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Small data machine learning in materials science
P Xu, X Ji, M Li, W Lu - npj Computational Materials, 2023 - nature.com
This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …
we analyzed the limitations brought by small data. Then, the workflow of materials machine …
High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery
High-entropy nanoparticles have become a rapidly growing area of research in recent years.
Because of their multielemental compositions and unique high-entropy mixing states (ie …
Because of their multielemental compositions and unique high-entropy mixing states (ie …
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists.
Recent studies suggested that these models could be useful in chemistry and materials …
Recent studies suggested that these models could be useful in chemistry and materials …
Nanoparticle synthesis assisted by machine learning
Many properties of nanoparticles are governed by their shape, size, polydispersity and
surface chemistry. To apply nanoparticles in chemical sensing, medical diagnostics …
surface chemistry. To apply nanoparticles in chemical sensing, medical diagnostics …
A survey on active learning: State-of-the-art, practical challenges and research directions
Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …
Artificial intelligence and machine learning in design of mechanical materials
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …
is becoming an important tool in the fields of materials and mechanical engineering …
Data quantity governance for machine learning in materials science
Y Liu, Z Yang, X Zou, S Ma, D Liu… - National Science …, 2023 - academic.oup.com
Data-driven machine learning (ML) is widely employed in the analysis of materials structure–
activity relationships, performance optimization and materials design due to its superior …
activity relationships, performance optimization and materials design due to its superior …
Recent applications of machine learning in alloy design: A review
The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …
Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …