materials informatics
Recently Published Documents


TOTAL DOCUMENTS

179
(FIVE YEARS 110)

H-INDEX

18
(FIVE YEARS 7)

2022 ◽  
Vol 201 ◽  
pp. 110939
Author(s):  
Luis Enrique Vivanco-Benavides ◽  
Claudia Lizbeth Martínez-González ◽  
Cecilia Mercado-Zúñiga ◽  
Carlos Torres-Torres

Nano Energy ◽  
2021 ◽  
pp. 106868
Author(s):  
Wei Xia ◽  
Zhufeng Hou ◽  
Jing Tang ◽  
Jingjing Li ◽  
Watcharop Chaikittisilp ◽  
...  

Author(s):  
Hideo Suzuki ◽  
Shin Kurosawa ◽  
Stephen Marcella ◽  
Masaru Kanba ◽  
Yuichi Koretaka ◽  
...  

Abstract The application of AI will develop further in the area of material technology similarly to how the application has advanced in the pharmaceutical industry. In this article, we explain how AI is applied in the pharmaceutical industry and in the material sciences. First, we show the trends of AI in data analysis for the different areas of the pharmaceutical industry. Second, we explain how the new machine learning platform (AutoML), in particular, benefits this type of data analysis by describing supervised machine learning. If the target value is available to define, executing the supervised machine learning is feasible to solve the problem. In this case, Implementing an AutoML process is the simple solution to look for insight. Third, we provide and discuss an example of an output from analysis done using unsupervised machine learning such as topological data analysis (TDA) as a new approach. Finally, we explain that these successful examples of AI applications in pharma provide a potential roadmap of how they may be applied to the science of material informatics. Adding new data to the current data is almost always required. Achievements are observed in the area of life science because many databases are consolidated into one database. Thus, creating new data with appropriate definitions and expanding the amount of applicable data will help materials informatics evolve into a field with both higher quality and more robust analyses in the future.


2021 ◽  
Author(s):  
Anthony Wang ◽  
Mahamad Salah Mahmoud ◽  
Mathias Czasny ◽  
Aleksander Gurlo

Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike.


Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5764
Author(s):  
Karol Frydrych ◽  
Kamran Karimi ◽  
Michal Pecelerowicz ◽  
Rene Alvarez ◽  
Francesco Javier Dominguez-Gutiérrez ◽  
...  

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials’ discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.


2021 ◽  
pp. 2104696 ◽  
Author(s):  
Rhys E. A. Goodall ◽  
Bonan Zhu ◽  
Judith L. MacManus‐Driscoll ◽  
Alpha A. Lee

Sign in / Sign up

Export Citation Format

Share Document