Science and Technology of Advanced Materials: Methods

Science and Technology of Advanced Materials: Methods (STAM Methods (STAM-M) ) is a peer-reviewed open-access journal for the publication of high-quality original research and reviews describing significant advances in methods and tools associated with materials science.

With the goal to improve and accelerate the discovery and applications of materials, STAM-M provides a platform for disseminating the latest information on developments in emerging methods and tools relevant to materials research to encourage real-world applications of data-driven methodology and computational approaches for materials design, analysis, and processing. The journal is aimed at a broad audience of researchers in academia and industry who are actively involved in materials science, especially from the viewpoint of instrumentation, informatics, and computer science, thereby facilitating interdisciplinary communication that often generates new challenges, which in turn spur further the development of advanced methods and tools.

Specific areas of interest include, but are not limited to:

  • Methodology, apparatus
  • Modeling and simulation
  • High throughput experimentation, instrumentation, and calculation
  • AI, machine learning, data-driven analysis
  • Data mining, high throughput screening
  • Computer systems and services
  • Databases

News

processor
13 Jan 2025
Electron spin states can now be efficiently explored at much higher resolution, opening new opportunities for faster electronics including quantum computers.
02 Dec 2024
By identifying the ideal manufacturing conditions, machine learning reduces the need for expensive and time-consuming experimentation.
17 Apr 2024
Electron spin states can now be probed at much higher resolution and more efficiently, opening new opportunities in materials analysis and data processing technologies.
xray machine learning
26 Feb 2024
Analysis of materials can be done quicker and with less expertise with the help of proven machine learning techniques established in biomedical fields.
GPT4 chemistry
26 Feb 2024
GPT-4 shows promise as an aid to chemistry researchers, yet its limitations reveal the need for further improvements.
robotic head
26 Feb 2024
Researchers have developed a proof-of-concept system that allows robotic experiments to run without any human intervention.
molecular structure representation
26 Feb 2024
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.
robot hand on keyboard
26 Feb 2024
Researchers have combined machine learning with robotic process automation to speed up and simplify a time-consuming process.
metal bar
15 Feb 2024
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
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
The latest ‘large language model’ artificial intelligence system, GPT-4, could aid chemistry researchers, but limitations reveal the need for improvements.
NIMS-OS links AI and robotics for innovative materials research
23 Aug 2023
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
Two key challenges in chemistry innovation are solved simultaneously by exploring chemical opportunities with artificial intelligence.
03 Mar 2023
Machine learning algorithms allow analysis and characterization of the atomic arrangement of silicon surface superstructures without the need for human expertise.
robot hand holding plant
28 Feb 2023
A model that rapidly searches through large amounts of materials could find sustainable alternatives to existing composites.
01 Dec 2022
Machine learning and robotic process automation combine to speed up and simplify a process used to determine crystal structures.
04 Nov 2022
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
A model that rapidly searches through large numbers of materials could find sustainable alternatives to existing composites.
STAM Image
03 Mar 2022
A quick, cost-effective approach improves the accuracy with which machine learning models can predict the properties of new materials.
29 Sep 2021
A quick, cost-effective approach improves the accuracy with which machine learning models can predict the properties of new materials.