of P. chinense.

Fast switching water processable electrochromic polymers.

This paper describes the synthesis of two new blue to transmissive donor-acceptor electrochromic polymers: a polymer synthesized using an alternating copolymerization route (ECP-Blue-A) and a polymer synthesized using a random copolymerization (ECP-Blue-R) by Stille polymerization. These polymers utilize side chains with four ester groups per donor moiety, allowing organic solubility in the ester form, and water solubility upon saponification to their carboxylate salt form. We demonstrate that the saponified polymer salts of ECP-Blue-A and ECP-Blue-R (WS-ECP-Blue-A and WS-ECP-Blue-R) can be processed from aqueous solutions into thin films by spray-casting. Upon the subsequent neutralization of the thin films, the resulting polymer acid films are solvent resistant and can be electrochemically switched between their colored state and a transmissive state in a KNO(3)/water electrolyte solution. The polymer acids, WS-ECP-Blue-A-acid and WS-ECP-Blue-R-acid, show electrochromic contrast Δ%T of 38% at 655 nm and 39% at 555 nm for a 0.5 s switch, demonstrating the advantage of an aqueous compatible electrochrome switchable in high ionic conductivity aqueous electrolytes. The results of the electrochromic properties study indicate that these polymers are promising candidates for aqueous processable and aqueous switching electrochromic materials and devices as desired for applications where environmental impact is of importance.