Diffusion models: A comprehensive survey of methods and applications

L Yang, Z Zhang, Y Song, S Hong, R Xu, Y Zhao… - ACM Computing …, 2023 - dl.acm.org
Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022 - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

[HTML][HTML] Machine learning for a sustainable energy future

Z Yao, Y Lum, A Johnston, LM Mejia-Mendoza… - Nature Reviews …, 2023 - nature.com
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

Generative models as an emerging paradigm in the chemical sciences

DM Anstine, O Isayev - Journal of the American Chemical Society, 2023 - ACS Publications
Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

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 …

[HTML][HTML] 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 …

Analyses of internal structures and defects in materials using physics-informed neural networks

E Zhang, M Dao, GE Karniadakis, S Suresh - Science advances, 2022 - science.org
Characterizing internal structures and defects in materials is a challenging task, often
requiring solutions to inverse problems with unknown topology, geometry, material …

Machine learning for alloys

GLW Hart, T Mueller, C Toher, S Curtarolo - Nature Reviews Materials, 2021 - nature.com
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

Machine learning for high-entropy alloys: Progress, challenges and opportunities

X Liu, J Zhang, Z Pei - Progress in Materials Science, 2023 - Elsevier
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …