jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brent Wagner | Olivia X Jastrzemski | Sasha R Vigil | Lien Tang | Shreeti Patel | Amy Cunningham | Joshua DeAguero | Karol Dokladny | G Patricia Escobar | Jing Yang | Frederick Gentry | Ian Henderson | Rachel Coyte | Martin L Kirk | James H Degnan | Bevan T Choate
Source: Federal Practitioner (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Gadolinium Intermediate Elimination and Persistent Symptoms After Magnetic Resonance Imaging Contrast Agent Exposure Our findings suggest that detectable gadolinium in serum and urine may reflect subclinical exposure, even at doses below standard prescriptions. Clinicians must consider gadolinium as a potential cause of pleiotropic symptoms even when, as in this case, symptoms persist despite undetectable levels of gadolinium. Gadolinium ; Magnetic resonance imaging ; Contrast (vision) ; Gadolinium DTPA Lanthanide and Transition Metal Complexes ; Radiation Dose and Imaging ; Advanced MRI Techniques and Applications
Authors: Andersen Chang | Tiffany M. Tang | Tarek M. Zikry | Genevera I. Allen
Source: ArXiv.org (preprint, 2025-06-05)
Field: Materials Science — Materials Chemistry

Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread utilization, there is a lack of standardization in unsupervised learning workflows for making reliable and reproducible scientific discoveries. In this paper, we present a structured workflow for using unsupervised learning techniques in science. We highlight and discuss best practices starting with formulating validatable scientific questions, conducting robust data preparation and exploration, using a range of modeling techniques, performing rigorous validation by evaluating the stability and generalizability of unsupervised learning conclusions, and promoting effective communication and documentation of results to ensure reproducible scientific discoveries. To illustrate our proposed workflow, we present a case study from astronomy, seeking to refine globular clusters of Milky Way stars based upon their chemical composition. Our case study highlights the importance of validation and illustrates how the benefits of a carefully-designed workflow for unsupervised learning can advance scientific discovery. Unsupervised learning ; Workflow ; Documentation ; Generalizability theory ; Best practice ; Stability (learning theory) ; Standardization Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Krishnaveni Nagappan | SHARAN RAJ RAJMOHAN | Malwane M. A. Ananda | AISWARYA MAHENDRAN | BALAGEE MUTHUKUMAR | Priyadarshana H.V.V
Source: International Journal of Applied Pharmaceutics (review, 2025-11-07)
Field: Chemistry — Analytical Chemistry

A COMPREHENSIVE REVIEW OF VARIOUS ANALYTICAL METHOD AVAILABLE FOR THE DETERMINATION OF FOOD COLOURS CONSIDERING DIFFERENT REGULATORY ASPECTS Food additives have been an integral part since ancient times in the preservation and enhancement of food. Ancient civilization had evolved from simple preservation techniques to sophisticated synthetic compounds that transformed over thousands of years. This review article focuses on various food additives such as: acidity regulators, emulsifying and stabilizing agents, antioxidants, colorants, flavouring agents, leavening agents, bulking agents, and preservatives. Particular emphasis is placed on the colorants in food and divided it into three main categories: natural, nature-identical, and synthetic. Food colours have a different regulatory framework across all the countries. However, few differences have been seen among FDA of United States, EFSA of European Union, and FSSAI of India. India has approved only nine synthetic colour additives while the FDA allows 19, EU allows food colours of 43 altogether-natural and synthetic. The analytical techniques employed for the analysis of food colours are validated HPLC, UV-Visible spectrophotometry, and Mass spectrometry methods. Coupled Ultra-High-Performance Liquid Chromatography (UHPLC) with tandem mass spectrometry (MS/MS) techniques have led to highly sensitive methods for determination of food colours in different food matrices. For this reason, the understanding of such analytical methods has ensured compliance toward regulatory limits; Inturn, food safety standards within the global food chain supply. Leavening agent ; Food additive ; Food products ; Food safety ; Food packaging ; Food processing ; Food industry Dye analysis and toxicity ; Melamine detection and toxicity ; Saffron Plant Research Studies
Authors: Hiroto Kuroyanagi | Tatsuro Yuge
Source: Physical review. E (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Deep learning of thermodynamic laws from microscopic dynamics We numerically show that a deep neural network (DNN) can learn macroscopic thermodynamic laws purely from microscopic data. Using molecular dynamics simulations, we generate the data of snapshot images of gas particles undergoing adiabatic processes. We train a DNN to determine the temporal order of input image pairs. We observe that the trained network induces an order relation between states consistent with adiabatic accessibility, satisfying the axioms of thermodynamics. Furthermore, the internal representation learned by the DNN acts as an entropy. These results suggest that machine learning can discover emergent physical laws that are valid at scales far larger than those of the underlying constituents---opening a pathway to data-driven discovery of macroscopic physics. Adiabatic process ; Artificial neural network ; Deep learning ; Axiom ; Representation (politics) ; Physical law ; Snapshot (computer storage) Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Phase Equilibria and Thermodynamics
Authors: Rodolfo O. Esquivel | Hazel Vázquez-Hernández | Jonatan Ornelas-Muñoz
Source: Preprints.org (preprint, 2025-12-15)
Field: Chemistry — Physical and Theoretical Chemistry

Spiral Representation of the Periodic Table: A Unified 3D Analysis of Information-Theoretic Functionals for the Atomic Elements Z = 1–103 Here we perform a detailed information-theoretic (IT) analysis of atomic electron densities in the Periodic Table, from hydrogen (Z = 1) to lawrencium (Z = 103). By use of the Shannon entropy, the Fisher information and the disequilibrium functional in both position and momentum spaces as fundamental descriptors of the atomic densities, the Periodic Table can be represented in our three-dimensional information space as a continuous, highly ordered manifold. The analysis shows that chemical periodicity naturally reveals a geometrical spiral at the coordinates of theoretic-information space (Shannon, Fisher, Disequilibrium), with each period forming one segment within the continuous global trajectory. We find information-theoretic signatures of shell structure, subshell filling, and electron-configuration anomalies, such as the familiar irregularities seen in chromium and copper. Through the uncertainty principle of complementary analysis in momentum space, more insights are gained by exposing maximal information-theoretic differentiation for lighter atoms and compression among heavy elements. We demonstrate, by considering triadic relationships and complexity properties in relation to the López-Mancini-Ruiz (LMC) and Fisher-Shannon (FS) functionals, that atomic complexity increases monotonically along with nuclear charge and we provide a quantitative measure of how organized atomic electron densities are distributed throughout the periodic system. Based on our IT analyses, the fundamental character of periodicity could be addressed by employing spiral representations that continue the characteristic of hydrogen, but also preserving the autonomy of the blocks of elements. Periodic table ; Spiral (railway) ; Space (punctuation) ; Representation (politics) ; Position (finance) ; Atomic number ; Atomic theory ; Electron History and advancements in chemistry ; Machine Learning in Materials Science ; Graph theory and applications
Authors: D. Vidović | Behrouz Shamsaei | Stephan C. Schürer | Phillip Kogan | Szymon Chojnacki | Michal Kouril | Mario Medvedovic | Wen Niu | Evren U. Azeloglu | Marc R. Birtwistle | Yibang Chen | Tong Chen | Jens Hansen | Bin Hu | Ravi Iyengar | Gomathi Jayaraman | Hong Li | Tong Liu | Eric A. Sobie | Yuguang Xiong | Matthew J. Berberich | Gary A. Bradshaw | Mirra Chung | Robert A. Everley | Benjamin L. Gaudio | Marc Hafner | Marian Kalocsay | Caitlin E. Mills | Maulik K. Nariya | Peter K. Sorger | Kartik Subramanian | Chiara Victor | Maria G. Bañuelos | Victoria Dardov | Ronald J. Holewinski | Danica-Mae Manalo | Berhan Mandefro | Andrea Matlock | Loren Ornelas | Dhruv Sareen | Clive N. Svendsen | Vineet Vaibhav | Jennifer E. Van Eyk | Vidya Venkatraman | Steve Finkbiener | Ernest Fraenkel | Jeffrey D. Rothstein | Leslie M. Thompson | Jacob K. Asiedu | Steven A. Carr | Karen E. Christianson | Desiree Davison | Deborah Dele‐Oni | Katherine C. DeRuff | Shawn B. Egri | Alvaro Sebastian Vaca Jácome | Jacob D. Jaffe | Daniel D. Lam | Lev Litichevskiy | Xiaodong Lü | James Mullahoo | Adam Officer | Malvina Papanastasiou | Ryan Peckner | Caidin Toder | Joel Blanchard | Michael Bula | Tak Ko | Li‐Huei Tsai | Jennie Z. Young | Vagisha Sharma | Ajay Pillai | Jarek Meller | Michael J. MacCoss
Source: Molecular & Cellular Proteomics (article, 2025-03-13)
Field: Chemistry — Spectroscopy

Comprehensive Proteomics Metadata and Integrative Web Portals Facilitate Sharing and Integration of LINCS Multiomics Data The Library of Integrated Network-based Cellular Signatures (LINCS), an NIH Common Fund program, has cataloged and analyzed cellular function and molecular activity profiles in response to >80,000 perturbing agents that are potentially disruptive to cells. Because of the importance of proteins and their modifications to the response of specific cellular perturbations, four of the six LINCS centers have included significant proteomics efforts in the characterization of the resulting phenotype. This manuscript aims to describe this effort and the data harmonization and integration of the LINCS proteomics data discussed in recent LINCS papers. Metadata ; Proteomics ; World Wide Web ; Computer science ; Data integration ; Computational biology ; Data science ; Biology ; Database Advanced Proteomics Techniques and Applications ; Biomedical Text Mining and Ontologies ; Bioinformatics and Genomic Networks
Authors: Honghao Chen (1675084) | Hongxuan Liu (14408982) | Yishen Tew (22250248) | Xiaotian Ren (13218418) | Xiaojin Tang (22250251) | Xiaonan Wang (276308)
Source: Figshare (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

Distilling Knowledgefrom Catalysis Literature withLong-Context Large Language Model Agents Decades of catalysis knowledge remain locked in unstructured prose, hindering data-driven discovery. Existing text-mining tools struggle to establish the synthesis–structure–performance relationships critical for catalyst knowledge discovery as they rarely connect synthesis protocols in one section with the resulting material properties and performance outcomes reported elsewhere. Here, we present CATDA (Corpus-aware Automated Text-to-Graph Catalyst Discovery Agent), a long-context large language model-driven agentic framework that reads full documents and distills them into actionable, provenance-tracked knowledge graphs linking material properties, multistep synthesis, conditions, and testing outcomes. Applied at corpus scale, CATDA extracts data with near-human fidelity (F1 = 0.983) and a 12-fold speedup over manual curation. This structured knowledge is made accessible through two synergistic applications: a DatasetAgent for exporting machine-learning-ready tables and a CatAgent providing a conversational, citation-linked interface for interactive discovery. The high-quality data set enabled the training of a predictive model for ethylbenzene conversion while simultaneously exposing systemic challenges such as feature sparsity and protocol heterogeneity in the source literature. By transforming the literature into a queryable and computable resource, CATDA offers a scalable route to accelerate large-scale data analysis, quantitative modeling, and a rational catalyst design paradigm. Scalability ; Set (abstract data type) ; Interface (matter) ; Protocol (science) ; Ethylbenzene ; Query language ; Fidelity ; Speedup ; Knowledge extraction Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Graph Neural Networks
Authors: Ryan Nduma | Hyunsoo Park | Aron Walsh
Source: ArXiv.org (preprint, 2025-11-30)
Field: Materials Science — Materials Chemistry

Crystalyse: a multi-tool agent for materials design We present Crystalyse, an open, provenance-enforced scientific agent for computational materials design of inorganic crystals that orchestrates tools for compositional screening, crystal structure generation, and machine-learning force-field evaluation. Crystalyse offers three operating modes to trade exploration speed against validation depth: creative (rapid query), adaptive (context-aware routing) and rigorous (comprehensive checks). We release the underlying source code and evaluation scripts to enable plug-and-play use and development. In demonstrations on quaternary oxide exploration, sodium-ion cathode design, and lead-free indoor photovoltaic candidate generation, the agent integrates chemical compound generation with fast stability and property filters. Under adversarial testing, provenance enforcement eliminated material-property hallucinations (a broad adversarial suite pass rate reached 86% from a 57% baseline). Crystalyse provides an agentic artificial intelligence system that can complement existing materials design pipelines, assisting in hypothesis generation while preserving transparency and reproducibility. Transparency (behavior) ; Adversarial system ; Scripting language ; Suite ; Traceability ; Key (lock) ; Interoperability Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Pandurang M. Gaikwad | Megha Hange | P. N. Sable
Source: International Journal of Pharmacy and Pharmaceutical Research (article, 2025-01-01)
Field: Materials Science — Biomaterials

Recent Advances in Nanosponges as Drug Delivery System The most recent developments in nanotechnology have led to the creation of a targeted medicine delivery system.A specialized medication delivery mechanism is necessary, nevertheless, in order to effectively target a molecule to a particular location.The development of nanosponge has been a significant stride in addressing issues like limited bioavailability and drug toxicity.Nanosponges, which are three-dimensional drug transporters at the nanoscale, are created by crosslinking polymers.The ability to hold a range of pharmaceuticals in various sizes is one advantage they provide.Because they may offer targeted drug delivery along with a regulated drug release pattern, nanosponges are better than alternative delivery methods.Nanosponges are microscopic sponges that are roughly the size of viruses (250 nm-1 m).They have chambers that can hold a wide range of medications.When it comes to regulated drug distribution, nanosponge is essential.Because it can load water and lipid-soluble drug molecules and lessen their side effects, it can increase the aqueous solubility and penurious bioavailability of medications in a variety of dosage forms for controlled drug administration, including oral, parenteral, topical, rectal, and inhalational dose forms.This review explores the advantages and disadvantages, methods of preparation, variables that affect their preparation, methods of characterization, uses, and the latest developments in nanosponges.The preparation process, characterization, and potential usage in drug delivery systems are the main topics of the current review. Drug delivery ; Drug ; Pharmacology ; Nanotechnology ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Bjarke Hastrup | François Cornet | Tejs Vegge | Arghya Bhowmik
Source: Research Square (preprint, 2025-06-17)
Field: Chemistry — Physical and Theoretical Chemistry

Discovering Chemical Space from First Principles with Reinforcement Learning Space (punctuation) ; Reinforcement learning ; Chemical space ; Reinforcement ; Computer science ; Artificial intelligence ; Psychology ; Chemistry ; Social psychology Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Shengchao Liu | Hannan Xu | Yan Ai | Huanxin Li | Yoshua Bengio | Hai-Feng Guo
Source: ArXiv.org (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization Large language models (LLMs) leverage chain-of-thought (CoT) techniques to tackle complex problems, representing a transformative breakthrough in artificial intelligence (AI). However, their reasoning capabilities have primarily been demonstrated in solving math and coding problems, leaving their potential for domain-specific applications-such as battery discovery-largely unexplored. Inspired by the idea that reasoning mirrors a form of guided search, we introduce ChatBattery, a novel agentic framework that integrates domain knowledge to steer LLMs toward more effective reasoning in materials design. Using ChatBattery, we successfully identify, synthesize, and characterize three novel lithium-ion battery cathode materials, which achieve practical capacity improvements of 28.8%, 25.2%, and 18.5%, respectively, over the widely used cathode material, LiNi0.8Mn0.1Co0.1O2 (NMC811). Beyond this discovery, ChatBattery paves a new path by showing a successful LLM-driven and reasoning-based platform for battery materials invention. This complete AI-driven cycle-from design to synthesis to characterization-demonstrates the transformative potential of AI-driven reasoning in revolutionizing materials discovery. Transformative learning ; Leverage (statistics) ; Battery (electricity) ; Coding (social sciences) ; Domain (mathematical analysis) ; Spatial intelligence Machine Learning in Materials Science ; Fault Detection and Control Systems ; Advanced Data Processing Techniques
Authors: Yu Zhang | Han Yang | Shuai Chen | Ruijie Yu | Xin Zhao | Xiangtian Liu | Kaipeng Zeng | Mengdi Yu | Jidong Tian | Feng Zhu | Xiaokang Yang | Yaohui Jin | Yanyan Xu
Source: ArXiv.org (preprint, 2025-04-25)
Field: Materials Science — Materials Chemistry

Large Language Models to Accelerate Organic Chemistry Synthesis Chemical synthesis, as a foundational methodology in the creation of transformative molecules, exerts substantial influence across diverse sectors from life sciences to materials and energy. Current chemical synthesis practices emphasize laborious and costly trial-and-error workflows, underscoring the urgent need for advanced AI assistants. Nowadays, large language models (LLMs), typified by GPT-4, have been introduced as an efficient tool to facilitate scientific research. Here, we present Chemma, a fully fine-tuned LLM with 1.28 million pairs of Q&amp;A about reactions, as an assistant to accelerate organic chemistry synthesis. Chemma surpasses the best-known results in multiple chemical tasks, e.g., single-step retrosynthesis and yield prediction, which highlights the potential of general AI for organic chemistry. Via predicting yields across the experimental reaction space, Chemma significantly improves the reaction exploration capability of Bayesian optimization. More importantly, integrated in an active learning framework, Chemma exhibits advanced potential for autonomous experimental exploration and optimization in open reaction spaces. For an unreported Suzuki-Miyaura cross-coupling reaction of cyclic aminoboronates and aryl halides for the synthesis of $α$-Aryl N-heterocycles, the human-AI collaboration successfully explored suitable ligand and solvent (1,4-dioxane) within only 15 runs, achieving an isolated yield of 67%. These results reveal that, without quantum-chemical calculations, Chemma can comprehend and extract chemical insights from reaction data, in a manner akin to human experts. This work opens avenues for accelerating organic chemistry synthesis with adapted large language models. Yield (engineering) ; Retrosynthetic analysis ; Transformative learning ; Work (physics) ; Organic synthesis ; Chemical products ; Representation (politics) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry
Authors: Tianrui Yang
Source: Journal of Computational Methods in Sciences and Engineering (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Automated construction of inorganic materials databases and materials discovery based on large language models The rapid expansion of materials science literature demands scalable and intelligent systems for extracting, structuring, and utilizing scientific knowledge. Traditional manual approaches to inorganic materials database construction are labor-intensive and error-prone. In this study, we propose a novel end-to-end framework that leverages instruction-tuned large language models (LLMs) for automated knowledge extraction and discovery in inorganic materials science. By fine-tuning LLMs on domain-specific corpora—including peer-reviewed articles, patents, and chemical databases—the system accurately extracts structured material-property-synthesis relationships from unstructured text. These records are aligned to a schema and stored in a queryable knowledge graph. Furthermore, we demonstrate inverse design by prompting LLMs to generate candidate materials satisfying user-defined targets (e.g., high thermal conductivity). Evaluations on benchmark synthesis corpora show high accuracy in named entity recognition (F1-score &gt; 95%) and low numerical error for temperature/duration extraction. The resulting database supports effective downstream applications such as Curie temperature prediction (RMSE = 23.6 K, R ^ 2 = 0.927). This work showcases how LLMs, when combined with schema-aware reasoning and human-in-the-loop curation, can serve as robust tools for accelerating autonomous materials discovery. Computer science ; Database ; Natural language processing ; Information retrieval Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Geochemistry and Geologic Mapping