Science and Technology of Advanced Materials: Methods
About Science and Technology of Advanced Materials: Methods
Science and Technology of Advanced Materials: Methods (STAM Methods (STAM-M)) publishes research on emergent methods and tools to improve and/or accelerate materials developments, such as methodology, modeling, high-throughput experimentation, materials informatics (MI), process informatics (PI) , artificial intelligence (AI), and databases.
- Website: https://www.tandfonline.com/journals/tstm20
- Current Issue: https://www.tandfonline.com/toc/tstm20/current
News
21 Apr 2026
National Institute for Materials Science (NIMS)
Engineers at NIMS develop a system that captures all the elements of trial and error in material design, enabling reliable reproduction of the reasoning processes and results
02 Feb 2026
National Institute for Materials Science (NIMS)
A new tool offers researchers a better way of exploring and understanding catalyst data.
21 Jan 2026
National Institute for Materials Science (NIMS)
Understanding how tree-like structures that form in thin films could be the key to next-generation materials for beyond-5G communications technologies.
21 Jan 2026
National Institute for Materials Science (NIMS)
By identifying the ideal manufacturing conditions, machine learning reduces the need for expensive and time-consuming experimentation.
19 Jan 2026
National Institute for Materials Science (NIMS)
Researchers at NIMS have created Research Data Express (RDE) to automate data processing and create AI-ready datasets for materials research.
07 Jan 2026
National Institute for Materials Science (NIMS)
Large language models accelerate construction of materials property databases.
02 May 2025
National Institute for Materials Science (NIMS)
A massive new database of dielectric material properties could speed up the development of electronics like smartphones and energy storage systems.
24 Jan 2025
National Institute for Materials Science (NIMS)
Electron spin states can now be efficiently explored at much higher resolution, opening new opportunities for faster electronics including quantum computers.
02 Dec 2024
National Institute for Materials Science (NIMS)
By identifying the ideal manufacturing conditions, machine learning reduces the need for expensive and time-consuming experimentation.
17 Apr 2024
National Institute for Materials Science (NIMS)
Electron spin states can now be probed at much higher resolution and more efficiently, opening new opportunities in materials analysis and data processing technologies.
28 Feb 2024
National Institute for Materials Science (NIMS)
Analysis of materials can be done quicker and with less expertise with the help of proven machine learning techniques established in biomedical fields.
28 Feb 2024
National Institute for Materials Science (NIMS)
GPT-4 shows promise as an aid to chemistry researchers, yet its limitations reveal the need for further improvements.
28 Feb 2024
National Institute for Materials Science (NIMS)
Researchers have developed a proof-of-concept system that allows robotic experiments to run without any human intervention.
28 Feb 2024
National Institute for Materials Science (NIMS)
Researchers have developed an AI-driven system that can design novel molecules with any desired properties and suggest methods to create them using readily available materials.
28 Feb 2024
National Institute for Materials Science (NIMS)
Researchers have combined machine learning with robotic process automation to speed up and simplify a time-consuming process.
16 Feb 2024
National Institute for Materials Science (NIMS)
A new method allows scientists to gather enough information about the properties of metals to enable the prediction of the properties of new materials.
16 Nov 2023
National Institute for Materials Science (NIMS)
Analysis of materials can be done quicker and with less expertise with the help of proven machine learning techniques established in biomedical fields
16 Oct 2023
National Institute for Materials Science (NIMS)
The latest ‘large language model’ artificial intelligence system, GPT-4, could aid chemistry researchers, but limitations reveal the need for improvements.
23 Aug 2023
National Institute for Materials Science (NIMS)
The search for innovative materials will be greatly assisted by software that can suggest new experimental possibilities and also control the robotic systems that check them out.
23 May 2023
National Institute for Materials Science (NIMS)
Two key challenges in chemistry innovation are solved simultaneously by exploring chemical opportunities with artificial intelligence.
10 Mar 2023
National Institute for Materials Science (NIMS)
A model that rapidly searches through large amounts of materials could find sustainable alternatives to existing composites.
10 Mar 2023
National Institute for Materials Science (NIMS)
Machine learning algorithms allow analysis and characterization of the atomic arrangement of silicon surface superstructures without the need for human expertise.
01 Dec 2022
National Institute for Materials Science (NIMS)
Machine learning and robotic process automation combine to speed up and simplify a process used to determine crystal structures.
04 Nov 2022
National Institute for Materials Science (NIMS)
Scientists in Japan have combined two computational models to extract more data on steel alloys from a single test, with implications for the discovery of new materials.
25 May 2022
National Institute for Materials Science (NIMS)
A model that rapidly searches through large numbers of materials could find sustainable alternatives to existing composites.
08 Mar 2022
National Institute for Materials Science (NIMS)
A quick, cost-effective approach improves the accuracy with which machine learning models can predict the properties of new materials.
29 Sep 2021
National Institute for Materials Science (NIMS)
A quick, cost-effective approach improves the accuracy with which machine learning models can predict the properties of new materials.