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 …

The 2021 room-temperature superconductivity roadmap

B Lilia, R Hennig, P Hirschfeld, G Profeta… - Journal of Physics …, 2022 - iopscience.iop.org
Designing materials with advanced functionalities is the main focus of contemporary solid-
state physics and chemistry. Research efforts worldwide are funneled into a few high-end …

Machine learning and the physical sciences

G Carleo, I Cirac, K Cranmer, L Daudet, M Schuld… - Reviews of Modern …, 2019 - APS
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

A critical review of machine learning of energy materials

C Chen, Y Zuo, W Ye, X Li, Z Deng… - Advanced Energy …, 2020 - Wiley Online Library
Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

The physics of pair-density waves: cuprate superconductors and beyond

DF Agterberg, JCS Davis, SD Edkins… - Annual Review of …, 2020 - annualreviews.org
We review the physics of pair-density wave (PDW) superconductors. We begin with a
macroscopic description that emphasizes order induced by PDW states, such as charge …

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

M Botifoll, I Pinto-Huguet, J Arbiol - Nanoscale Horizons, 2022 - pubs.rsc.org
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Y **e, K Sattari, C Zhang, J Lin - Progress in Materials Science, 2023 - Elsevier
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

Machine learning for quantum matter

J Carrasquilla - Advances in Physics: X, 2020 - Taylor & Francis
Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

[HTML][HTML] The 2021 quantum materials roadmap

F Giustino, JH Lee, F Trier, M Bibes… - Journal of Physics …, 2021 - iopscience.iop.org
In recent years, the notion of'Quantum Materials' has emerged as a powerful unifying
concept across diverse fields of science and engineering, from condensed-matter and …

Heterogeneity at multiple length scales in halide perovskite semiconductors

EM Tennyson, TAS Doherty, SD Stranks - Nature Reviews Materials, 2019 - nature.com
Materials with highly crystalline lattice structures and low defect concentrations have
classically been considered essential for high-performance optoelectronic devices …