Understanding and design of metallic alloys guided by phase-field simulations
Y Zhao - npj Computational Materials, 2023 - nature.com
Phase-field method (PFM) has become a mainstream computational method for predicting
the evolution of nano and mesoscopic microstructures and properties during materials …
the evolution of nano and mesoscopic microstructures and properties during materials …
From classical thermodynamics to phase-field method
Phase-field method is a density-based computational method at the mesoscale for modeling
and predicting the temporal microstructure and property evolution during materials …
and predicting the temporal microstructure and property evolution during materials …
Atomic‐Level Design of Active Site on Two‐Dimensional MoS2 toward Efficient Hydrogen Evolution: Experiment, Theory, and Artificial Intelligence Modelling
Atom‐economic catalysts open a new era of computationally driven atomistic design of
catalysts. Rationally manipulating the structures of the catalyst with atomic‐level precision …
catalysts. Rationally manipulating the structures of the catalyst with atomic‐level precision …
[HTML][HTML] MA2Z4 family heterostructures: Promises and prospects
Recent experimental synthesis of ambient-stable MoSi 2 N 4 monolayer has garnered
enormous research interest. The intercalation morphology of MoSi 2 N 4—composed of a …
enormous research interest. The intercalation morphology of MoSi 2 N 4—composed of a …
Accelerating materials discovery using machine learning
Y Juan, Y Dai, Y Yang, J Zhang - Journal of Materials Science & …, 2021 - Elsevier
The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …
modern society and technology innovation, the traditional materials research mainly …
Progress and challenges for memtransistors in neuromorphic circuits and systems
Due to the increasing importance of artificial intelligence (AI), significant recent effort has
been devoted to the development of neuromorphic circuits that seek to emulate the energy …
been devoted to the development of neuromorphic circuits that seek to emulate the energy …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
From prediction to design: recent advances in machine learning for the study of 2D materials
H He, Y Wang, Y Qi, Z Xu, Y Li, Y Wang - Nano Energy, 2023 - Elsevier
Although data-driven approaches have made significant strides in various scientific fields,
there has been a lack of systematic summaries and discussions on their application in 2D …
there has been a lack of systematic summaries and discussions on their application in 2D …
A bright future for engineering piezoelectric 2D crystals
The piezoelectric effect, mechanical-to-electrical and electrical-to-mechanical energy
conversion, is highly beneficial for functional and responsive electronic devices. To fully …
conversion, is highly beneficial for functional and responsive electronic devices. To fully …
2D materials-based nanoscale tunneling field effect transistors: current developments and future prospects
The continuously intensifying demand for high-performance and miniaturized semiconductor
devices has pushed the aggressive downscaling of field-effect transistors (FETs) design …
devices has pushed the aggressive downscaling of field-effect transistors (FETs) design …