Machine learning for high-entropy alloys: Progress, challenges and opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …
mechanical properties and the vast compositional space for new HEAs. However …
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
DeePMD-kit v2: A software package for deep potential models
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …
simulations using machine learning potentials known as Deep Potential (DP) models. This …
Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …
predict or elucidate the relationship between the atomic-scale structure of matter and its …
Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles
Chemical vapor deposition has been most recently employed to fabricate centimeter-scale
high-quality single-layer MoSi 2 N 4 (Science; 2020; 369; 670). Motivated by this exciting …
high-quality single-layer MoSi 2 N 4 (Science; 2020; 369; 670). Motivated by this exciting …
First‐principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine‐learning interatomic potentials
Density functional theory calculations are robust tools to explore the mechanical properties
of pristine structures at their ground state but become exceedingly expensive for large …
of pristine structures at their ground state but become exceedingly expensive for large …
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
We develop a neuroevolution-potential (NEP) framework for generating neural network-
based machine-learning potentials. They are trained using an evolutionary strategy for …
based machine-learning potentials. They are trained using an evolutionary strategy for …
Extending machine learning beyond interatomic potentials for predicting molecular properties
Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …
chemical processes and materials. ML provides a surrogate model trained on a reference …
Multi-label active learning-based machine learning model for heart disease prediction
The rapid growth and adaptation of medical information to identify significant health trends
and help with timely preventive care have been recent hallmarks of the modern healthcare …
and help with timely preventive care have been recent hallmarks of the modern healthcare …