Modeling and simulations for 2D materials: a ReaxFF perspective
Recent advancements in the field of two-dimensional (2D) materials have led to the
discovery of a wide range of 2D materials with intriguing properties. Atomistic-scale …
discovery of a wide range of 2D materials with intriguing properties. Atomistic-scale …
Deep learning-assisted methods for accelerating the intelligent screening of novel 2D materials: New perspectives focusing on data collection and description
Y Lin, J Ma, YG Jia, C Yu, JH Cheng - Coordination Chemistry Reviews, 2025 - Elsevier
Since the isolation of graphene, the interest in two-dimensional (2D) materials has been
steadily growing thanks to their unique chemical and physical properties, as well as their …
steadily growing thanks to their unique chemical and physical properties, as well as their …
A tunable metamaterial microwave absorber inspired by chameleon's color-changing mechanism
A metamaterial absorber capable of swiftly altering its electromagnetic response in the
microwave range offers adaptability to changing environments, such as tunable stealth …
microwave range offers adaptability to changing environments, such as tunable stealth …
Reinforcement learning optimisation for graded metamaterial design using a physical-based constraint on the state representation and action space
The energy harvesting capability of a graded metamaterial is maximised via reinforcement
learning (RL) under realistic excitations at the microscale. The metamaterial consists of a …
learning (RL) under realistic excitations at the microscale. The metamaterial consists of a …
Deep reinforcement learning for inverse inorganic materials design
A major obstacle to the realization of novel inorganic materials with desirable properties is
efficient materials discovery over both the materials property and synthesis spaces. In this …
efficient materials discovery over both the materials property and synthesis spaces. In this …
Machine learning meets process control: Unveiling the potential of LSTMc
In the past three decades, proportional‐integral/PI‐differential (PI/PID) controllers and model
predictive controller (MPCs) have predominantly governed complex chemical process …
predictive controller (MPCs) have predominantly governed complex chemical process …
Investigation of mechanical properties and structural integrity of graphene aerogels via molecular dynamics simulations
Graphene aerogel (GA), a 3D carbon-based nanostructure built on 2D graphene sheets, is
well known for being the lightest solid material ever synthesized. It also possesses many …
well known for being the lightest solid material ever synthesized. It also possesses many …
Data-driven prediction of the mechanical behavior of nanocrystalline graphene using a deep convolutional neural network with PCA
W Shin, S Jang, Y Hwang, J Han - Engineering with Computers, 2024 - Springer
The mechanical properties of nanocrystalline graphene significantly depend on its complex
grain boundary configurations and defect distributions, with its inherent nanostructural …
grain boundary configurations and defect distributions, with its inherent nanostructural …
Using 3D printing as a research tool for materials discovery
In this perspective, we highlight some significant advances in polymer additive
manufacturing and bioprinting over the past few years, with an eye toward future …
manufacturing and bioprinting over the past few years, with an eye toward future …
[HTML][HTML] Deep reinforcement learning for stacking sequence optimization of composite laminates
Fiber reinforced polymer (FRP) composite laminates are increasingly used in a wide range
of safety–critical products due to their excellent material properties. The stacking sequence …
of safety–critical products due to their excellent material properties. The stacking sequence …