jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wen Liu | Ning Xu | Zheng Li | Meiliang Ma | Xiaojuan Hu | Zhongkang Han | Ying Jiang | Wentao Yuan | Hangsheng Yang | Sergey V. Levchenko | Yong Wang
Source: Physical review. B./Physical review. B (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Advancing the understanding and prediction accuracy of molecular adsorption energy with artificial intelligence Molecular adsorption plays a key role in heterogeneous catalysis, with the $d$-band center theory being extensively utilized for qualitative analysis of adsorption behavior of molecules on transition-metal surfaces. However, adsorption energies predicted by $d$-band center theory can have large errors, especially when a wide range of metals is considered. The physical mechanism responsible for the deviation has been unclear, posing significant challenges of the precise design of catalysts. Here, we have integrated density-functional theory calculations with artificial intelligence approaches to develop a unifying model that quantitatively evaluates the adsorption energy of water on various transition-metal surfaces. Data mining revealed that the large deviations between the $d$-band center theory predictions and first-principles calculations primarily stem from surface relaxation effects. Additionally, we discovered that the synergistic effects of electron donation and back donation, which are mediated by the $d$ and $p$ orbitals in metals, also play a role in these deviations, although their impact is less significant compared to that of the surface relaxation. These findings enhance our understanding of molecular adsorption behavior and are poised to influence research fields such as surface science and heterogeneous catalysis. Artificial intelligence ; Energy (signal processing) ; Computer science ; Machine learning ; Mathematics ; Statistics Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques
Authors: Razieh Dashti | Alireza Jahandoost | Sahand Fardadidokht | Seyyed Abed Hosseini | Omid Mojarad | Armin Torabi Golsefid | Jane E. Gross | Ali Samadi
Source: Biomedical Materials & Devices (article, 2025-10-21)
Field: Materials Science — Biomaterials

A Review of Enhancing HIV/AIDS Treatment with Artificial Intelligence: Optimizing Drug Loading and Release Kinetics in Polymeric Nanoparticle Systems Nanocarriers ; Drug delivery ; Drug ; Context (archaeology) ; Human immunodeficiency virus (HIV) ; Precision medicine Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Bobo Zou | Kun‐Yu Liu | Yushan Yan | Xinhe Liu | Hongli Long | Man Li | Haobo Zhang | Kaixi You | Chen Mi | Xinyan Liu
Source: Advanced Materials (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Decoding Lithium Metal Battery Degradation with Symmetric‐Cell Artificial Intelligence Diagnostics (SAID) While the underlying crosstalk effect between cathode and anode complicates the understanding of lithium metal degradation utilizing asymmetric (full)-cell data, employing a symmetric cell configuration enables isolation of contributions from specific electrodes. In this study, a symmetric-cell artificial intelligence diagnostics (SAID) is devised to decipher lithium metal anode degradation. Leveraging easily accessible, early-cycle lithium | lithium symmetric-cell data, SAID is demonstrated to accurately predict the elbow points (indicators of polarization acceleration) with a test mean absolute percentage error of 13.3%. More importantly, SAID reveals the persistent role of an initial-nucleation-related fingerprint in determining long-term cell polarization, which is validated through experiments and extended to full cells across different electrolytes. This approach, therefore, not only offers valuable insights into battery design but also exhibits great potential in uncovering hidden chemical correlations and advancing the field of energy storage in general. Anode ; Lithium metal ; Cathode ; Polarization (electrochemistry) ; Battery (electricity) ; Decoding methods ; Degradation (telecommunications) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Khalid Bouiti | Youssra El Hamdouni | Najoua Labjar | Houda Labjar | Souad Najoua Lagmiri | Hamid Nasrellah | Souad El Hajjaji
Source: Journal of Materials Engineering and Performance (review, 2025-03-31)
Field: Materials Science — Materials Chemistry

Materials Engineering and Artificial Intelligence Innovations: A Comprehensive Review Materials science ; Engineering ethics ; Systems engineering ; Construction engineering ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced materials and composites
Authors: Takahiro Misawa | Ai Koizumi | Ryo Tamura | Kazuyoshi Yoshimi
Source: Science and Technology of Advanced Materials Methods (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Exploring utilization of generative AI for research and education in data-driven materials science 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. Based on the outcomes of the hackathon (AIMHack2024), this paper highlights novel strategies for AI-assisted software use, building AI tutors, and GUI development. These results demonstrate the transformative potential of generative AI for advancing research and education in data-driven materials science. Generative grammar ; Data science ; Engineering ethics ; Computer science ; Engineering ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Austin D. Sendek | Venkatasubramanian Viswanathan
Source: The Electrochemical Society Interface (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Electrolyte Design: Going Beyond the Molecular Paradigm Electrolytes are fundamental to the function of batteries. They transport ions between the electrodes, while remaining inert in the harsh electrochemical environment the electrodes create. Designing electrolytes is a challenging, multidimensional problem that is unique from typical materials discovery or molecular design efforts: for one, a candidate electrolyte must satisfy tens of properties simultaneously. Second, the design space is immense. Electrolytes are chemical mixtures consisting of organic molecules and inorganic salt; thus, designing an electrolyte formulation for Li-ion batteries involves the combinatorial challenge of selecting a small number of organic molecules and a lithium-containing inorganic salt from a seemingly endless number of options, while accurately predicting all the key properties for each candidate formulation. Electrolyte ; Nanotechnology ; Cognitive science ; Computer science ; Materials science ; Chemistry ; Psychology ; Electrode Machine Learning in Materials Science
Authors: Qiuchen Yu | Mengjiao Zhao | Q. Han | Yu Chen | Zi‐Jiang Yang | Shasha Gao | Sheng Huang
Source: Sensors and Actuators A Physical (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

A generative artificial intelligence model for efficient gas sensitivity prediction in materials without parameters from first principle calculation Sensitivity (control systems) ; Generative grammar ; Generative model ; Artificial intelligence ; Computer science ; Engineering ; Electronic engineering Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Nuclear Physics and Applications
Authors: Kyle D. Crans | Zhaohong Sun | Ariel A. Nehoray | Richard L. Brutchey
Source: Inorganic Chemistry (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Data-Driven Mapping of the Cesium Cadmium Bromide Phase Space Utilizing a Soft-Chemistry Approach Soft-chemistry techniques provide a versatile approach to synthesizing inorganic materials under mild conditions, enabling access to compositions and structures that are challenging to achieve through traditional thermodynamically driven solid-state methods. However, these solution-based routes often result in phase competition, requiring precise control over reaction conditions to achieve selective product formation. While one-variable-at-a-time (OVAT) approaches have traditionally been used for phase selection, data-driven strategies are emerging as more efficient methods for navigating complex synthetic spaces. Ternary metal halides, such as cesium cadmium bromides (Cs-Cd-Br), are of growing interest due to their potential in wide and ultrawide band gap applications. Unlike the well-studied cesium lead halide phases, the compositional diversity and solution-based synthesis of ternary Cs-Cd-Br phases remain largely unexplored. This study systematically investigates the synthetic phase space of the Cs-Cd-Br system by constructing a data-driven phase map. Using a common set of precursors and a standardized experimental procedure, we successfully synthesize all four known Cs-Cd-Br phases─CsCdBr<sub>3</sub>, Cs<sub>2</sub>CdBr<sub>4</sub>, Cs<sub>3</sub>CdBr<sub>5</sub>, and Cs<sub>7</sub>Cd<sub>3</sub>Br<sub>13</sub>─each exhibiting distinct structures, morphologies, and optical properties. Our findings highlight the potential of soft-chemistry methods for expanding the library of ternary metal halides and provide key insights into the thermodynamic and kinetic factors governing phase formation. Chemistry ; Caesium ; Soft chemistry ; Cadmium ; Bromide ; Phase (matter) ; Inorganic chemistry ; Organic chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Fuchun Ge | Pavlo O. Dral
Source: ChemRxiv (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space Molecular dynamics ; Transferability ; Scalability ; Artificial neural network ; Ab initio ; Boundary (topology) ; Space (punctuation) ; Scope (computer science) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Xiangfei Ye | Haoxiang Lan | Liangshun Zhang | Jiaping Lin | Yisheng Xu | Lei Du | Xiaohui Tian
Source: Chemistry of Materials (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Discovery of Trade-Off Performance Frontiers of Cross-Linking Polymers through Artificial Intelligence-Driven Multiobjective Bayesian Optimization The intrinsic trade-offs among various properties have always hindered the pursuit of materials with excellent overall performance. Traditional trial-and-error methods have revealed inherent limitations in addressing material optimization problems under high-dimensional parameter spaces and complex property interdependencies. Especially, it is a longstanding challenge to simultaneously improve Young’s modulus, strength, and toughness of cross-linking epoxy resins as structural matrices in high-performance materials. In this contribution, by leveraging the multiobjective Bayesian optimization (MOBO) approach, we overcome the challenge through an AI-driven investigation of the optimization of curing conditions, a critical yet often overlooked factor in epoxy resins, to simultaneously achieve multiple outstanding properties. In particular, as a multiobjective-oriented acquisition function, the expected hypervolume improvement is applied to evaluate the properties of epoxy resins under unexplored regions of curing conditions. It is demonstrated that the proposed method can effectively optimize the multivariate curing conditions of epoxy, including comprehensive improvement of all properties or enhancement of a specific property. Importantly, the feature analysis of data-driven surrogate models highlights the critical role of curing conditions at distinct stages in determining the ultimate performance of epoxy resins, originating from a change in microscopic structures. The multiobjective Bayesian optimization method proposed in this work provides a promising AI-based paradigm for accelerating the high-performance materials design in high-dimensional parameter space and understanding the processing–structure–property relationships from data-informed perspectives. Bayesian optimization ; Bayesian probability ; Artificial intelligence ; Polymer ; Computer science ; Multi-objective optimization ; Biochemical engineering ; Nanotechnology ; Materials science ; Machine learning ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Polymer crystallization and properties
Authors: Aslam Hossain | Zhengyou Li | P.D. Kuznetsova | И. А. Панкин | А. В. Солдатов | Jun Ho Shim
Source: Chemical Engineering Journal (article, 2025-11-09)
Field: Chemistry — Inorganic Chemistry

Converging pathways of microfluidic metal-organic framework and artificial intelligence: A novel research frontier The synergistic integration of microfluidics, metal-organic frameworks (MOF), and artificial intelligence (AI) represents a groundbreaking approach to redefine materials science, chemical engineering, and smart system design. This convergence addresses urgent challenges in sustainable chemistry, precision manufacturing, and adaptive sensing by considering the unique strengths of each domain: microfluidics enables unprecedented control over reactive environments, MOF offer tunable architectures for catalysis and separation, and AI unlocks data-driven optimization of complex processes. In this review, we systematically examine how these fields intersect to revolutionize MOF synthesis, characterization, and applications. We highlight breakthroughs in microfluidic platforms that enable real-time monitoring of MOF nucleation, growth, and phase transitions, alongside innovative strategies for creating tailored synthesis environments with spatiotemporal precision. The ability to scale MOF production while maintaining uniformity—a longstanding bottleneck—is critically assessed, emphasizing microfluidics role in advancing industrial throughput. Furthermore, we explore how AI integration transforms these systems from passive reactors to intelligent, self-optimizing platforms capable of accelerating MOF discovery, predicting structure-property relationships, and dynamically controlling chemical processes with feedback loops. This review serves as a catalyst for interdisciplinary innovation, bridging theory, experimentation, and machine intelligence to unlock the full potential of MOF-based technologies in the next decade. • Integration of microfluidics, MOFs, and AI revolutionizes smart materials design. • Real-time monitoring of MOF growth achieved via advanced microfluidic techniques. • Microfluidics significantly enhances industrial scalability of uniform MOFs. • AI transforms passive reactors into intelligent, self-optimizing MOF platforms. • New frontiers emerge by combining microfluidics precision with AI-driven MOFs. Microfluidics ; Scalability ; System integration ; Automation ; Scale (ratio) Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Magnesium Oxide Properties and Applications
Authors: Ronald Márquez | Blaise L. Tardy | Roberto Aguado | Giovana Signori-Iamin | Esther Argelagós | Marc Delgado‐Aguilar
Source: Journal of Chemical Education (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Artificial Intelligence (AI)-Augmented “Living” Meta-Analyses toward Critical Thinking Engagement in Chemical Education and Research: A Case Study of Nanocellulose-Stabilized Pickering Emulsions As research on sustainable and advanced materials accelerates (e.g., cellulose-based composites and functionalized nanomaterials), research output has expanded rapidly, increasing complexity and making it challenging for industrial and engineering chemistry researchers to maintain comprehensive, up-to-date data compilation and analysis. Therefore, traditional meta-analyses, even when attempting to adhere to systematic methodologies such as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), face challenges with manual data curation, risking oversights and impacting reproducibility in the face of such volume. Updated meta-analysis methodologies are necessary to critically assess advances and new technologies for the upscaling processes and innovation in the advanced materials field. To address this, we propose a novel framework for creating “living” meta-analyses augmented by artificial intelligence (AI). We explore these concepts via a tutorial case study on nanocellulose-stabilized Pickering emulsions, illustrating how the integration of AI-based extraction with bibliometric mapping reveals patterns, identifies research gaps, and enables the deployment of informed decisions for future research and accelerated product development in academia and industry. Integrating Large Language Models (LLMs), such as ChatGPT and Gemini, with bibliometric platforms (e.g., ACS CAS SciFinder, Scopus, Dimensions) and VOSviewer, allowed one to systematically curate and synthesize data from over 50 publications. The resulting interactive platform reveals complex relationships among nanocellulose properties (e.g., type, modification, concentration), processing conditions, and emulsion characteristics (e.g., droplet size, stability). The database and software are available at 10.5281/zenodo.15808694. We critically discuss the current limitations of LLMs in performing meta-analyses in the chemical engineering and advanced materials fields and emphasize the role of “human-in-the-loop” expertise in interpretation Pickering emulsion ; Critical thinking ; Chemistry education ; Higher education ; Science education Machine Learning in Materials Science ; Various Chemistry Research Topics ; Innovative Teaching and Learning Methods
Authors: Lan Na Duo | Yuze Hao | Jinlu He
Source: ChemRxiv (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Autonomous Materials Synthesis Laboratories: Integrating Artificial Intelligence with Advanced Robotics for Accelerated Discovery Robotics ; Transformative learning ; Applications of artificial intelligence ; Representation (politics) ; Robot ; Pipeline (software) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Erlita Mastan | Zhong Zeng | Shiping Zhu
Source: Polymer (article, 2025-06-24)
Field: Chemistry — Inorganic Chemistry

AI-assisted derivation of random chain scission degradation theory for linear polymers having an arbitrary molecular weight distribution Chain scission ; Polymer ; Molar mass distribution ; Chain (unit) ; Bond cleavage ; Linear polymer ; Degradation (telecommunications) ; Materials science ; Polymer science ; Distribution (mathematics) ; Polymer chemistry ; Mathematics ; Composite material ; Physics ; Chemistry ; Mathematical analysis ; Computer science ; Quantum mechanics ; Organic chemistry Radioactive element chemistry and processing ; Thermal and Kinetic Analysis ; Chemical Synthesis and Characterization
Authors: Qingkun Zhao | Zhenghao Zhang | Huajian Gao | Haofei Zhou
Source: Advanced Functional Materials (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

AI‐Powered Visualization of Invisible Mechano‐Information: Stress, Defects, and Beyond Abstract Despite significant advances in high‐resolution structural characterization, visualizing complex mechano‐information—such as local stress fields induced by lattice distortions or elemental distributions—remains a formidable challenge. This “invisible” information, inaccessible through current experimental techniques, hinders a comprehensive understanding of material properties and behaviors across multiple fields. Artificial intelligence (AI) has emerged as a transformative tool, bridging material properties with their structures and enabling the visualization of previously hidden mechano‐information. This review explores AI‐driven approaches to reveal mechano‐information, including local stress distributions across scales (from macroscale to nanoscale) and the distribution of ultra‐light elements at lattice defects, along with their effects on local stress fields. Additionally, recent AI‐assisted methods for visualizing structural, chemical, and functional information are highlighted, and current challenges and future opportunities in this rapidly evolving field are discussed. Materials science ; Visualization ; Stress (linguistics) ; Nanotechnology ; Human–computer interaction ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Force Microscopy Techniques and Applications
Authors: Ali Asghar | Bo Zhang | Vianna F. Jafari | Tanja Junkers
Source: Reaction Chemistry & Engineering (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

A data-driven approach to the generalization of free radical polymerization kinetic models <i>via</i> automated flow chemistry Bayesian optimization drives kinetic model evolution in chain transfer polymerization using inline NMR and online GPC in a flow setup by iterative prediction of new experiments to add to a database. Polymerization ; Kinetic energy ; Flow (mathematics) ; Chain transfer ; Chain (unit) ; Radical polymerization ; Generalization Machine Learning in Materials Science
Authors: Maliheh Shaban Tameh | Veaceslav Coropceanu | Thomas A. R. Purcell
Source: Chemistry of Materials (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Guided Search for Chalcogenide Hybrid Inorganic/Organic Polymers Comonomers Chalcogenide hybrid inorganic/organic polymers (CHIPs) have the potential to revolutionize infrared (IR) optics and create sustainable and recyclable devices. CHIPs combine elemental sulfur with organic comonomers via inverse vulcanization to create a high-sulfur-content polymer, with optical properties that rival state-of-the-art inorganic solids with the processability and recyclability of plastic materials. However, the optimal comonomer for these applications remains unknown. This work presents a gradient-boosted tree model that determines which comonomers merit further consideration as high-performing CHIPs materials. After training models on previously calculated IR absorption data, we apply them to a larger set of 960,966 molecules from the GDB data set and validate the predictions for both highly transparent molecules and a set of 1000 randomly selected molecules. We then look at the 199,511 molecule subset of the expanded search space with chemical moieties eligible for inverse vulcanization and found 2942 possible comonomers predicted to have better optical properties than the state-of-the-art comonomer stillene. Finally, we calculate the optical properties of all 2942 comonomers in the gas phase and in a configuration to approximate the polymer films to find a set of target comonomers. Comonomer ; Chalcogenide ; Vulcanization ; Polymer ; Molecule ; Monomer ; Figure of merit Phase-change materials and chalcogenides ; Conducting polymers and applications ; Polyoxometalates: Synthesis and Applications