A review of the application of machine learning and data mining approaches in continuum materials mechanics
Machine learning tools represent key enablers for empowering material scientists and
engineers to accelerate the development of novel materials, processes and techniques. One …
engineers to accelerate the development of novel materials, processes and techniques. One …
Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials
Conventional approaches to developing biomaterials and implants require intuitive tailoring
of manufacturing protocols and biocompatibility assessment. This leads to longer …
of manufacturing protocols and biocompatibility assessment. This leads to longer …
Material structure-property linkages using three-dimensional convolutional neural networks
The core materials knowledge needed in the accelerated design, development, and
deployment of new and improved materials is most accessible when cast in the form of …
deployment of new and improved materials is most accessible when cast in the form of …
Machine‐learning modeling for ultra‐stable high‐efficiency perovskite solar cells
Y Hu, X Hu, L Zhang, T Zheng, J You… - Advanced Energy …, 2022 - Wiley Online Library
Understanding the key factor driving the efficiency and stability of semiconductor devices is
vital. To date, the key factor influencing the long‐term stability of perovskite solar cells …
vital. To date, the key factor influencing the long‐term stability of perovskite solar cells …
In-situ investigation of plasticity in a Ti-Al-V-Fe (α+ β) alloy: Slip mechanisms, strain localization, and partitioning
As one of the representative characteristics of plastic deformation, microstructural plastic
strain inhomogeneity has triggered a broad interest in uncovering the corresponding …
strain inhomogeneity has triggered a broad interest in uncovering the corresponding …
Materials informatics
S Ramakrishna, TY Zhang, WC Lu, Q Qian… - Journal of Intelligent …, 2019 - Springer
Materials informatics employs techniques, tools, and theories drawn from the emerging
fields of data science, internet, computer science and engineering, and digital technologies …
fields of data science, internet, computer science and engineering, and digital technologies …
Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials
Various machine learning models have been used to predict the properties of polycrystalline
materials, but none of them directly consider the physical interactions among neighboring …
materials, but none of them directly consider the physical interactions among neighboring …
Multiple tensor-on-tensor regression: An approach for modeling processes with heterogeneous sources of data
In recent years, measurement or collection of heterogeneous sets of data such as those
containing scalars, waveform signals, images, and even structured point clouds, has …
containing scalars, waveform signals, images, and even structured point clouds, has …
Towards ultra-high strength dual-phase steel with excellent damage tolerance: The effect of martensite volume fraction
Abstract Ultra-high strength (> 1 GPa) dual-phase (DP) steels have been extensively
investigated in literature. Yet, the damage tolerance of these DP steels remains mostly …
investigated in literature. Yet, the damage tolerance of these DP steels remains mostly …
Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of nickel-based superalloys
Nickel-based superalloys, used extensively in advanced gas turbine engines, exhibit
complex microstructures that evolve during exposure to high temperatures (ie, aging …
complex microstructures that evolve during exposure to high temperatures (ie, aging …