相关论文: Coupling Physics in Machine Learning to Predict Pr…

Machine learning offers promising tools to develop surrogate models for polymer structure-property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers.…

The application machine learning (ML) algorithms to turbulence modeling has shown promise over the last few years, but their application has been restricted to eddy viscosity based closure approaches. In this article we discuss rationale…

Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical to preventing overheating, adjusting process parameters, and ensuring process stability. While physics-based computational models offer…

Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the…

Thermoelectric properties of Half Heusler alloys are predicted by adopting an ensemble modelling approach, specifically the stacking model integrated using Random Forest and XGBoost scheme. Leveraging a diverse dataset encompassing thermal…

Multi-principal element alloys (MPEAs) are produced by combining metallic elements in what is a diverse range of proportions. MPEAs reported to date have revealed promising performance due to their exceptional mechanical properties.…

Process optimization in chemical engineering may be hindered by the limited availability of reliable thermodynamic data for fluid mixtures. Remarkable progress is being made in predicting thermodynamic mixture properties by machine learning…

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model…

Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for material property prediction. As a representative example, polymer…

High-entropy alloys (HEAs) stand out between multi-component alloys due to their attractive microstructures and mechanical properties. In this investigation, molecular dynamics (MD) simulation and machine learning were used to ascertain the…

Given the strong dependence of material structure and properties on the length and strength of constituent bonds and the fact that surface adsorption and chemical reactions are initiated by the formation of bonds between two systems,…

Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work,…

Machine Learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a comparative assessment of ML models based on state-of-the-art…

The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom+diatom collisions is of considerable practical interest in atmospheric re-entry. Due to the large…

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such…

The first version of the machine learning greybox model i-Melt was trained to predict latent and observed properties of K$_2$O-Na$_2$O-Al$_2$O$_3$-SiO$_2$ melts and glasses. Here, we extend the model compositional range, which now allows…

Science-based simulation tools such as Finite Element (FE) models are routinely used in scientific and engineering applications. While their success is strongly dependent on our understanding of underlying governing physical laws, they…

Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning…

Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional…

Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to…