jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jörg Behler | Gábor Csányi | Lucas Foppa | Kisung Kang | Marcel F. Langer | Johannes T. Margraf | Akhil S. Nair | Thomas A. R. Purcell | Patrick Rinke | Matthias Scheffler | Alexandre Tkatchenko | Milica Todorović | Oliver T. Unke | Yi Yao
Source: ChemRxiv (preprint, 2024-11-13)
Field: Materials Science — Materials Chemistry

Workflows for Artificial Intelligence The efficiency and reliability of artificial-intelligence (AI)-driven physics, chemistry, biophysics, materials science and engineering depends on the acquisition of sufficient, high-quality data. Due to its all-electron, full potential treatment, and its scalability to larger systems without precision limitations, FHI-aims provides accurate ab initio data from a wide range of computer simulations, such as electronic structure calculations and molecular dynamics. To leverage the capabilities of AI models, workflows that seamlessly integrate AI tools with FHI-aims are essential. These workflows automate the acquisition of data and their use by AI. Thus, they facilitate the iterative data exchange between AI models and simulations, allowing FHI-aims to be used as a powerful AI-guided calculation engine. Also, interpretable AI models aid in analyzing the generated data. Furthermore, AI complements ab initio studies as it enables to perform simulations at larger time and length scales. In turn, also the AI models must incorporate the physics required for an accurate representation of the ab initio data. This contribution highlights workflows developed to integrate FHI-aims with AI and future challenges. Workflow ; Computer science ; Leverage (statistics) ; Scalability ; Artificial intelligence ; Ab initio ; Experimental data ; Machine learning ; Chemistry ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Sakhinana Sagar Srinivas | Geethan Sannidhi | Sreeja Gangasani | Chidaksh Ravuru | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-09-17)
Field: Materials Science — Surfaces, Coatings and Films

Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we introduce a fully automated, end-to-end pipeline that leverages recent advances in Generative AI. It is designed for analyzing and understanding the microstructures of semiconductor materials with effectiveness comparable to that of human experts, contributing to the pursuit of Artificial General Intelligence (AGI) in nanomaterial identification. Our approach utilizes Large MultiModal Models (LMMs) such as GPT-4V, alongside text-to-image models like DALLE-3. We integrate a GPT-4 guided Visual Question Answering (VQA) method to analyze nanomaterial images, generate synthetic nanomaterial images via DALLE-3, and employ in-context learning with few-shot prompting in GPT-4V for accurate nanomaterial identification. Our method surpasses traditional techniques by enhancing the precision of nanomaterial identification and optimizing the process for high-throughput screening. Frontier ; Electron micrographs ; Micrograph ; Generative grammar ; Semiconductor ; Artificial intelligence ; Computer science ; Materials science ; Nanotechnology ; Electron microscope ; Physics ; Scanning electron microscope ; Optics ; Political science ; Optoelectronics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Mathew J. Cherukara
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Surfaces, Coatings and Films

High Performance Computing and Artificial Intelligence Enabled Materials Characterization and Experimental Automation Characterization (materials science) ; Automation ; Computer science ; Artificial intelligence ; Materials science ; Nanotechnology ; Engineering ; Mechanical engineering Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Chao Huang | Xiao He | Chen Chen | Chunyan Chen | Yi Zhao | Shiyu Du | Yiming Zhang | Sha He | Riliang Gu
Source: arXiv (Cornell University) (preprint, 2024-11-13)
Field: Materials Science — Materials Chemistry

Polymetis:Large Language Modeling for Multiple Material Domains As the application of large language models in various fields continues to expand, materials science also ushers in opportunities for AI-driven innovation. The traditional way of relying on manual search for materials science-related information is now using artificial intelligence technology as an auxiliary tool to improve the efficiency of materials science research. To accelerate researchers' knowledge acquisition and intelligent decision-making support in materials science research, this paper proposes a large language model Polymetis model for a variety of materials fields, aiming to provide highly professional knowledge answers in the field of materials, covering energy materials, functional materials, alloy materials, physical chemistry, biology, and other material directions. The model uses a dataset of about 2 million material knowledge instructions, and in the process of building the dataset, we developed the Intelligent Extraction Large Model (IELM), which is specially used to extract and form structured knowledge from scientific texts, avoiding a large number of costs that need to be manually annotated, and improving efficiency. We inject this data into the GLM4-9B model for learning to enhance its inference capabilities in a variety of material domains. In addition, we have introduced enhanced prompt strategies to ensure that the answers to the model are more organized and comprehensive, providing efficient and comprehensive intelligent support for the diverse needs of materials science exploration, and promoting the development of material science. Computer science ; Natural language processing Machine Learning in Materials Science
Authors: Suman Saini | Jagdeep Walia | Jatinder Kaur
Source:  (review, 2024-05-28)
Field: Materials Science — Materials Chemistry

Nanomaterials in Diverse Fields: A Compact Review and Outlook (up to 2023) The aim of this article is to explore the fields of nanochemistry and nanocatalysis, focusing on their current progress, practical applications, and potential future impact. Nanomaterials have revolutionized nanocatalysis and opened up new opportunities across various sectors. The distinctive chemistry of nanomaterials enables the synthesis of intelligent materials with specific properties, leading to the creation of innovative products. This paradigm shift in nanoscience intersects with emerging fields like artificial intelligence, influencing catalyst design, drug delivery systems, and other domains. This article emphasizes the role of nanochemistry in accelerating the integration of nanotechnology into real-world scenarios. Nanomaterials ; Nanotechnology ; Materials science Advanced Nanomaterials in Catalysis ; Laser-Ablation Synthesis of Nanoparticles ; Nanoparticles: synthesis and applications
Authors: Deren Yang | Guodong Qian | Chang‐Zhi Li
Source: Information & Functional Materials (article, 2024-04-15)
Field: Chemistry — Physical and Theoretical Chemistry

<i>Information &amp; Functional Materials</i>: An interdisciplinary platform exploring the frontiers of functional materials, nanotechnologies, and informatics Information and functional materials are pivotal in powering scientific and technological advancements of modern societies. The continuous progresses and achievements made from functional materials and nanotechnologies have driven the development of smarter, faster, and more efficient informatics and optoelectronics. Concurrently, technological innovations such as green energy and artificial intelligence, etc., cannot be realized without the breakthrough of functional materials. These collective progresses are vital in resolving social and climate challenges. Given the photovoltaics as an example, they allow reducing reliance on fossil fuels and mitigating carbon footprints by employing advanced semiconductor materials for solar photon-to-electron conversion. Together with the advancements in high-capacity rechargeable batteries and high-performance catalysts for chemical fuel production, etc., they are paving the way for a sustainable energy economy to power up a more efficient and intelligent society in the near future. Information & Functional Materials (IFM) is a multi-disciplinary and open-access journal that covers the growing scientific interests in the field of material science, micro and nanotechnologies, and informatics, with the scopes of semiconductor materials, energy materials, ceramics, soft materials and composites, etc., as well as their micro and nanostructures for various applications in information technology. It is co-built by Zhejiang University and Wiley, and will serve as a top-level platform for researchers to publish and share their recent research achievements, outlooks, and perspectives in the multi-disciplinary field of information and functional materials for the good of society. Our outstanding editorial team consists of the internationally recognized scientists who span the diversity within the fields of material science, micro and nanotechnologies, and information technology. Prof. Deren Yang from Zhejiang University serves as the Editor-in-Chief, overseeing the entire publication process. Prof. Guodong Qian and Prof. Chang-Zhi Li serve as Associate Editors. IFM also has a top-class editorial board consisting of world-renowned scientists and talents. The editorial team is committed to actively serve and explore the high-caliber research results and progresses within the domain of information and functional materials. Now, we are excited to introduce this new journal to the community. Informatics ; Materials informatics ; Data science ; Nanotechnology ; Health informatics ; Engineering ethics ; Computer science ; Engineering ; Materials science ; Engineering informatics ; Medicine History and advancements in chemistry
Authors: Rajeev S. Assary | Yangang Liang | Vijayakumar Murugesan
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Chemistry — Electrochemistry

Towards Autonomous Electrochemistry: Data-Driven Computations and Robotic Experiments Autonomous electrochemistry has significant potential to accelerate the material discovery for lithium-ion and beyond lithium-based energy storage applications, including redox flow, lithium-metal, metal-sulfur, metal-air, or multivalent batteries. While the material discovery and system optimization are essential, the performance of these energy storage devices often suffers significantly due to undesired chemical reactions or contaminations with non-inert atmosphere (O 2 , H 2 O, CO 2 etc.). To this end, it is essential to utilize inert atmosphere conditions and settings for AI-driven autonomous electrochemistry experiments. We describe an electrolyte discovery framework to perform semi-autonomous experiments to identify optimal electrolyte materials that satisfy multiple desired properties such as redox potential, solubility, and viscosity for non-aqueous redox flow energy storage application. Our contributions 1-4 shows notable progress towards autonomous electrochemistry in terms of instrumentation and AI approach development, where inter institutional collaborations may be needed. Agarwal, G.; Doan, H. A.; Robertson, L. A.; Zhang, L.; Assary, R. S., Discovery of energy storage molecular materials using quantum chemistry-guided multiobjective bayesian optimization. Chemistry of Materials 2021, 33 (20), 8133-8144. Jain, A.; Shkrob, I. A.; Doan, H. A.; Adams, K.; Moore, J. S.; Assary, R. S., Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries. ACS Applied Materials &amp; Interfaces 2023, 15 (50), 58309-58319. Jain, A.; Shkrob, I. A.; Doan, H. A.; Robertson, L. A.; Zhang, L.; Assary, R. S., In silico discovery of a new class of anolyte redoxmers for non-aqueous redox flow batteries. Digital Discovery 2023, 2 (4), 1197-1208. Noh, J.; Doan, H. A.; Job, H.; Robertson, L. A.; Zhang, L.; Assary, R. S.; Mueller, K.; Murugesan, V.; Liang, Y., An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations. Nature Communications 2024, 15 (1), 2757. Computation ; Computer science ; Artificial intelligence ; Electrochemistry ; Human–computer interaction ; Chemistry ; Algorithm ; Electrode Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Hiroshi Morita
Source: Kobunshi (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Future Aspects of Data-Driven Materials Design Using both Simulation and AI 高分子材料におけるAIを使った材料設計に関して、シミュレーション、およびAIの視点から、未来像を俯瞰する。具体的には、AIおよびシミュレーションを使ったデータ創成、AIとシミュレーション技術の融合や連成技術に関して、5～10年後に必要となる技術に関して未来視点で説明し、今後のAI技術への期待を述べる。 Computer science ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced ceramic materials synthesis
Authors: Ji‐Xing Gao | Zheng-Hui Hu | Jia‐Le Song | P. X. Zhang | Xiang-Yu Shi | Shusheng Wang | Sujing Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular-Based Lead-Free Semiconductor Materials with High Phase Transition Temperature: [1-Methylimidazole][Sbbr4] Lead (geology) ; Semiconductor ; Phase transition ; Materials science ; Condensed matter physics ; Phase (matter) ; Engineering physics ; Optoelectronics ; Physics ; Geology ; Quantum mechanics Organic and Molecular Conductors Research ; Synthesis and characterization of novel inorganic/organometallic compounds ; Optical properties and cooling technologies in crystalline materials
Authors: Thi H. Ho | Quynh Anh T. Nguyen | Tran Bao Tien | Yoshiyuki Kawazoe | Viet Q. Bui
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Design of High-Curie Temperature Full-Heusler Alloys for Spintronic Applications Spintronics ; Curie temperature ; Condensed matter physics ; Materials science ; Heusler compound ; Curie ; Ferromagnetism ; Physics Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Phillip Gräfensteiner | Markus Osenberg | André Hilger | Nicole Bohn | Joachim R. Binder | Ingo Manke | Volker Schmidt | Matthias Neumann
Source: arXiv (Cornell University) (preprint, 2024-09-17)
Field: Materials Science — Materials Chemistry

Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes A stochastic 3D modeling approach for the nanoporous binder-conductive additive phase in hierarchically structured cathodes of lithium-ion batteries is presented. The binder-conductive additive phase of these electrodes consists of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. The model parameters are calibrated to 3D image data of cathodes in lithium-ion batteries acquired by focused ion beam scanning electron microscopy. Subsequently, model validation is performed by comparing model realizations with measured image data in terms of various morphological descriptors that are not used for model fitting. Finally, we use the stochastic 3D model for predictive simulations, where we generate virtual, yet realistic, image data of nanoporous binder-conductive additives with varying amounts of graphite particles. Based on these virtual nanostructures, we can investigate structure-property relationships. In particular, we quantitatively study the influence of graphite particles on effective transport properties in the nanoporous binder-conductive additive phase, which have a crucial impact on electrochemical processes in the cathode and thus on the performance of battery cells. Nanoporous ; Battery (electricity) ; Electrical conductor ; Materials science ; Cathode ; Composite material ; Nanotechnology ; Electrical engineering ; Engineering ; Physics ; Thermodynamics ; Power (physics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Recycling and Waste Management Techniques
Authors: Oxana Andriuc | Martin Siron | Kristin A. Persson
Source: ChemRxiv (preprint, 2024-02-05)
Field: Materials Science — Materials Chemistry

Systematic Computational Study of Oxide Adsorption Properties for Applications in Photocatalytic CO2 Reduction While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO2 reduction reaction: TiO2 (rutile and anatase), SrTiO3, NaTaO3, and CeO2. Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors. Photocatalysis ; Adsorption ; Reduction (mathematics) ; Oxide ; Materials science ; Chemical engineering ; Nanotechnology ; Computer science ; Chemistry ; Mathematics ; Engineering ; Metallurgy ; Physical chemistry ; Organic chemistry ; Catalysis Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Catalysis and Oxidation Reactions
Authors: murphy murat peksen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Optimising Thermomechanical Catalyst Stability in Reversible Solid Oxide Cell Feedstock Preparation: An Integrated Approach for Sustainability and Clean Hydrogen Energy Systems Raw material ; Sustainability ; Catalysis ; Hydrogen ; Oxide ; Hydrogen production ; Materials science ; Waste management ; Clean energy ; Process engineering ; Chemical engineering ; Environmental science ; Chemistry ; Engineering ; Metallurgy ; Organic chemistry ; Environmental engineering Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes
Authors: Min‐Ho Nam | Gyeongho Yun | Suhyeon Cho | Rian Kim | Seog‐Young Yoon | Seunghyup Lee
Source: Advanced Engineering Materials (article, 2024-06-22)
Field: Materials Science — Materials Chemistry

Synthesis of Spherical Hexagonal Boron Nitride via Precursor Morphology Control The growing demand for high‐performance semiconductors, driven by the advancement of emerging industries such as artificial intelligence (AI), necessitates the development of novel materials for thermal management. In this respect, hexagonal boron nitride (h‐BN) has emerged as a promising candidate due to its unique properties. However, challenges arise from its two‐dimensional layered structure, resulting in thermal transfer anisotropy and poor fluidity when mixed with polymers for thermal management. To address these challenges, researchers have attempted to fabricate h‐BN into spherical shapes. In this study, a two‐step synthesis method of spherical h‐BN (s‐BN) particles via control of the precursor morphology and a subsequent thermal reaction is proposed. Therefore, as‐fabricated s‐BN exhibits solid spherical shapes with a uniform size distribution, with a median particle size of 0.955 μm. These s‐BN particles, when integrated into epoxy resin, disperse homogeneously, forming efficient heat transfer networks that achieve a 138% improvement in thermal conductivity compared to h‐BN particles with similar diameters, even at lower viscosities. This can overcome the limitations found in the conventional particle shapes while preserving the advantages of h‐BN. Furthermore, it is anticipated that the s‐BN will be applied in thermal management systems, thereby accelerating advancements in electronic technology. Materials science ; Hexagonal boron nitride ; Morphology (biology) ; Hexagonal crystal system ; Boron nitride ; Chemical engineering ; Nanotechnology ; Boron ; Nitride ; Crystallography ; Organic chemistry Boron and Carbon Nanomaterials Research ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Giulia Bellini | Frank Girgsdies | Gregor Koch | Spencer J. Carey | Olaf Timpe | Gudrun Auffermann | Matthias Scheffler | Robert Schlögl | Lucas Foppa | Annette Trunschke
Source: ChemRxiv (preprint, 2024-04-14)
Field: Materials Science — Materials Chemistry

CO Oxidation Catalyzed by Perovskites: The Role of Crystallographic Distortions Highlighted by Systematic Experiments and AI The identification of key parameters that correlate with catalytic performance through a combination of experiments and model calculations can accelerate the development of improved catalysts and reveal the relevant underlying processes. However, the analysis of correlations in heterogeneous catalysis is often hindered by inconsistent data. Besides, nontrivial, yet unknown relationships may be important, and the intricacy of the various processes may be significant. Here, we address these challenges for perovskite-catalyzed CO oxidation by linking systematic experiments and artificial intelligence (AI). For this purpose, 14 AMn(1-x)CuxO3 phase-pure perovskites with A = Pr, La were synthesized, characterized, and tested according to rigorous experiments. To the so-generated consistent dataset, we applied the symbolic-regression SISSO approach and identified a descriptor for CO consumption rates as an analytical expression containing the bulk and surface A content, the copper surface content, and the deviation (D) of the normalized lattice constants from the cubic root of the normalized cell volume. Crucially, D reflects a crystallographic distortion that depends on the element at the A site. Thus, in addition to the relative abundance of redox-active species on the surface, the species at the A-sites also influence the performance by modulating the properties of the material and the surface. Perovskite (structure) ; Catalysis ; Redox ; Distortion (music) ; Lattice (music) ; Copper ; Lattice constant ; Chemistry ; Phase (matter) ; Surface (topology) ; Materials science ; Chemical physics ; Crystallography ; Thermodynamics ; Inorganic chemistry ; Mathematics ; Physics ; Metallurgy ; Geometry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Condensed Matter Physics