Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption

Y Wu, J Fang, C Wu, C Li, G Sun, Q Li - International Journal of Mechanical …, 2023 - Elsevier
Lightweight materials and structures have been extensively studied for a wide range of
applications in design and manufacturing of more environment-friendly and more …

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

N Yao, X Chen, ZH Fu, Q Zhang - Chemical Reviews, 2022 - ACS Publications
Rechargeable batteries have become indispensable implements in our daily life and are
considered a promising technology to construct sustainable energy systems in the future …

Global glacier change in the 21st century: Every increase in temperature matters

DR Rounce, R Hock, F Maussion, R Hugonnet… - Science, 2023 - science.org
Glacier mass loss affects sea level rise, water resources, and natural hazards. We present
global glacier projections, excluding the ice sheets, for shared socioeconomic pathways …

A survey on trajectory-prediction methods for autonomous driving

Y Huang, J Du, Z Yang, Z Zhou… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

The Russia-Ukraine conflict: Its implications for the global food supply chains

S Jagtap, H Trollman, F Trollman, G Garcia-Garcia… - Foods, 2022 - mdpi.com
Food is one of the most traded goods, and the conflict in Ukraine, one of the European
breadbaskets, has triggered a significant additional disruption in the global food supply …

A review of safe reinforcement learning: Methods, theory and applications

S Gu, L Yang, Y Du, G Chen, F Walter, J Wang… - arXiv preprint arXiv …, 2022 - arxiv.org
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Recent advances and applications of deep learning methods in materials science

K Choudhary, B DeCost, C Chen, A Jain… - npj Computational …, 2022 - nature.com
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Gaussian process regression for materials and molecules

VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Interpretable machine learning for knowledge generation in heterogeneous catalysis

JA Esterhuizen, BR Goldsmith, S Linic - Nature catalysis, 2022 - nature.com
Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …

Combustion machine learning: Principles, progress and prospects

M Ihme, WT Chung, AA Mishra - Progress in Energy and Combustion …, 2022 - Elsevier
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …