jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gyoung S. Na | Chanyoung Park
Source:  (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL. Granularity ; Representation (politics) ; Physics ; Computer science ; Electron ; Statistical physics ; Nanotechnology ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface Chemistry and Catalysis
Authors: Alberto Esteban Reyes Peralta | Francisco José López Cortés | Rigoberto Cerino Jiménez | David Pinto | Enrique Pérez Gutiérrez
Source: Computación y Sistemas (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Toward an Offline Generative AI Dialogue Model to Support the Automation of a Materials Synthesis Laboratory Materials synthesis laboratories face challenges due to repetitive and error-prone tasks, necessitating innovative solutions. This review explores the potential of large language models (LLMs) to develop an offline dialogue model based on generative artificial intelligence, designed to automate processes in materials synthesis laboratories through conversational interaction with robotic systems and human personnel. Through a systematic literature review from 2019 to 2025, 13 studies are analyzed across three categories: generative AI and language models, automation and robotics in laboratories, and automation and evaluation frameworks. The findings highlight improvements in efficiency (20–90%) and cost reduction (up to 84%) in robotic and scientific tasks but also point out limitations in accuracy (60–80%) and reproducibility (65%). Offline operation requires optimization for local hardware and data security. A hybrid model with human feedback is recommended to maximize impact in materials synthesis, addressing both technical and ethical challenges. Automation ; Robotics ; Generative grammar ; Point (geometry) ; Generative model ; Robot Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; AI in Service Interactions
Authors: Wei Wang (17594) | Yu Chen (29959) | Dao-Hong Gong (21723597) | Run-Jiang Song (21723600) | Lang Pan (5145221) | Heng-Zhi Wang (7463336) | Lian-Yang Bai (6555683) | W.M.W.W. Kandegama (21723603) | Pradeepa C. Gunathilake Bandaranayake (21723606) | Lei Lian (3904636) | Ge-Fei Hao (364656)
Source: Figshare (article, 2025-07-16)
Field: Chemistry — Organic Chemistry

AI-Empowered MolecularEditing Opens a New Horizonin Pesticide Discovery Rapid evolution of digital technologies has enabled vital tools in pesticide discovery, which are crucial for agricultural productivity and food security. Therein, molecular editors have emerged as basic and critical tools in this field. However, existing molecular editors lack advanced features to optimize computer-aided pesticide discovery. We propose three enhancements: (1) generating practical synthesis strategies for novel candidates; (2) contributing to the generation of 2D structural molecules with AI technology; and (3) finding possible targetable pockets and sites based on 2D structural molecules. We believe that our viewpoints can contribute to further advancement of AI-driven molecular editing, enabling it to better facilitate pesticide discovery. Viewpoints ; Pesticide ; Productivity ; Emerging technologies ; Agriculture ; Horizon Click Chemistry and Applications ; Insect-Plant Interactions and Control ; Chemical Synthesis and Analysis
Authors: Junjie Liu (193511) | Yudong Yang (185767) | Zhengyang Bin (1411951) | Jingsong You (1397368)
Source: Figshare (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Diversity-OrientedSynthesis of Diaza-Polycyclic AromaticHydrocarbons Coupled with Virtual Screening for OLED Applications The development of organic light-emitting diode (OLED) materials requires precisely engineered molecular architectures with tailored optoelectronic properties. Here, we present a strategy that synergistically integrates diversity-oriented synthesis (DOS) with virtual screening to systematically explore the chemical space of diaza-polycyclic aromatic hydrocarbons (diaza-PAHs) for OLED applications. DOS, originally developed for drug discovery, serves as a powerful tool to generate structurally diverse molecular libraries, granting access to previously unexplored diaza-PAHs with potential OLED functionality. Complementing this synthetic approach, virtual screening enables the rapid identification of optimal candidates from an extensive library of computationally generated structures, thereby streamlining the material discovery process. In this work, we introduce a rhodium-catalyzed method that combines sequential C–H activation and annulation, offering a modular synthetic protocol for diaza-PAH scaffolds with tunable electronic properties. By computationally screening 1,061 core structures, we identified two lead frameworks2,6-diazaanthracene and 2,8-dipyridylthiopheneas potential prototypes for OLED applications. Guided by these computational insights, we synthesized four diaza-PAH derivatives, which exhibited exceptional performance as electron-transporting and host materials in high-efficiency phosphorescent OLEDs. This integrated experimental and computational approach not only accelerates the identification of functional materials but also significantly reduces experimental time and resource consumption. This paradigm shift underscores the potential of merging synthetic diversity with data-driven molecular discovery to advance optoelectronic materials research. OLED ; Virtual screening ; Modular design ; Chemical space ; Identification (biology) ; Diode Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials
Authors: Partridge, Matthew | Pearman-Kanza, Samantha | Frey, Jeremy
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

How we are stewarding the Physical Chemistry Properties Data Collection to be the best data collection it can be &lt;p&gt;This poster was presented at the Careers and Skills for Data-driven Research (CaSDaR) Townhall Launch event on the 18th September 2025.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The Physical Chemistry Properties Data Collection (PChemProp), hosted on the Physical Sciences Data Infrastructure (PSDI), is an evolving data collection designed to curate and share high-quality, machine-readable physical chemistry data. This poster presents the current scope of PChemProp, including datasets covering melting points, solubilities, and other key properties, aggregated from diverse sources. It outlines our systematic auditing processes&mdash;focusing on metadata completeness, unit standardisation, provenance tracking, and duplicate reconciliation&mdash;to ensure the integrity and FAIRness of every entry. We also highlight new developments, including the transition to a JSON-based MongoDB backend, integration of AI-assisted ingestion workflows, and adoption of tools like ProperSea and M-layer to improve semantic structure and unit handling. Looking ahead, we are expanding community engagement and interoperability through schema alignment with OPTIMADE and future web interfaces. This poster showcases how we are stewarding PChemProp to be a robust, sustainable, and valuable resource for researchers and data-driven modelling.&lt;/p&gt; Metadata ; Interoperability ; Data collection ; Audit ; Scope (computer science) ; Schema (genetic algorithms) ; Documentation ; Resource (disambiguation) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Xue Fang (648169) | Ulzhalgas Karatayeva (20617671) | John D. Worth (22558889) | Merve Gumussoy Girgin (22558892) | Safa Ali Al Siyabi (22558895) | Dauren Mukhanov (22558898) | Ella M. Gale (22558901) | Charl F. J. Faul (7404977) | Natalie Fey (407870)
Source: Figshare (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

A Solvent SelectionFramework for Porous Organic Polymers Selecting suitable solvents to control the morphology and properties of novel functional materials remains a significant challenge, especially when there is limited or no prior knowledge of the material and its solubility. In this work, we present a solvent selection toolkit for functional porous organic polymers. We have developed the <i>MLoc</i> algorithm for the fast determination of Hansen solubility parameters (HSPs) for novel materials. This approach requires ultraviolet and visible (UV/vis) absorbance data, measured for a number of candidate solvents using a standard laboratory setup. Based on these measurements, <i>MLoc</i> determines the HSPs for novel porous organic materials using a centroid-location algorithm based on Hansen distance. The results of this algorithm can guide the fine-tuning of both morphology and carbon-capture performance of target polymers, which we illustrate in a case study. In this example, performing the polymer synthesis in solvents with HSPs most similar to the porous material has led to CO<sub>2</sub> uptake improved by 220% compared to a reported analogue (from 2.16 to 6.95 wt %). Using <i>MLoc</i>, we have also compiled a HSP database for 17 porous organic polymers, enhanced with data for over 80 reactions, sampling different conditions, which we present as a resource for future data-driven research in this area. Porosity ; Solvent ; Polymer ; Porous medium ; Absorbance ; Organic solvent ; Selection (genetic algorithm) Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis ; Oxidative Organic Chemistry Reactions
Authors: Xue Fang | Ulzhalgas Karatayeva | John Worth | Merve Gumussoy Girgin | Safa Ali Al Siyabi | Dauren Mukhanov | Ella Gale | Charl F. J. Faul | Natalie Fey
Source: Journal of Chemical Information and Modeling (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

A Solvent Selection Framework for Porous Organic Polymers Selecting suitable solvents to control the morphology and properties of novel functional materials remains a significant challenge, especially when there is limited or no prior knowledge of the material and its solubility. In this work, we present a solvent selection toolkit for functional porous organic polymers. We have developed the <i>MLoc</i> algorithm for the fast determination of Hansen solubility parameters (HSPs) for novel materials. This approach requires ultraviolet and visible (UV/vis) absorbance data, measured for a number of candidate solvents using a standard laboratory setup. Based on these measurements, <i>MLoc</i> determines the HSPs for novel porous organic materials using a centroid-location algorithm based on Hansen distance. The results of this algorithm can guide the fine-tuning of both morphology and carbon-capture performance of target polymers, which we illustrate in a case study. In this example, performing the polymer synthesis in solvents with HSPs most similar to the porous material has led to CO<sub>2</sub> uptake improved by 220% compared to a reported analogue (from 2.16 to 6.95 wt %). Using <i>MLoc</i>, we have also compiled a HSP database for 17 porous organic polymers, enhanced with data for over 80 reactions, sampling different conditions, which we present as a resource for future data-driven research in this area. Porosity ; Solvent ; Polymer ; Absorbance ; Porous medium ; Organic solvent ; Solubility Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis ; Oxidative Organic Chemistry Reactions
Authors: Jiaqian Wang (5594630) | Xiaojuan Hu (4978064) | Ying Jiang (5910) | Wentao Yuan (1423672) | Hangsheng Yang (3203370) | Zhong-Kang Han (6270353) | Yong Wang (12837)
Source: Figshare (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Identifying Key Factors Influencing Advanced HydrogenEvolution Reaction Catalysts Data-driven approaches are increasingly vital in the field of catalyst design, significantly accelerating catalyst development. However, the mechanisms and rules underlying these approaches often lack transparency, potentially leading to unreliable outcomes due to an insufficient understanding of the specific processes involved. Here, we developed a method that combines analytical learning with constrained data mining techniques to not only identify high-performance materials but also elucidate the optimization pathways for enhancing their performance. Using this method, we screened over a thousand potential catalysts, identifying top-performing single-atom catalysts for the hydrogen evolution reaction and mapping out optimization pathways to progressively improve performance. Notably, our findings suggest that decisions aimed at enhancing material performance, when based on tuning key factors identified from entire data sets, can be misleading. Instead, a more effective strategy is to make decisions through a systematic, step-by-step analysis of subgroup data sets, specifically focusing on subsets of high-performance materials that exhibit common characteristics. This approach enhances both the development of knowledge from data and the trustworthiness of the results, offering new insights for advancing data-driven approaches in the rational design of material properties. Trustworthiness ; Key (lock) ; Field (mathematics) ; Catalysis ; Rational design Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Minhui Xu | Jinfeng Zhao | Hanyu Wang | Han Xue-bing
Source: Journal of Agricultural and Food Chemistry (review, 2025-09-18)
Field: Materials Science — Biomaterials

Modified Cellulose for Intestinal Health Rising global incidence of bowel disorders is driving an urgent demand for novel therapeutic strategies capable of overcoming the limitations of conventional treatments, particularly their substantial side effects, high costs, and potential enteric neurotoxicity. Cellulose, the most abundant natural polymer, serves as a promising material due to its biocompatibility, sustainability, and capacity for functional modification. This review comprehensively explores recent advances in the use of modified cellulose for managing intestinal diseases. We systematically analyze the relationship between the structure and function among various cellulose derivatives and describe the physical, chemical, and biological methods used to improve their therapeutic potential. A major emphasis is placed on the diverse therapeutic mechanisms of modified cellulose. These include its role as an engineered carrier for targeted delivery of drugs and probiotics, its ability to regulate gut microbiota and strengthen the intestinal barrier, and its functions in immune modulation and toxin adsorption. We also address remaining challenges and future directions, highlighting the potential of artificial intelligence to advance rational material design. This review aims to provide a foundation for developing effective, safe, and clinically viable cellulose-based therapies for intestinal health. Cellulose ; Inflammatory Bowel Diseases ; Bacterial cellulose ; Function (biology) ; Rational design ; Therapeutic approach Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls
Authors: Meilin Liu | Xueyu Hu | Ke Liao | Yucun Zhou | Haoyu Li | Zheyu Luo | Nai Shi | Yong Ding | Ning Wang | Weilin Zhang | Doyeub Kim | Chanho Kim | Yoojin Ahn | Nikhil Govindarajan | Zhijun Liu
Source: Research Square (preprint, 2025-03-21)
Field: Materials Science — Materials Chemistry

Mechanistically Interpretable AI for Accelerated Energy Materials Design Energy (signal processing) ; Computer science ; Artificial intelligence ; Reliability engineering ; Engineering ; Mathematics ; Statistics Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials
Authors: Arijit Das | Siva Prasad Behera | Ritesh Haldar | Palak Dawar | Sandeepan Maity | Suranjan De
Source: Discover Applied Sciences (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Generative AI as chemist’s virtual assistant: a case study with the chemistry of SmI2 The scientific community is standing on the verge of the emergence of artificial intelligence (AI) in chemical education and research. While the increasing role of AI-based technologies in research is inevitable, it is of paramount importance to verify and improve the output of AI-powered generative systems. We critically evaluated ChatGPT and Google Gemini’s (formerly known as BARD) performance on domain-specific chemical questions, leveraging our expertise in SmI₂ chemistry to benchmark the accuracy and conceptual alignment of AI-generated outputs with established literature. A series of questions related to the well-established chemistry as well as ongoing research of SmI2 were asked to ChatGPT and Google Gemini. The responses obtained from ChatGPT and Google Gemini were analyzed and correlated based on existing literature proof of SmI2 chemistry. Our analysis indicates that ChatGPT and Google Gemini can accurately answer many of the questions related to SmI2 chemistry. However, it has also been observed that these AI-based Chatbots provide completely or slightly different answers when asked multiple times or from different accounts. It was also observed that the response of AI Chatbots can be improved through introducing prompts in the questions. It is our observation and recommendation that these AI-based Chatbots are still a long way from replacing humans as scientific assistants. Generative grammar ; Benchmark (surveying) ; Generative model ; Chatbot ; Applications of artificial intelligence ; Concept learning Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; AI in Service Interactions
Authors: Gayatri V. Padole | Pravin B. Pokle
Source:  (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Advances in Self-Healing Electronics: A Review of 2D Materials and AI-Based Design Approaches Self-healing electronics, which incorporate materials that can self-repair damage, form the promising frontier to develop robust, sustainable electronic devices. The incorporation of 2D materials, such as graphene, MXenes, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{MoS}_{2}$</tex>, has shown the potential to significantly enhance the electrical conductivities and mechanical properties needed for self-healing. This paper will cover the current ongoing research efforts, particularly the presentation of 2D materials in selfhealing electronics and the integration of artificial intelligence techniques into material discovery, device optimization, and manufacturing. The conclusion reveals that damage to electronic devices can be repaired by self-healing through reversible chemical bonding and polymerization, which has a much longer serviceable life and requires less maintenance. AI approaches have also succeeded in predicting new self-healing materials and optimizing device architecture to enhance healing efficiency. However, challenges remain in the area of scalability, material stability, and adaptation during healing times in real time. However, self-healing electronics show great promise in their applications to industries such as health, consumer electronics, and space. Development in this direction will ensure more sustainability since devices will last longer and cost less to operate and maintain. Future directions include addressing existing limitations that retard the commercialization of selfhealing electronics. Commercialization ; Electronics ; Flexibility (engineering) ; Sustainability ; Adaptation (eye) ; Presentation (obstetrics) MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Caleb Simiyu Khaemba | Hongsong Feng | Dong Chen | Chun‐Long Chen | Guo‐Wei Wei
Source: ArXiv.org (preprint, 2025-11-05)
Field: Chemistry — Inorganic Chemistry

Commutative Algebra Modeling in Materials Science -- A Case Study on Metal-Organic Frameworks (MOFs) Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry's constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery. Interpretability ; Commutative property ; Algebra over a field ; Algebraic structure ; Representation (politics) ; Algebraic number ; Algebraic geometry ; Complex system ; Stability (learning theory) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography