cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25505/fiz.icsd.cc2pwlqx
title: CSD 2499180: Experimental Crystal Structure Determination

description: Related Article: Emily D. Williams, Matthew S. Powell, Colin D. McMillen, Joseph W. Kolis|2026|Cryst.Growth Des.|||doi:10.1021/acs.cgd.5c01541
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2pwlry
title: CSD 2499181: Experimental Crystal Structure Determination

description: Related Article: Emily D. Williams, Matthew S. Powell, Colin D. McMillen, Joseph W. Kolis|2026|Cryst.Growth Des.|||doi:10.1021/acs.cgd.5c01541
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2q0bkn
title: CSD 2502771: Experimental Crystal Structure Determination

description: Related Article: Alizée Deslandes, Fouad Alassani, Stanislav Péchev, Véronique Jubera, Mathieu Duttine, Jacinthe Gamon, Antoine Villesuzanne, Leïlou Loiseau-Foucher, Etienne Durand, Alexandre Fargues, Alain Demourgues|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04288
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2q0blp
title: CSD 2502772: Experimental Crystal Structure Determination

description: Related Article: Alizée Deslandes, Fouad Alassani, Stanislav Péchev, Véronique Jubera, Mathieu Duttine, Jacinthe Gamon, Antoine Villesuzanne, Leïlou Loiseau-Foucher, Etienne Durand, Alexandre Fargues, Alain Demourgues|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04288
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2q0bps
title: CSD 2502775: Experimental Crystal Structure Determination

description: Related Article: Alizée Deslandes, Fouad Alassani, Stanislav Péchev, Véronique Jubera, Mathieu Duttine, Jacinthe Gamon, Antoine Villesuzanne, Leïlou Loiseau-Foucher, Etienne Durand, Alexandre Fargues, Alain Demourgues|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04288
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2q0brv
title: CSD 2502777: Experimental Crystal Structure Determination

description: Related Article: Alizée Deslandes, Fouad Alassani, Stanislav Péchev, Véronique Jubera, Mathieu Duttine, Jacinthe Gamon, Antoine Villesuzanne, Leïlou Loiseau-Foucher, Etienne Durand, Alexandre Fargues, Alain Demourgues|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04288
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2q0bsw
title: CSD 2502778: Experimental Crystal Structure Determination

description: Related Article: Alizée Deslandes, Fouad Alassani, Stanislav Péchev, Véronique Jubera, Mathieu Duttine, Jacinthe Gamon, Antoine Villesuzanne, Leïlou Loiseau-Foucher, Etienne Durand, Alexandre Fargues, Alain Demourgues|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04288
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.18433342
title: BANK TIZIMIDA FOIZ STAVKALARI SIYOSATI VA IQTISODIY FAOLLIKKA TA'SIRI

description: Mazkur maqolada bank tizimida foiz stavkalari siyosatining iqtisodiy faollikka ko‘rsatadigan ta’siri har tomonlama tahlil qilinadi. Foiz stavkalari pul-kredit siyosatining asosiy instrumentlaridan biri sifatida investitsiya jarayonlari, jamg‘arma hajmi, iste’mol xarajatlari hamda tadbirkorlik faolligini tartibga solishda muhim o‘rin egallaydi. Tadqiqotda foiz stavkalarining o‘zgarishi iqtisodiyotning real sektoriga qanday mexanizmlar orqali ta’sir ko‘rsatishi nazariy va amaliy jihatdan yoritilgan. Shuningdek, foiz stavkalari siyosatining makroiqtisodiy barqarorlikni ta’minlash, inflyatsion jarayonlarni nazorat qilish va iqtisodiy o‘sishni rag‘batlantirishdagi roli asoslab berilgan. Maqolada bank tizimi va real iqtisodiyot o‘rtasidagi o‘zaro bog‘liqlik ochib berilib, foiz stavkalarini samarali boshqarish iqtisodiy faollikni oshirishning muhim sharti ekanligi xulosalanadi.
DOI: 10.5281/zenodo.18433343
title: BANK TIZIMIDA FOIZ STAVKALARI SIYOSATI VA IQTISODIY FAOLLIKKA TA'SIRI

description: Mazkur maqolada bank tizimida foiz stavkalari siyosatining iqtisodiy faollikka ko‘rsatadigan ta’siri har tomonlama tahlil qilinadi. Foiz stavkalari pul-kredit siyosatining asosiy instrumentlaridan biri sifatida investitsiya jarayonlari, jamg‘arma hajmi, iste’mol xarajatlari hamda tadbirkorlik faolligini tartibga solishda muhim o‘rin egallaydi. Tadqiqotda foiz stavkalarining o‘zgarishi iqtisodiyotning real sektoriga qanday mexanizmlar orqali ta’sir ko‘rsatishi nazariy va amaliy jihatdan yoritilgan. Shuningdek, foiz stavkalari siyosatining makroiqtisodiy barqarorlikni ta’minlash, inflyatsion jarayonlarni nazorat qilish va iqtisodiy o‘sishni rag‘batlantirishdagi roli asoslab berilgan. Maqolada bank tizimi va real iqtisodiyot o‘rtasidagi o‘zaro bog‘liqlik ochib berilib, foiz stavkalarini samarali boshqarish iqtisodiy faollikni oshirishning muhim sharti ekanligi xulosalanadi.
DOI: 10.6084/m9.figshare.30467288
title: Intrinsic magnetic properties for SmFe<i><sub>12-x</sub> T<sub>x</sub></i> thin films via high-throughput experiments and machine learning techniques

description: 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  12-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 12−xTx)-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 (μ0Ms), anisotropy field (μ0Hs), and Curie temperature (Tc). High-throughput structural and magnetic characterizations, coupled with machine learning (ML) predictions, facilitated efficient data acquisition and analysis. Experimental results reaffirmed trends such as μ0Ms enhancement with Co and phase-stabilization capabilities of Ti and V. Novel insights into additives like Cr and Ta revealed potential Tc improvements. ML regression models (Random Forest and XGBoost) identified electronegativity as a key factor influencing μ0Ms. Predictive analyses successfully estimated μ0Ms trends and ThMn 12 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. A novel high-throughput evaluation method reveals key intrinsic properties of ThMn<sub>12</sub>-type magnets, enabling efficient discovery and optimization of high-performance permanent magnets via combinatorial experiments and machine learning
DOI: 10.6084/m9.figshare.30467288.v2
title: Intrinsic magnetic properties for SmFe<i><sub>12-x</sub> T<sub>x</sub></i> thin films via high-throughput experiments and machine learning techniques

description: 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  12-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 12−xTx)-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 (μ0Ms), anisotropy field (μ0Hs), and Curie temperature (Tc). High-throughput structural and magnetic characterizations, coupled with machine learning (ML) predictions, facilitated efficient data acquisition and analysis. Experimental results reaffirmed trends such as μ0Ms enhancement with Co and phase-stabilization capabilities of Ti and V. Novel insights into additives like Cr and Ta revealed potential Tc improvements. ML regression models (Random Forest and XGBoost) identified electronegativity as a key factor influencing μ0Ms. Predictive analyses successfully estimated μ0Ms trends and ThMn 12 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. A novel high-throughput evaluation method reveals key intrinsic properties of ThMn<sub>12</sub>-type magnets, enabling efficient discovery and optimization of high-performance permanent magnets via combinatorial experiments and machine learning
DOI: 10.6084/m9.figshare.30467288.v1
title: Intrinsic magnetic properties for SmFe 12−xTx thin films via high-throughput experiments and machine learning techniques

description: 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  12-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 12−xTx)-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 (μ0Ms), anisotropy field (μ0Hs), and Curie temperature (Tc). High-throughput structural and magnetic characterizations, coupled with machine learning (ML) predictions, facilitated efficient data acquisition and analysis. Experimental results reaffirmed trends such as μ0Ms enhancement with Co and phase-stabilization capabilities of Ti and V. Novel insights into additives like Cr and Ta revealed potential Tc improvements. ML regression models (Random Forest and XGBoost) identified electronegativity as a key factor influencing μ0Ms. Predictive analyses successfully estimated μ0Ms trends and ThMn 12 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. A novel high-throughput evaluation method reveals key intrinsic properties of ThMn<sub>12</sub>-type magnets, enabling efficient discovery and optimization of high-performance permanent magnets via combinatorial experiments and machine learning
DOI: 10.6084/m9.figshare.30196478
title: Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation

description: The orchestration software (OS) for controlling self-driving laboratories (SDLs) has been advanced significantly in recent years. We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. In this study, we demonstrate the realization of an SDL via NIMO by integrating AI into a legacy robotic system. As a proof of concept, we integrated an automated liquid handling system controlled by a Visual Basic (VB) program into the SDL through NIMO and performed parameter optimization of the dispensing process using Bayesian optimization, thereby enabling autonomous and automated experiments. NIMO facilitates AI integration through straightforward file exchanges, ensuring compatibility with robotic experimental systems programmed in non-Python languages such as VB and LabVIEW, as well as SDLs managed by other OS platforms. We anticipate that NIMO’s ability to support a broad spectrum of AI-driven autonomous experiments will significantly enhance the functionality and versatility of SDLs. NIMO enables AI-driven automation in self-driving labs by bridging diverse experimental systems, including those using non-Python platforms, greatly enhancing SDL accessibility and flexibility.
DOI: 10.6084/m9.figshare.30196478.v1
title: Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation

description: The orchestration software (OS) for controlling self-driving laboratories (SDLs) has been advanced significantly in recent years. We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. In this study, we demonstrate the realization of an SDL via NIMO by integrating AI into a legacy robotic system. As a proof of concept, we integrated an automated liquid handling system controlled by a Visual Basic (VB) program into the SDL through NIMO and performed parameter optimization of the dispensing process using Bayesian optimization, thereby enabling autonomous and automated experiments. NIMO facilitates AI integration through straightforward file exchanges, ensuring compatibility with robotic experimental systems programmed in non-Python languages such as VB and LabVIEW, as well as SDLs managed by other OS platforms. We anticipate that NIMO’s ability to support a broad spectrum of AI-driven autonomous experiments will significantly enhance the functionality and versatility of SDLs. NIMO enables AI-driven automation in self-driving labs by bridging diverse experimental systems, including those using non-Python platforms, greatly enhancing SDL accessibility and flexibility.
DOI: 10.6084/m9.figshare.30104840
title: NIMS polymer database PoLyInfo (III): modularizing ShEx schemas for descriptors and properties in PoLyInfoRDF

description: PoLyInfo is a polymer database of the National Institute for Materials Science (NIMS) of Japan. In our previous work, to make the PoLyInfo data machine-readable and further machine-understandable, we built PoLyInfoRDF to store these data in the standard Resource Description Framework (RDF) format and then defined its schema in the Shape Expressions (ShEx) language. When designing the schema, it is important to modularize the schema such that the common components are reusable. This is the objective of this study and is essential for efficiently defining schemas of the descriptors and properties, which constitute the core of PoLyInfo, a large collection of experimentally measured polymer characteristics. As an example of modularization, descriptors of the source-based name and molecular formula both include a string value, hence their schemas may well share (‘inherit’) the schema for string values, which would be defined once and subsequently reused throughout the entire set of schemas. Actually we noticed a considerable amount of common portions among schemas of descriptors and properties, and clarified a ‘schema hierarchy’ to reflect the above ‘inheritance’ relationships, separately from the ontological ‘concept hierarchy’. We then investigated the extent to which the adapted strategy was able to successfully define the PoLyInfoRDF schema. Under this schema hierarchy, inheritance mechanisms in ShEx played a significant role in sharing common portions effectively in a well-organized manner. We expect future developments based on our approach to contribute to the standardization of scientific data representation in RDF by providing a library of reusable schemas. We have developed a new method for modularizing scientific data schemas and　managing them hierarchically, and demonstrated it in PoLyInfo. This paves the way for data fusion in materials chemistry.
DOI: 10.6084/m9.figshare.30104840.v1
title: NIMS polymer database PoLyInfo (III): modularizing ShEx schemas for descriptors and properties in PoLyInfoRDF

description: PoLyInfo is a polymer database of the National Institute for Materials Science (NIMS) of Japan. In our previous work, to make the PoLyInfo data machine-readable and further machine-understandable, we built PoLyInfoRDF to store these data in the standard Resource Description Framework (RDF) format and then defined its schema in the Shape Expressions (ShEx) language. When designing the schema, it is important to modularize the schema such that the common components are reusable. This is the objective of this study and is essential for efficiently defining schemas of the descriptors and properties, which constitute the core of PoLyInfo, a large collection of experimentally measured polymer characteristics. As an example of modularization, descriptors of the source-based name and molecular formula both include a string value, hence their schemas may well share (‘inherit’) the schema for string values, which would be defined once and subsequently reused throughout the entire set of schemas. Actually we noticed a considerable amount of common portions among schemas of descriptors and properties, and clarified a ‘schema hierarchy’ to reflect the above ‘inheritance’ relationships, separately from the ontological ‘concept hierarchy’. We then investigated the extent to which the adapted strategy was able to successfully define the PoLyInfoRDF schema. Under this schema hierarchy, inheritance mechanisms in ShEx played a significant role in sharing common portions effectively in a well-organized manner. We expect future developments based on our approach to contribute to the standardization of scientific data representation in RDF by providing a library of reusable schemas. We have developed a new method for modularizing scientific data schemas and　managing them hierarchically, and demonstrated it in PoLyInfo. This paves the way for data fusion in materials chemistry.
DOI: 10.6084/m9.figshare.28554905
title: Linking structure and process in dendritic growth using persistent homology with energy analysis

description: We present a material analysis method that links structure and process in dendritic growth using explainable machine learning approaches. We employed persistent homology (PH) to quantitatively characterize the morphology of dendritic microstructures. By using interpretable machine learning with energy analysis, we established a robust relationship between structural features and Gibbs free energy. Through a detailed analysis of how Gibbs free energy evolves with morphological changes in dendrites, we uncovered specific conditions that influence the branching of dendritic structures. Moreover, energy gradient analysis based on morphological feature provides a deeper understanding of the branching mechanisms and offers a pathway to optimize thin-film growth processes. Integrating topology and free energy enables the optimization of a range of materials from fundamental research to practical applications. We introduce a novel method that bridges structure and process in dendritic growth by integrating persistent homology with energy analysis. Our framework quantitatively maps dendritic morphology to Gibbs free energy variations, revealing energy gradients that drive branching behavior. This approach provides new insights into crystal growth and offers a powerful, data-driven pathway for optimizing thin-film fabrication.
DOI: 10.6084/m9.figshare.28554905.v2
title: Linking structure and process in dendritic growth using persistent homology with energy analysis

description: We present a material analysis method that links structure and process in dendritic growth using explainable machine learning approaches. We employed persistent homology (PH) to quantitatively characterize the morphology of dendritic microstructures. By using interpretable machine learning with energy analysis, we established a robust relationship between structural features and Gibbs free energy. Through a detailed analysis of how Gibbs free energy evolves with morphological changes in dendrites, we uncovered specific conditions that influence the branching of dendritic structures. Moreover, energy gradient analysis based on morphological feature provides a deeper understanding of the branching mechanisms and offers a pathway to optimize thin-film growth processes. Integrating topology and free energy enables the optimization of a range of materials from fundamental research to practical applications. We introduce a novel method that bridges structure and process in dendritic growth by integrating persistent homology with energy analysis. Our framework quantitatively maps dendritic morphology to Gibbs free energy variations, revealing energy gradients that drive branching behavior. This approach provides new insights into crystal growth and offers a powerful, data-driven pathway for optimizing thin-film fabrication.