In predicting the shear transfer strength (STS) of concrete joints, numerous design
parameters need to be considered due to diverse application scenarios and various …

High-temperature alloy design requires a concurrent consideration of multiple mechanisms
at different length scales. We propose a workflow that couples highly relevant physics into …

This study introduces a machine learning approach to predict the effect of alloying elements
and test conditions on the hydrogen environment embrittlement (HEE) index of austenitic …

Advances in machine learning (ML), especially in the cooperation between ML predictions,
density functional theory (DFT) based first-principles calculations, and experimental …

For the design of stainless steel bolted connections, current design codes firstly calculate the
design resistance of each potential failure mode and then take the minimum of the design …

For the design of high strength steel bolted connections, all existing standards adopt the
same framework–(i) calculating the design resistance for each potential failure mode and (ii) …

Although it is of scientific and practical importance, the state-of-the-art of predicting the
thermal expansion of oxides over broad temperature and composition ranges by physics …

Accelerating the discovery of advanced materials is crucial for modern industries,
aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are …

Flexible sensors have gained extensive interest because of their versatile applications in
healthcare, robotics, and wearable devices. This study introduces a capacitive sensor …

Although of practical importance, there is no established modeling framework to accurately
predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the …