Accurately predicting copolymer properties plays a pivotal role in the field of polymer informatics. This endeavor necessitates a comprehensive understanding of polymer structures, adept feature engineering, and proficient application of machine learning algorithms. In traditional methodologies, features for each monomer structure were generated independently, thus, segregating features from individual monomers. This approach results in a less informative representation, with limited applicability. To address these challenges, we introduce an innovative machine learning framework, named weighted-chained-SMILES. By constructing a representative SMILES notation, more intricate information can be encapsulated within the generated features. Our experimental results to predict the thermal properties demonstrate that our approach not only delivers competitive predictive performance but also exhibits enhanced adaptability across a diverse range of molecular representations. The versatility showcased by our model suggests promising potential for tackling more complex copolymer systems and extending its predictive capabilities to various other polymer properties.

Chelating agents are commonly employed in microelectronic processes to prevent metal ion contamination. The ligand fragments of a chelating agent largely determine its binding strength to metal ions. Identification of ligands with suitable characteristics will facilitate the design of chelating agents to enhance the capture and removal of metal ions from the substrate in microelectronic processes. This study employed quantum chemical calculations to simulate the binding process between eleven ligands and the hydrated forms of Ni²⁺, Cu²⁺, Al³⁺, and Fe³⁺ ions. The binding strength between the metal ions and ligands was quantified using binding energy and binding enthalpy. Additionally, we explored the binding interaction mechanisms and explained the differences in binding abilities of the eleven ligands using frontier molecular orbitals, nucleophilic indexes, electrostatic potentials, and energy decomposition calculations based on molecular force fields. Based on our computational results, promising chelating agent structures are proposed, aiming to guide the design of new chelating agents to address metal ion contamination issues in integrated circuit processes.

Polyamines have become important chemical components used in several integrated circuit manufacturing processes, such as etching, chemical mechanical polishing (CMP), and cleaning. Recently, researchers pointed out that polyamines can be excellent enhancers in promoting the material removal rate (MRR) of Si CMP, but the interaction mechanism between the polyamines and the silicon surface has not been clarified. Here, the micro-interaction mechanisms of polyamines, including ethylenediamine (EDA), diethylenetriamine (DETA), triethylenetetramine (TETA), tetraethylenepentamine (TEPA), and pentaethylenehexamine (PEHA), with the Si(1, 0, 0) surface were investigated through molecular dynamics (MD) simulations using the ReaxFF reactive force field. Polyamines can adsorb onto the Si(1, 0, 0) surface, and the adsorption rate first accelerates and then tends to stabilize with the increase in the quantity of -CH₂CH₂NH-. The close connection between the adsorption properties of polyamines and the polishing rate has been confirmed by CMP experiments on silicon wafers. A comprehensive bond analysis indicates that the adsorption of polyamines can stretch surface Si–Si bonds, which facilitates subsequent material removal by abrasive mechanical wear. This work reveals the adsorption mechanism of polyamines onto the silicon substrate and the understanding of the MRR enhancement in silicon CMP, which provides guidance for the design of CMP slurry.

In this work, we propose that there exists a moving triangle singularity in the $\psi (2S)\to {\pi }^{+}{\pi }^{-}{K}^{+}{K}^{-}$ process, whose position can vary from 1.158 to 1.181 GeV in the invariant mass spectrum of $K^{+}{K}^{-}$. After a precise analysis on this process, it turns out that after doing some cuts on $m_{{\pi }^{+}{\pi }^{-}}$, experiments do have the opportunity to observe this triangle singularity. In addition, when changing the cuts on $m_{{\pi }^{+}{\pi }^{-}}$, the movement of the predicted triangle singularity can also be observed. Thus, we suggest future experiments, especially Super Tau-Charm Facility (STCF), to do an analysis on the $\psi (2S)\to {\pi }^{+}{\pi }^{-}{K}^{+}{K}^{-}$ process to verify our prediction.