Results 241 to 250 of about 16,538,551 (301)

Artificial Intelligence-Powered Materials Science [PDF]

Nanomicro Lett
A detailed exploration is provided of how artificial intelligence (AI) and machine learning techniques are applied across various aspects of materials science. Major challenges in AI-driven materials science are evaluated.
Xiaopeng Bai, Xingcai Zhang
semanticscholar   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Implant Material Sciences

Dental Clinics of North America, 2021
In the past, the only way to replace missing teeth was to have a removable appliance. However, these days, dental implants are commonly being used to replace missing teeth. The dental implants are improving as a result of new technological and scientific advances.
Allen, Glied, Junaid, Mundiya
openaire   +2 more sources

Materials Science Challenges to Graphene Nanoribbon Electronics.

ACS Nano, 2021
Graphene nanoribbons (GNRs) have recently emerged as promising candidates for channel materials in future nanoelectronic devices due to their exceptional electronic, thermal, and mechanical properties and chemical inertness. However, the adoption of GNRs
V. Saraswat, R. Jacobberger, M. Arnold
semanticscholar   +1 more source

Innovative Materials Science via Machine Learning

Advanced Functional Materials, 2021
Nowadays, the research on materials science is rapidly entering a phase of data‐driven age. Machine learning, one of the most powerful data‐driven methods, have been being applied to materials discovery and performances prediction with undoubtedly ...
Chao Gao, X. Min, M. Fang, Tianyi Tao, Xiaohong Zheng, Yan’gai Liu, Xiaowen Wu, Zhaohui Huang   +7 more
semanticscholar   +1 more source

Encoding the atomic structure for machine learning in materials science

WIREs Computational Molecular Science, 2021
In recent years, we have witnessed a widespread application of machine learning techniques in the field of materials science, owing to the increased availability of research data and sophisticated algorithms.
Shunning Li, Yuanji Liu, Dong Chen, Yi Jiang, Zhiwei Nie, Feng Pan   +5 more
semanticscholar   +1 more source

Membrane research beyond materials science

Journal of Membrane Science, 2021
The majority of research in membrane separation science is focused on developing better membrane materials, yet very few of these materials are being used in commercial applications.
U. Beuscher, E. Kappert, J. G. Wijmans
semanticscholar   +1 more source

Catalyze Materials Science with Machine Learning

ACS Materials Letters, 2021
Discovering and understanding new materials with desired properties are at the heart of materials science research, and machine learning (ML) has recently offered special shortcuts to the ultimate goal.
Jaehyun Kim, D. Kang, Sangbum Kim, Ho Won Jang   +3 more
semanticscholar   +1 more source

HoneyComb: A Flexible LLM-Based Agent System for Materials Science

Conference on Empirical Methods in Natural Language Processing
The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks for materials science. Many LLMs, however, often struggle with distinct complexities of material science tasks, such as materials science ...
Huan Zhang, Yu Song, Ziyu Hou, Santiago Miret, Bang Liu   +4 more
semanticscholar   +1 more source

Materials Science

Journal of Dental Research, 1981
Wax pattern surface treatments were compared to determine their effectiveness in reducing contact angles incident at wax pattern-investment interfaces.. Gypsum-bonded and phosphate-bonded investments were examined. A coordinate system was used to obtain a polynomial expression from which the contact angles could be determined.
J.T. Morrison, M.G. Duncanson, H.T. Shillingburg   +2 more
openaire   +2 more sources