The 2021 room-temperature superconductivity roadmap
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 …
state physics and chemistry. Research efforts worldwide are funneled into a few high-end …
Artificial intelligence for search and discovery of quantum materials
Artificial intelligence and machine learning are becoming indispensable tools in many areas
of physics, including astrophysics, particle physics, and climate science. In the arena of …
of physics, including astrophysics, particle physics, and climate science. In the arena of …
[HTML][HTML] Autonomous materials synthesis by machine learning and robotics
R Shimizu, S Kobayashi, Y Watanabe, Y Ando… - APL Materials, 2020 - pubs.aip.org
Future materials-science research will involve autonomous synthesis and characterization,
requiring an approach that combines machine learning, robotics, and big data. In this paper …
requiring an approach that combines machine learning, robotics, and big data. In this paper …
Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring
More than a century after the discovery of superconductors (SCs), numerous studies have
been accomplished to take advantage of SCs in physics, power engineering, quantum …
been accomplished to take advantage of SCs in physics, power engineering, quantum …
A generative approach to materials discovery, design, and optimization
Despite its potential to transform society, materials research suffers from a major drawback:
its long research timeline. Recently, machine-learning techniques have emerged as a viable …
its long research timeline. Recently, machine-learning techniques have emerged as a viable …
Machine learning prediction of superconducting critical temperature through the structural descriptor
J Zhang, Z Zhu, XD **ang, K Zhang… - The Journal of …, 2022 - ACS Publications
Superconductivity allows electric conductance with no energy losses when the ambient
temperature drops below a critical value (T c). Currently, the machine learning (ML)-based …
temperature drops below a critical value (T c). Currently, the machine learning (ML)-based …
The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning
Machine learning (ML) has experienced rapid development in recent years and been widely
applied to assist studies in various research areas. Two-dimensional (2D) materials, due to …
applied to assist studies in various research areas. Two-dimensional (2D) materials, due to …
Clustering superconductors using unsupervised machine learning
B Roter, N Ninkovic, SV Dordevic - Physica C: Superconductivity and its …, 2022 - Elsevier
In this work we used unsupervised machine learning methods in order to find possible
clustering structures in superconducting materials data sets. We used the SuperCon …
clustering structures in superconducting materials data sets. We used the SuperCon …
Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells
W Hussain, S Sawar, M Sultan - RSC advances, 2023 - pubs.rsc.org
Perovskite solar cells offer great potential for smart energy applications due to their flexibility
and solution processability. However, the use of solution-based techniques has resulted in …
and solution processability. However, the use of solution-based techniques has resulted in …
Machine-learning-guided prediction models of critical temperature of cuprates
Cuprates have been at the center of long debate regarding their superconducting
mechanism; therefore, predicting the critical temperatures of cuprates remains elusive …
mechanism; therefore, predicting the critical temperatures of cuprates remains elusive …