jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher W. Stubbs | Dongpeng Huang | Jungyoon Koh | Madeleine Woods | Andrés A. Plazas Malagón
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Lessons Learned from Integrating Generative AI into an Introductory Undergraduate Astronomy Course at Harvard We describe our efforts to fully integrate generative artificial intelligence (GAI) into an introductory undergraduate astronomy course. Ordered by student perception of utility, GAI was used in instructional Python notebooks, in a subset of assignments, for student presentation preparations, and as a participant (in conjunction with a RAG-encoded textbook) in a course Slack channel. Assignments were divided into GAI-encouraged and GAI-discouraged. We incentivized student mastery of the material through midterm and final exams in which electronics were not allowed. Student evaluations of the course showed no reduction compared to the non-GAI version from the previous year. Python (programming language) ; Course (navigation) ; Generative grammar ; Undergraduate student ; Presentation (obstetrics) ; Conjunction (astronomy) Machine Learning in Materials Science ; Computational Physics and Python Applications ; Artificial Intelligence in Healthcare and Education
Source: Journal of Physics Conference Series (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Preface The 2025 6th International Conference on Advanced Materials and Intelligent Manufacturing (ICAMIM 2025) was held in Zhaoqing, China, from November 21 to 23, 2025. Aiming to build an exchange platform integrating academic frontiers, industrial demands, and strategic orientation, the conference was closely aligned with the strategic guidance of The Outline Development Plan for the Guangdong-Hong Kong-Macao Greater Bay Area —which emphasizes “building an international innovation and technology hub with global influence” and “accelerating the development of advanced manufacturing.” This conference brought together international experts, scholars, engineering technicians, and industry representatives, achieving the in-depth integration of the “Industry Summit” and the “International Academic Conference”. Numerous keynote speeches, invited speeches, oral presentations, and poster sessions were conducted. For instance, Prof. Ruxu Du presented a cutting-edge analysis on “Artificial Intelligence and Smart Manufacturing”, while Prof. Liejun Li provided a systematic elaboration on “The Development Trends of New Materials at Home and Abroad and the Development of the New Materials Industry in Guangdong”—both providing high-level academic and industrial perspectives. Additionally, experts and scholars from South China University of Technology, Guangdong Academy of Sciences, and Songshan Lake Materials Laboratory shared their innovative findings and profound insights in areas such as soft magnetic composite materials, ultra-high-strength steel, eutectic high-entropy alloys, intelligent design methodologies, unmanned surface vessel swarm control, and digital factory construction. We believe that knowledge derives its value from dissemination and co-creation. The publication of this volume of proceedings is intended to disseminate academic achievements to a broader audience. It is anticipated that the perspectives, data, and methodologies presented herein will inspire researchers worldwide. The collected papers range from in-depth exploration of the extreme properties of traditional materials to pioneering research into emerging fields such as composite materials and smart structures; they encompass both rigorous investigations into microscopic mechanisms and practical optimization targeting engineering challenges engineering challenges. These works fully reflect the authors’ efforts to enhance material performance, advance manufacturing intelligence, and strengthen industrial competitiveness. List of Committee Member is available in this PDF. Dissemination ; Factory (object-oriented programming) ; Government (linguistics) ; China ; Plan (archaeology) ; Strategic planning ; Digitization Machine Learning in Materials Science ; Material Selection and Properties ; Architecture and Computational Design
Authors: V R Teja Sruthi Pagadala, P. Rithisha, S K Asma, Hasrat Mirza, P. Niveditha Spurthi, B. Rohini, L. Geethanjali, S K Samrin, S K Karishma
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-18)
Field: Materials Science — Biomaterials

AI-Designed Protein-Lipid Hybrid Nanoparticles for Targeted Cancer Therapy and Multi-Disease Applications Chronic and complex diseases, particularly cancer, require targeted and efficient drug delivery strategies to overcome the limitations of conventional therapeutics, such as poor specificity, systemic toxicity, and variable clinical outcomes. Advances in nanotechnology have led to the development of protein-lipid hybrid nanoparticles (PLHNs), which integrate the biocompatibility and drug-loading capacity of lipid-based carriers with the biological recognition and targeting functions of proteins. These hybrid systems offer improved stability, controlled drug release, and enhanced interaction with biological systems. Artificial intelligence (AI) has emerged as a powerful tool to rationally design and optimize PLHNs by predicting optimal lipid-protein combinations, particle size, surface charge, drug encapsulation efficiency, and targeting ligand selection. AI-driven approaches reduce empirical trial-and-error experimentation and enable precise tuning of nano-bio interactions. In targeted cancer therapy, AI-designed PLHNs exploit both passive targeting via the enhanced permeability and retention (EPR) effect and active targeting through protein- or peptide-mediated receptor recognition, leading to improved cellular uptake and controlled intracellular drug release. Beyond oncology, AI-optimized PLHNs demonstrate significant potential in the treatment of neurological, infectious, metabolic, and inflammatory diseases by enabling barrier penetration, sustained drug delivery, and disease-specific targeting. Their modular and biomimetic design supports the co-delivery of diverse therapeutic payloads, including small molecules, proteins, and nucleic acids. Overall, AI-designed protein-lipid hybrid nanoparticles represent a promising and versatile nanomedicine platform with strong potential to enhance therapeutic efficacy, improve translational predictability, and support the future development of precision and personalized medicine. Nanomedicine ; Targeted drug delivery ; Drug delivery ; Cancer therapy ; Drug ; Modular design ; Biocompatibility ; Cancer treatment Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Cancer, Lipids, and Metabolism
Authors: Neethu Sebastian | Wan-Chin Yu | Deepak Balram | Qianqiao Chen | Salman S. Alharthi | Hamed M. Al‐Saidi
Source: Journal of environmental chemical engineering (article, 2026-02-24)
Field: Chemistry — Analytical Chemistry

Intelligent quantification of ecotoxic fungicide in viticultural environment using AI-driven non-isotropic GPR integrated electrochemical sensor Detection limit ; Electrochemical gas sensor ; Analyte ; Nanocomposite ; Reflectometry ; Radiative forcing ; Contamination Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Sensors
Authors: Dinh-Khiet Le | Minh-Quyet Ha | Nguyễn Tiến Đạt | Viet-Ha Nguyen | Hieu‐Chi Dam
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Crystallography as language: transforming structural data into textual description for LLM-aided materials discovery The discovery of high-performance permanent magnets necessitates effective strategies to stabilize promising compounds, such as SmFe12, which exhibit favorable magnetic properties but inherent thermodynamic instability. While data-driven methods have advanced materials discovery, they face significant limitations: combinatorial explosion of potential substitutions, descriptor design challenges, data scarcity and bias, and the opaque nature of many models lacking physical interpretability. This study presents a human-AI collaborative framework leveraging large language models (LLMs) to reason about crystal structures by transforming crystallographic data into interpretable textual descriptions. Our Prompt-Guided LLM approach achieves 85% classification accuracy and 88% F1-score across 96 SmFe12-based structures when validated against DFT calculations. The framework further demonstrates structure generation capability, producing 24 candidate structures, of which 10 exhibit enhanced stability or magnetization compared with their host structures; notably, SmFe12N and SmFe10Co2N – show enhancement in both properties compared with SmFe12 (<i>I</i>4/<i>mmm</i>). Crucially, the model provides physically sound rationales for its predictions, suggesting mechanisms such as lattice strain relief and electron balance optimization that align with established materials science principles. These results demonstrate that carefully implemented LLMs can effectively support crystal structure discovery while providing explanatory insights for materials design. Crystal structure prediction ; Lattice (music) ; Crystal structure ; Stability (learning theory) ; Data structure ; Face (sociological concept) ; Opacity Machine Learning in Materials Science ; Magnetic Properties of Alloys ; X-ray Diffraction in Crystallography
Authors: Dinh-Khiet Le | Minh-Quyet Ha | Nguyễn Tiến Đạt | Viet-Ha Nguyen | Hieu‐Chi Dam
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Crystallography as language: transforming structural data into textual description for LLM-aided materials discovery The discovery of high-performance permanent magnets necessitates effective strategies to stabilize promising compounds, such as SmFe12, which exhibit favorable magnetic properties but inherent thermodynamic instability. While data-driven methods have advanced materials discovery, they face significant limitations: combinatorial explosion of potential substitutions, descriptor design challenges, data scarcity and bias, and the opaque nature of many models lacking physical interpretability. This study presents a human-AI collaborative framework leveraging large language models (LLMs) to reason about crystal structures by transforming crystallographic data into interpretable textual descriptions. Our Prompt-Guided LLM approach achieves 85% classification accuracy and 88% F1-score across 96 SmFe12-based structures when validated against DFT calculations. The framework further demonstrates structure generation capability, producing 24 candidate structures, of which 10 exhibit enhanced stability or magnetization compared with their host structures; notably, SmFe12N and SmFe10Co2N – show enhancement in both properties compared with SmFe12 (<i>I</i>4/<i>mmm</i>). Crucially, the model provides physically sound rationales for its predictions, suggesting mechanisms such as lattice strain relief and electron balance optimization that align with established materials science principles. These results demonstrate that carefully implemented LLMs can effectively support crystal structure discovery while providing explanatory insights for materials design. Crystal structure prediction ; Lattice (music) ; Crystal structure ; Stability (learning theory) ; Data structure ; Face (sociological concept) ; Opacity Machine Learning in Materials Science ; Magnetic Properties of Alloys ; X-ray Diffraction in Crystallography
Authors: Courtney Bower | Paolo Patelli | Babetta L. Marrone | William L. Kubic
Source: Journal of Cleaner Production (article, 2026-03-01)
Field: Materials Science — Biomaterials

Quantifying market volume sensitivity to material property modifications in polyhydroxybutyrate: A parametric analysis approach Commercialization ; Volume (thermodynamics) ; Market share ; Market analysis ; Production (economics) ; Property (philosophy) ; Biopolymer ; Parametric statistics biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Polymer crystallization and properties
Authors: Dina Listov | Sarel J. Fleishman
Source: Current Opinion in Structural Biology (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

De novo enzyme design: Controlling structure to design function The ultimate mission of de novo enzyme design methodology is to develop strategies that produce new-to-nature enzymes that match the efficiency and versatility of natural ones. Until recent years, design methods yielded enzymes with low catalytic efficiencies even for simple reactions, underscoring the need for tighter control over backbone structure and active-site preorganization. Two approaches have recently emerged to address these problems: artificial intelligence-driven design of de novo folds and evolution-guided atomistic design of natural folds. These strategies have produced catalysts with efficiencies approaching natural enzymes, but achieving high catalytic rates and complex, multistep mechanisms remains challenging. We argue that progress toward high-performance enzymes for novel reactions requires more precise control over complex natural folds and close collaboration between designers and computational chemists. Enzyme ; Function (biology) ; Protein design ; Protein structure ; Structure function Enzyme Structure and Function ; Protein Structure and Dynamics ; Fungal and yeast genetics research
Authors: Jinfeng Zhang | Pbs Lai | Baojun Li | Xianguang Yang
Source: Advanced Optical Materials (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

From Optical Pipes to Intelligent Platforms: The Evolution of Fiber‐Optic Waveguides with Multifunctional Materials ABSTRACT Fiber‐optic waveguides (FOWGs) are undergoing a transformative leap beyond their traditional role as passive light‐transmission channels. This evolution is fueled by the integration of novel functional materials—such as stimuli‐responsive hydrogels, aggregation‐induced emission (AIE) luminogens, and natural biomaterials—coupled with advanced fabrication techniques. These innovations enable a “material‐as‐function” paradigm, embedding capabilities like sensing, actuation, and computation directly into the waveguide structure. This Perspective explores how these intelligent FOWG‐based platforms are unlocking emerging applications in real‐time environmental and biomedical sensing, minimally invasive photomedicine, and intuitive human‐machine interfaces for robotics. Looking forward, we chart a developmental trajectory from materials to devices and systems, emphasizing the pivotal role of artificial intelligence in orchestrating this multifunctional integration. The convergence of material science, photonics, and AI is poised to transition FOWGs from simple optical pipes into adaptive, intelligent platforms that will underpin next‐generation healthcare, environmental monitoring, and interactive technologies. Transformative learning ; Embedding ; Fabrication ; Waveguide ; Convergence (economics) ; Perspective (graphical) ; Chart Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Advanced Sensor and Energy Harvesting Materials
Authors: Lang-tao Fan | Lin Shen | Yongchao Liang | Lili Zhou | Zihan Zheng | Wen-Hao Ji | Hui-jie Wang | Xiaolu Xie
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Glass-Forming Ability in Metallic Glasses Based on Experimental Characteristic Temperatures Robustness (evolution) ; Amorphous metal ; Experimental data ; Alloy ; Stacking ; Random forest ; Casting Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Injection Molding Process and Properties
Authors: Pan Li | Jitao Zhou | Huihui Sun | Ying Wei | Qiqi Yang
Source: Materials Today Communications (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Thermodynamics-constrained generative adversarial network for synergistic strength-ductility inverse design of rare-earth magnesium alloys Data-driven methodologies are reshaping magnesium alloy research, yet resolving the critical “strength-ductility trade-off” in high-dimensional composition spaces remains hindered by several unsolved bottlenecks in existing data-driven models for rare-earth magnesium alloy design, such as data sparsity in high-performance regions, and lack of thermodynamic feasibility and physical interpretability. To address these issues, this study proposes a novel Thermodynamics-Constrained Generative Adversarial Network (TC-GAN) framework, which integrates physical metallurgy rules with generative AI via a synergistic “Data Augmentation-Property Prediction-Inverse Design” pathway to realize end-to-end inverse design of manufacturable alloys. The key novelties and quantitative contributions are threefold: (i) A distribution-aware data augmentation module (PIDAM) is developed to reconstruct high-dimensional sparse data manifolds, expanding the original dataset from 860 to 2060 thermodynamically feasible high-fidelity samples; (ii) A sequential attention-based TabNet deep ensemble predictor (TabNetDP) is constructed, achieving high-precision prediction of mechanical properties for multi-component magnesium alloys, with a test-set R² > 0.95 and rigorous uncertainty quantification; (iii) For the first time, Hume-Rothery rules and phase equilibrium constraints are embedded into the generative adversarial network via the thermodynamics-constrained generator (MOTCG), establishing a “physical fence” mechanism that boosts the thermodynamic feasibility rate of generated compositions to 98.2%. Applied to the Mg-Gd-Y-Zn-Zr system, TC-GAN designs significantly transcend the existing Pareto front: the optimal alloy achieves an ultimate tensile strength of 485 MPa and an elongation of 12.5%, outperforming the commercial WE43 alloy by 18.3% in strength and 47.1% in ductility. SHAP analysis and CALPHAD calculations verify the underlying synergistic strengthening mechanism, establishing a universal physics-data fusion paradigm for high-performance multi-component alloy design. Adversarial system ; Inverse ; Generative grammar ; Magnesium ; Inverse problem ; Generative adversarial network Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Magnesium Alloys: Properties and Applications
Authors: Ran Li | Shimin Di | Haowei LI | Luanshi Bu | Jiachuan Wang | Wangze Ni | Lei Chen
Source: arXiv (Cornell University) (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning Chemical reaction prediction is pivotal for accelerating drug discovery and synthesis planning. Despite advances in data-driven models, current approaches are hindered by an overemphasis on parameter and dataset scaling. Some methods coupled with evaluation techniques that bypass fundamental challenges in reaction representation and fail to capture deep chemical intuition like reaction common sense and {topological atom mapping logic}. We argue that the core challenge lies in instilling these knowledge into the models. To this end, we propose a unified framework that prioritizes chemical understanding over scale through three key innovations: (1) a {Latent Chemical Consistency} objective that models reactions as movements on a continuous chemical manifold, ensuring reversible and physically plausible transformations; (2) a {Hierarchical Cognitive Curriculum} that trains the model through progressive stages, from syntax mastery to semantic reasoning, building robust chemical intuition; (3) {Atom-Map Permutation Invariance (AMPI)}, which force the model to learn invariant relational topology and balance multi-task learning. (4)and structured plan-based reasoning to improve the performance of the LLMs. Our compact {0.5B-parameter model}, \textbf{RxnNano} significantly outperforms fine-tuned LLMs ten times larger (&gt;7B) and all the domain baselines, achieving a 23.5\% Top-1 accuracy improvement on rigorous benchmarks without test-time augmentation. https://github.com/rlisml/RxnNano. Intuition ; Representation (politics) ; Retrosynthetic analysis ; Key (lock) ; Knowledge representation and reasoning ; Domain knowledge ; Bridging (networking) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Kuladip Barman | Shivam Kumar | Sourabh Satapathy | Neha Singh | Balak Das Kurmi | Ankur M. Vaidya | Preeti Patel
Source: Current Spectroscopy and Chromatography (article, 2026-03-11)
Field: Chemistry — Spectroscopy

Advances in Liquid Chromatography-Mass Spectrometry (LC-MS): Integration, Innovations, and Applications Abstract: Liquid Chromatography-Mass Spectrometry (LC-MS) has evolved into a cornerstone of analytical science. This review moves beyond a descriptive overview to provide a critical synthesis of the literature, highlighting technological advances, methodological strengths and limitations, and future opportunities. The novelty lies in its analytical perspective-evaluating challenges such as reproducibility, scalability, and regulatory adoption-while contextualizing innovations like highresolution mass analyzers, AI-driven data analysis, and sustainable LC-MS workflows. By combining critique with synthesis, this review offers insights into both the current state and the transformative potential of LC-MS. Mass spectrometry (MS) has significantly enhanced liquid-phase separations by enabling sensitive and precise analysis of complex mixtures. This review examines the integration of MS with chromatographic techniques, including UPLC, HRMS, and IMS. Advances in instrumentation, including novel ionization methods and miniaturization, have improved analytical performance. Methodologies critical for successful liquid phase separations, including sample preparation and chromatographic techniques, are discussed along with innovations in instrumentation such as miniaturization and novel ionization methods. Through miniaturization and automation, multiple parallel experiments can be conducted efficiently. Applications of liquid phase MS in pharmaceutical analysis, environmental monitoring, and metabolomics/proteomics illustrate its broad utility and evolving role. The interfaces for gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/ mass spectrometry (LC/MS) are adapting to rapid developments in MS instrumentation and separation technologies. Recent decades have seen significant advances in analytical techniques like MS and NMR, applicable for the identification and structural analysis of molecules. Highthroughput experimentation is a growing field demanding fast analytical techniques for critical data generation. This review explores recent developments in coupling liquid chromatographic and electrophoretic separations with MS, facilitating structural elucidation and quantitation. It examines current trends, methodologies, and prospects in liquid phase MS, emphasizing the transformative impact of advancements and the drive toward realizing its full potential in analytical chemistry. Instrumentation (computer programming) ; Mass spectrometry ; Miniaturization ; Analytical technique ; Field (mathematics) ; Data acquisition ; Characterization (materials science) ; Ambient ionization Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Chunyan Li | Shaojie Qiao | Guifei Zhou | Jianming Wang
Source: Knowledge-Based Systems (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Disentangled molecular representation learning with context-aware codebook for OOD generalization Spurious relationship ; Invariant (physics) ; Codebook ; Representation (politics) ; External Data Representation ; Feature learning ; Vector quantization ; ENCODE Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Domain Adaptation and Few-Shot Learning