High-entropy alloys (HEAs) are alloys that contain multiple principal elements, each in the range of 5–35%. HEAs exhibit excellent properties, however, even with conventional trial-and-error, high-throughput experimentation, and computational materials approaches, exploring their vast compositional space remains highly challenging. Accordingly, data-driven machine learning and generative-model-based inverse design methods are increasingly essential. In this study, we propose a generative-model-enabled HEA inverse design framework aimed at improving ultimate tensile strength (UTS). We first compiled 501 HEA data points from published literature and performed statistical analyses to understand their characteristics. Next, we tuned the hyperparameters of XGBoost and random forest (RF) models via Bayesian optimization, compared their performance with that of a deep neural network (DNN), and selected XGBoost as the optimal predictive model. In the subsequent stage, we trained a PyTorch-based variational autoencoder (VAE) on data from regions of the latent space associated with high-UTS probability. We randomly sampled 1,000 latent vectors, decoded them to generate candidate alloy compositions, and evaluated these candidates using the optimized XGB model. Finally, Shapley additive explanations (SHAP) interpretability analysis and a network plot were used to quantify the contributions and interactions of each feature variable, thereby assessing the physical plausibility of the model-suggested compositions.

Sulfur copolymer (poly(S-r-CEA)) was synthesized via facile inverse vulcanization of elemental sulfur with 2-carboxyethyl acrylate (CEA). Polysulfide (PS) oligomer was soluble to common solvents including DMF, producing homogenous dope solution with PAN as filler. PS-PAN was electrospun resulting to nanofiber membrane effective for Hg2+ sequestration with recorded maximum capacity of 612 mg g-1 based on Langmuir model isotherm. Kinetics, selectivity and reusability were also evaluated. This work presents new and cheap yet effective material for heavy metal sequestration from contaminated water. This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2015R1A2A1A15055407) and by the Ministry of Education (No. 2009-0093816).

본 논문에서는 역강성법을 비좌굴 가새골조시스템의 내진설계에 적용하여 목표 변위를 만족시키는 비좌굴 가새의 단면적을 산정하였으며, 기존 연구와 비교하여 타당성을 검토하였다. 본 설계 방법은 개념이 직관적이고 적용이 간편하며, 고차모드 고려가 가능하고 각 층의 연성도를 개별적으로 조절할 수 있는 장점이 있다. 각 모델에 본 설계법을 적용한 결과, 전반적으로 목표변위 증가에 따라 가새 단면적이 감소하지만 층수와 연성도의 증가에 따라 일부층에서 오히려 가새 단면적이 증가하는 것을 확인하였다. 또한 항복 후 강성비의 변화가 단면적에 끼치는 영향은 가새의 항복 후 강성비가 작은 경우 더 민감한 차이를 보이는 것으로 나타났다.