相关论文: An Interpretable Boosting-based Predictive Model f…
Pre-trained protein language models have demonstrated significant applicability in different protein engineering task. A general usage of these pre-trained transformer models latent representation is to use a mean pool across residue…
Besides permitting an accurate determination of the ferromagnetic-to-paramagnetic phase transition temperature and the characteristic temperatures for the beginning and end of the growth of martensite (austenite) phase at the expense of…
Halide perovskites exhibit unpredictable properties in response to environmental stressors, due to several composition-dependent degradation mechanisms. In this work, we apply data visualization and machine learning (ML) techniques to…
Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles…
Stacking fault energies (SFEs) are vital parameters for understanding the deformation mechanisms in metals and alloys, with prior knowledge of SFEs from ab initio calculations being crucial for alloy design. Machine learning (ML) algorithms…
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a…
Chemical and biomass processing systems release volatile matter compounds into the environment daily. Catalytic reforming can convert these compounds into valuable fuels, but developing stable and efficient catalysts is challenging. Machine…
Shape memory polymers (SMPs) are materials with a great potential for future use in smart materials and structures. When heated from cold state (below the transformation temperature, which can either be the glass transition temperature or…
The irreversibility of the martensite transition in magnetic shape memory alloys (MSMAs) with respect to external magnetic field is one of the biggest challenges that limits their application as giant caloric materials. This transition is a…
With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive…
We have synthesized a nominal composition of Ni50Mn30Sn20 alloy using arc melting technique. Rietveld refinement confirms the austenite L21 structure in Fm-3m space group. Electrical resistivity has been found to clearly exhibiting two…
Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is…
Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies using…
Materials exhibiting a martensitic phase transition are essential for applications in shape memory alloys, actuators and sensors. Hexamethylbenzene (HMB) has long been considered as a classical example of ferroelastic organic crystals since…
We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of…
The magnetic ground state of the ferromagnetic shape memory alloy of nominal composition Cu$_{2.84}$Mn$_{0.44}$Al$_{0.72}$ was investigated. The sample shows reentry of a glassy magnetic phase below the martensitic transition temperature,…
Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions pose significant challenges for accurately modeling arbitrary…
We present here results of temperature dependent high resolution synchrotron x-ray powder diffraction study of sequence of phase transitions in Ni2MnGa. Our results show that the incommensurate martensite phase results from the…
The first order martensitic transition in the ferromagnetic shape memory alloy Ni45Co5Mn38Sn12 is also a magnetic transition and has a large field induced effect. While cooling in the presence of field this first order magnetic martensite…
The partially overlapped ferroelastic/martensitic and para-ferromagnetic phase transitions of a Ni$_{50.53}$Mn${33.65}$In$_{15.82}$ metamagnetic shape memory alloy have been studied from calorimetric, magnetic and acoustic emission…