With advances in informatics applied to materials science, predicting the physical properties of numerous materials has become increasingly feasible, creating a growing demand for their experimental validation. It has been expected that the integration of robotic systems into experimental materials science excels at efficiently performing repetitive and time-consuming tasks without the need for human intervention, thus significantly increasing throughput and reducing the risk of human error, while there have been a limited number of reports tackled the synthesis process of solid bulk material so far possibly because of the complex as well as a wide variety of processes to deal with. In this paper, we report an automated arc melting system controlled by a robot operating system 2 (ROS2). Taking advantage of ROS2, we have constructed a machine that can handle multiple experimental apparatuses simultaneously with flexibility for future expansion of functions. The constructed machine is capable of not only performing repeated operation of a specific process but also dealing with multiple elements for synthesis of intermetallic compounds. The system is expected to accelerate experimental validation of data-driven materials exploration.

We investigated segregant development for FePt high-density magnetic recording media using large language models (LLMs) and identified new fluoride-based materials. By increasing the temperature of the softmax function to select probable vocabularies, we successfully incorporated the prior knowledge involved in LLM training into the material selection process (exploratory generation) and identified LaF3 as an optimal material. Meanwhile, we independently conducted sputtering deposition of FePt-LaF3 nanogranular samples and verified whether the LLM could reproduce the results. The inhomogeneity in the surface chemical composition of FePt-LaF3 in the non-equilibrium state of sputtering were also reproduced by the LLM, leading to the identification of AlF3 as an alternative segregant. Structures and magnetic properties strongly dependent on the sputtering apparatus cannot be reproduced by simple LLM predictions, necessitating interactive data exchange between physical and cyber experiments. However, phenomena that can occur in physical experiments are almost accurately reproduced by LLM, demonstrating the usefulness of LLM predictions in material development. LLMs that generate responses from the most likely vocabulary tend to select well-known materials. To identify unknown materials as candidates, techniques such as exploratory generation or inputting unexpected results are required.

To accelerate the digital transformation (DX) in materials science, foundational technology for effectively managing vast amounts of data from diverse experiments and calculations and sharing them in a reusable format is essential. However, a significant barrier is that data formats, description styles, and terminologies differ among research fields, instrument manufacturers, and models, and are often not publicly available as readily usable schemas, making them unreadable. Research Data Express (RDE), introduced in this paper, is a highly flexible and scalable data accumulation and sharing system developed to solve these challenges. The core feature of RDE is the ‘Dataset Template’, which defines the format for data description and translation. Users can flexibly define data structures tailored to their research content using these templates. During data registration, raw files from experimental instruments and manually entered experimental conditions are automatically interpreted, integrated, and structured by a processing program defined in the template. This automated process includes metadata extraction, translation into common domain-specific terms, data visualization, and even the calculation of feature values for machine learning applications. In this paper, we detail the basic design and system architecture of RDE and explain the data management methodology based on Dataset Templates. RDE significantly reduces the burden of routine data processing for researchers and enhances data findability, interoperability, reusability (the FAIR principles), and traceability, thereby strongly promoting data-driven materials research.

Optical microscopy is vital in many scientific fields, and various super-resolution techniques have been developed to overcome the resolution limit that restricts the separation of spatially mixed light. However, conventional methods inherently cannot resolve overlapping optical polarization (OP) components, limiting the ‘polarization resolution’ in polarized light microscopy. Instead of quantitatively evaluating ‘polarization resolution’, this study aims to reliably separate intrinsic OP states based on consistent clustering results that are robust to variations in the spatial receptive field (SRF) size. We integrate statistical analysis, machine learning, and deep learning to evaluate overlapping OP states in temperature-dependent birefringence imaging of the stress-induced ferroelectric SrTiO[Formula: see text] under an external force of 231 MPa. A long short-term memory (LSTM) network is used to extract temperature-dependent features from sequential image data, which effectively captures subtle changes in structural and ferroelectric phase transitions. A 3D convolutional autoencoder (3DCAE) learns spatial relationships between adjacent pixels from these temperature-dependent features, addressing OP overlap at different spatial scales based on different SRF sizes. Although the 3DCAE output considerably depends on the SRF size, clustering results obtained via temperature series forest (Tsf) analysis are highly consistent. This robustness indicates that the extracted OP states reflect physically meaningful spatial distributions rather than convolution artifacts. The proposed sequential analytical framework successfully reconstructs intrinsic OP distributions while balancing local and global structural features, providing a robust foundation for OP-sensitive imaging in materials science.

We designed a data structure to promote the reuse of experimental data collected from multiple research institutions in a research project on solid electrolytes for next-generation batteries. Leveraging data from multiple research institutions is extremely challenging because the data formats and items differ, preventing their integration and joint use. Therefore, a data structure is required that meets the following criteria: (1) it is readily usable by researchers, (2) it introduces commonality into the data, and (3) it is easily understood by third parties. By analysing experimental data from several institutions, we decomposed each process into ‘elementary process units’ and proposed a ‘process‑chain’ data structure created by concatenating these units. Furthermore, by adopting RDE – the cloud‑based data acquisition system developed by NIMS – and linking the tabular format to the RDE format, we streamlined data collection and management. The proposed data structure is not limited to solid electrolytes but can also be applied to the management of experimental data for other materials, making it useful as a cross-disciplinary method for accumulating material data.

The Institute for Solid State Physics (ISSP) at The University of Tokyo has been carrying out a software development project named ‘the Project for Advancement of Software Usability in Materials Science (PASUMS)’. Since the launch of PASUMS, various open-source software programs have been developed/advanced, including the first-principles calculations, effective model solvers, and software for machine learning. We also focus on activities that make the software easier to use, such as developing comprehensive computing tools that enable efficient use of supercomputers and interoperability between different software programs. We hope to contribute broadly to developing the computational materials science community through these activities.

The development of next-generation permanent magnets has become critical due to the limited performance improvements in Nd-Fe-B magnets and concerns over rare earth supply. ThMn [Formula: see text]-type rare-earth intermetallic compounds have emerged as promising alternatives, offering superior performance and reduced rare earth content. This study systematically investigates the magnetic properties of Sm(Fe[Formula: see text])-based thin films synthesized via combinatorial sputtering. Various stabilizing elements (e.g. Ti, V, Co, Cr) were analyzed to explore their effects on phase stability, saturation magnetization ([Formula: see text]), anisotropy field ([Formula: see text]), and Curie temperature ([Formula: see text]). High-throughput structural and magnetic characterizations, coupled with machine learning (ML) predictions, facilitated efficient data acquisition and analysis. Experimental results reaffirmed trends such as [Formula: see text] enhancement with Co and phase-stabilization capabilities of Ti and V. Novel insights into additives like Cr and Ta revealed potential [Formula: see text] improvements. ML regression models (Random Forest and XGBoost) identified electronegativity as a key factor influencing [Formula: see text]. Predictive analyses successfully estimated [Formula: see text] trends and ThMn[Formula: see text] phase stability for unexplored compositions, enhancing the active learning framework for material discovery. This work highlights the synergy of combinatorial deposition, high-throughput data collection, and ML-assisted prediction in accelerating the exploration of magnetic materials. Future extensions to multi-element systems and other magnetic phases are expected to expedite the discovery of high-performance magnets for motors and energy applications.

Generative AI has recently had a profound impact on various fields, including daily life, research, and education. To explore its efficient utilization in data-driven materials science, we organized a hackathon – AIMHack2024—in July 2024. In this hackathon, researchers from fields such as materials science, information science, bioinformatics, and condensed matter physics worked together to explore how generative AI can facilitate research and education. Based on the results of the hackathon, this paper presents topics related to (1) conducting AI-assisted software trials, (2) building AI tutors for software, and (3) developing GUI applications for software. While generative AI continues to evolve rapidly, this paper provides an early record of its application in data-driven materials science and highlights strategies for integrating AI into research and education.

In the recurrence of cancer and autoimmune diseases, cellular senescence, a state where cells cease division, plays a pivotal role. Distinguishing the senescence requires a combination of multiple markers. Fluorescent dyes incorporation limits the imaging observation time due to the potential toxicity of those compounds over prolonged exposure, while colorimetric dye incorporation requires cell fixation, where cells are not in native state, and long staining time (≥4 hours). One of the distinct characteristics that distinguishes senescent cells from other state of cells is the impaired protein degradation ability by the lysosome, resulting on the protein accumulation. In the current work, we take advantage of protein accumulation to distinguish senescence cells from other cells. The protein concentration is visualized by recording the phase difference using the quantitative phase microscopy (QPM). Our live QPM observations on breast cancer cells confirm that senescent cells exhibit a higher refractive index compared to cells in normal growth and quiescent states, attributed to the accumulation of undigested lysosomal protein cargo. This heightened refractive index suggests an imbalance in protein turnover rates within senescent cells. These findings shed light on a potential label-free non-invasive approach for a prolonged monitoring of senescent cell dynamics.

The discovery of high-performance electrochemical materials is essential for sustainable energy technologies, yet conventional methods rely on trial-and-error experiments, time-consuming computations, and detailed structural data. To address these challenges, we introduce a periodic table-based compositional descriptor that requires only chemical formulas, enabling efficient material discovery with reversible design. We applied this approach to two key applications: fast Li-ion conductors for solid-state electrolytes and platinum-group metal (PGM)-free oxygen evolution reaction (OER) electrocatalysts. Our model identified both known and new Li-ion conductors, including anti-fluorite structures with high ionic conductivity at 600–700 K — significantly lower than traditional compounds like Li2S and Li2Se. For OER electrocatalysts, we predicted Fe0.1Co0.1Cu0.1Ag0.1W0.6 oxide, which exhibited experimentally validated performance comparable to RuO2 but at a lower overpotential. The periodic descriptor offers a scalable and efficient framework for accelerating the discovery of green energy materials, including next-generation batteries and hydrogen production, contributing to carbon neutrality.