Machine learning‐driven biomaterials evolution
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to
achieve desired biological responses. While there is constant evolution and innovation in …
achieve desired biological responses. While there is constant evolution and innovation in …
Ceramic science of crystal defect cores
Ceramic materials are polycrystalline solids that are made up of metal and non-metal
elements, and inorganic crystal grains with specific crystal structures are fundamental …
elements, and inorganic crystal grains with specific crystal structures are fundamental …
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 …
Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials
Phononic properties are commonly studied by calculating force constants using the density
functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of …
functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of …
Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
One of the ultimate goals of computational modeling in condensed matter is to be able to
accurately compute materials properties with minimal empirical information. First-principles …
accurately compute materials properties with minimal empirical information. First-principles …
[HTML][HTML] A machine learning approach for accelerated design of magnesium alloys. Part B: Regression and property prediction
Abstract Machine learning (ML) models provide great opportunities to accelerate novel
material development, offering a virtual alternative to laborious and resource-intensive …
material development, offering a virtual alternative to laborious and resource-intensive …
Nanoinformatics, and the big challenges for the science of small things
The combination of computational chemistry and computational materials science with
machine learning and artificial intelligence provides a powerful way of relating structural …
machine learning and artificial intelligence provides a powerful way of relating structural …
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
It is well-known that the calculation of thermal conductivity using classical molecular
dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic …
dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic …
Bandgap prediction of two-dimensional materials using machine learning
Y Zhang, W Xu, G Liu, Z Zhang, J Zhu, M Li - PLoS One, 2021 - journals.plos.org
The bandgap of two-dimensional (2D) materials plays an important role in their applications
to various devices. For instance, the gapless nature of graphene limits the use of this …
to various devices. For instance, the gapless nature of graphene limits the use of this …
Drawing phase diagrams of random quantum systems by deep learning the wave functions
T Ohtsuki, T Mano - Journal of the Physical Society of Japan, 2020 - journals.jps.jp
Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …
beginning to be well accepted. Obtaining and representing the ground and excited state …