jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingyue Li | Xin Yang | Shilin Yan | Jinye Ran | Morui Zhu | Zirui Peng | Huanqing Peng | Wei Peng | Guanghua Zhang | Shuo Li | Hao Zhang
Source: ArXiv.org (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Phase-Interface Instance Segmentation as a Visual Sensor for Laboratory Process Monitoring Reliable visual monitoring of chemical experiments remains challenging in transparent glassware, where weak phase boundaries and optical artifacts degrade conventional segmentation. We formulate laboratory phenomena as the time evolution of phase interfaces and introduce the Chemical Transparent Glasses dataset 2.0 (CTG 2.0), a vessel-aware benchmark with 3,668 images, 23 glassware categories, and five multiphase interface types for phase-interface instance segmentation. Building on YOLO11m-seg, we propose LGA-RCM-YOLO, which combines Local-Global Attention (LGA) for robust semantic representation and a Rectangular Self-Calibration Module (RCM) for boundary refinement of thin, elongated interfaces. On CTG 2.0, the proposed model achieves 84.4% AP@0.5 and 58.43% AP@0.5-0.95, improving over the YOLO11m baseline by 6.42 and 8.75 AP points, respectively, while maintaining near real-time inference (13.67 FPS, RTX 3060). An auxiliary color-attribute head further labels liquid instances as colored or colorless with 98.71% precision and 98.32% recall. Finally, we demonstrate continuous process monitoring in separatory-funnel phase separation and crystallization, showing that phase-interface instance segmentation can serve as a practical visual sensor for laboratory automation. Segmentation ; Process (computing) ; Inference ; Benchmark (surveying) ; Representation (politics) ; Visual inspection ; Interface (matter) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Industrial Vision Systems and Defect Detection
Authors: Aatish Pradhan | Gaurav Vishwakarma | Johannes Hachmann
Source: ChemRxiv (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Reward Hacking in Materials Discovery: An Interpretable Inverse Design Framework and its Physical Limits The inverse design of tailored molecular materials is currently dominated by high-capacity generative models.The "black-box" nature of these models often obscures structure-property relationships and yields candidates with questionable physical transparency.In this work, we present an interpretable, featurecorrelation-driven inverse engineering protocol that bridges robust statistical descriptor analysis with constrained genetic algorithms (GAs).By extracting structural primitives via hypergeometric Z-score and Ascore analysis-subject to rigorous false discovery rate (FDR) corrections-we provide a physically grounded complement to latent-space sampling.The protocol successfully generated 2nd-generation libraries for highrefractive-index polyimides and high-density organic molecules.Critically, we employ first-principles DFT validation to diagnose the "extrapolation crisis" inherent in 2D surrogate models.The GA successfully optimized topological motifs, yielding a predicted RI max = 2.16 and # max = 1770 kg/m^3.DFT validation, however, revealed that 3D steric effects cap the realizable properties at RI = 1.58 and # = 1251 kg/m^3.This work establishes a transparent, motif-guided framework for constrained generative design.It also provides a rigorous methodology for identifying the physical boundaries where topological heuristics fail. Inverse ; Hacker ; Inverse problem ; Entropy (arrow of time) Machine Learning in Materials Science ; Material Selection and Properties ; Computational Drug Discovery Methods
Authors: Ubaid Ullah Khan | Muhammad Umair | Ali Hamza Zahid | Maryam Aziz | Muhammad Umar Farooq Ahmad Kharl | Muhammad Aftab | Huraira Sayam | Muhammad Umer Farooq | Shariq Hassan | Waheed Zaman Khan
Source: Scholars Journal of Engineering and Technology (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

AI and Physics-Guided Multimodal Chemical Measurement: Unifying Spectroscopy, Chromatography–Mass Spectrometry, Electrochemical Sensing, and Chemical Imaging Via Data Fusion from Molecules to Materials and Devices AI-enabled chemistry is rapidly moving from single-instrument modeling to multimodal chemical measurement, where spectroscopy, chromatography–mass spectrometry, electrochemical sensing, and chemical imaging jointly constrain the same underlying chemical state. This review synthesizes how data fusion (low-, mid-, and high-level) can unify heterogeneous signals from molecules to materials and devices, and why physics guidance is essential for models that remain reliable under matrix effects, instrument drift, and cross-laboratory transfer. We discuss practical fusion architectures, hybrid mechanistic–learning models, and uncertainty-aware inference that converts predictions into decision-ready measurement results. A central theme is that multimodal AI must be evaluated as an analytical procedure: calibration, figures of merit, cross-modality consistency checks, and uncertainty budgets must be reported with the same discipline expected in analytical chemistry. We map common AI tasks by modality (peak deconvolution, spectral unmixing, retention-time prediction, MS annotation, EIS parameter estimation, image segmentation) and show representative case studies spanning pharma/biomedicine, food/environmental sensing, operando catalysis, energy devices, and polymer/materials quality control. Finally, we outline future directions: standardized multimodal benchmarks, interoperable metadata and formats, real-time closed-loop experimentation, greener miniaturized platforms, and trustworthy AI practices that support regulatory acceptance and deployment. Overall, multimodal measurement is converging toward a new paradigm: quantitative, uncertainty-aware, and deployable chemical inference from fused evidence rather than isolated instrument readouts. Sensor fusion ; Chemical space ; Interoperability ; Consistency (knowledge bases) ; Inference ; Metadata ; Trustworthiness Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Sieun Chae | J. H. Kim | Joshua R. Anderson | Sanghyun Hong | Yaser Mike Banad | Hanjong Paik
Source: Nano Convergence (article, 2026-02-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational and experimental pathways to next-generation ultrawide-band-gap oxide semiconductors Ultrawide-band-gap (UWBG) oxide semiconductors are emerging as key platforms for next-generation energy-efficient, high-power, and high-frequency electronics. Further performance gains require the discovery of alternative UWBG systems with band gaps well above 4 eV, while offering intrinsically higher carrier mobility and controllable dopability. This paper highlights recent computational predictions and experimental advances toward such materials, with a focus on dopant activation, carrier density control, and phonon-limited mobility. Although computational screening has uncovered numerous promising candidates, most have yet to be realized as high-quality, single-crystalline thin films suitable for scalable devicefabrication. While epitaxial growth offers a unique platform to probe the intrinsic properties of these materials, the experimental realization is often limited by kinetic and processing constraints, such as nucleation dynamics and precise control of growth parameters. This gap often leads to significant deviations from the theoretical ground-state electrical transport properties. We examine the origins of these discrepancies and highlight integrated approaches, combining high-throughput computation with advanced epitaxial synthesis, to accelerate the development of next-generation UWBG oxide semiconductors. Dopant ; Semiconductor ; Scalability ; Oxide ; Nucleation ; Realization (probability) ; Key (lock) ; Electron mobility Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Acoustic Wave Resonator Technologies
Authors: Nnamdi Ikemefuna Okafor | Nkeiruka N. Igbokwe | Hope Onohuean | Mbuso Faya | Yahya E. Choonara
Source: BioNanoScience (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Driven Development and Characterization of Nanomedicine Nanomedicine has enabled major advances in targeted therapeutics by improving drug bioavailability, precision delivery, and safety profiles. However, the rational design and reproducible synthesis of nanoparticles with tightly controlled physicochemical attributes such as size, morphology, and surface characteristics remain significant challenges due to the complex, nonlinear interplay of formulation and process parameters. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to address these limitations by enabling data-driven optimization, predictive modeling, and automated analysis across nanoparticle synthesis and characterization workflows. Recent advances demonstrate that AI-based models can accurately predict nanoparticle properties, optimize synthesis conditions, interpret high-dimensional characterization data, and forecast biological performance, thereby reducing experimental burden and accelerating translation. This review critically examines current AI and ML strategies applied to nanoparticle synthesis, optimization of key physicochemical attributes, characterization, and biological evaluation for nanomedicine applications. Emphasis is placed on comparative model performance, integration of experimental and computational pipelines, and emerging challenges related to data quality, interpretability, and generalizability. Collectively, this work highlights the transformative potential of AI-enabled nanotechnology while outlining key directions required for its reliable clinical translation. Automated Synthesis Platforms: Integrating AI and ML for Next-Generation Nanomaterials Nanomedicine ; Characterization (materials science) ; Transformative learning ; Process (computing) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanoparticle-Based Drug Delivery
Authors: Ivan Malashin | Dmitry Martysyuk | Vladimir Nelyub | А. С. Бородулин | Andrei Gantimurov | Vadim Tynchenko
Source: Discover Nano (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

A review of applications of machine learning in quantum dots research Machine learning (ML) is increasingly applied in quantum dot (QD) research to support data analysis, device control, materials optimization, sensing, and theoretical modeling. This review surveys recent ML-based approaches across experimental, applied, and computational QD studies, with emphasis on how data-driven methods are embedded within established physical workflows rather than treated as standalone solutions. ML techniques are examined in the context of automated device tuning, high-throughput characterization, synthesis parameter exploration, chemical and biological sensing, photonic and optoelectronic device analysis, and reduced-order modeling of interacting quantum systems. In most cases, ML serves to replace time-consuming fitting procedures, guide experimental sampling, or approximate computationally intensive simulations. Common methodological patterns include supervised learning on limited datasets, transfer learning across device instances, and hybrid approaches incorporating physical constraints into model design or training objectives. Recurrent limitations are also identified, including dataset bias, restricted cross-laboratory transferability, lack of standardized benchmarks, and limited treatment of uncertainty. Rather than positioning ML as a standalone solution, this work frames it as a complementary tool whose reliability and scientific value depend on integration with physical insight, experimental design, and validation protocols. Workflow ; Context (archaeology) ; Reliability (semiconductor) ; Experimental data ; Quantum dot ; Photonics ; Learning curve Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture
Authors: Malin Zollner | Yashar Moshfeghi | Tahereh Nematiaram
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence in the discovery and design of molecular semiconductors: a systematic review AI-driven molecular semiconductor discovery: models learn from molecular datasets to predict spectra and electronic structure, guide photoactive and emissive materials design, and accelerate device optimisation for next-generation optoelectronics. Applications of artificial intelligence ; Key (lock) ; Semiconductor materials Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Chemical and Physical Properties of Materials
Authors: H. I-Lieh Huang | Pingwei Liu
Source: ChemRxiv (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

HHT: A Polymer Property Prediction Framework Leveraging Natural Language Predicting the macroscopic properties of polymers solely from 1D chemical sequences (e.g., SMILES) is inherently an ill-posed problem, as it neglects critical higher-order factors such as chain topology, condensed-state morphology, and processing history. To bridge this "semantic gap" between low-level structural representations and high-level physical functionalities, we propose HHT (Highly Hybrid Text), a novel framework that integrates Large Language Models (LLMs) into polymer property prediction. HHT employs a dual-stream architecture that synergizes implicit topological features extracted by a pre-trained chemical language model (PolyBERT) with explicit mechanistic reasoning generated by a scientific LLM. A dynamic Adaptive Gating Mechanism is introduced to weigh the contribution of structural versus semantic features based on task-specific dependencies. Extensive evaluations on seven benchmark datasets demonstrate that HHT outperforms many baseline models. Crucially, HHT transforms the traditional "black-box" fitting paradigm into a "whitebox" interpretable system. We first validate the model's focus on mechanistic logic via semantic attention visualization, confirming its ability to prioritize physical descriptors over statistical noise. Furthermore, through counterfactual sensitivity analysis, we demonstrate that the model correctly infers property shifts induced by textual morphological perturbations, demonstrating that natural language can serve as a high-dimensional, physically consistent feature modality for materials science. Counterfactual thinking ; Property (philosophy) ; Natural language ; Benchmark (surveying) ; Natural language understanding ; Component (thermodynamics) ; Feature (linguistics) ; Focus (optics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Anshul Yadav | Astha Kamboj | Kavita Poonia
Source: Discover Chemistry. (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

AI-guided approaches in organometallic research: cognition in chemistry Organometallic chemistry plays a crucial role in catalysis, energy innovation, and medical advancements. However, rational design of metal-ligand systems remains challenging due to complex electronic structures. This review explores how artificial intelligence, alongside density functional theory, is reshaping this field. By training machine learning models on DFT data, researchers have accelerated targeted discovery, and mechanistic analysis with great efficiency. The use of generative algorithms and autonomous lab systems has expanded our ability to navigate chemical spaces rapidly; however, the extent of this progress strongly depends on the availability of high-quality datasets and the incorporation of domain-specific chemical constraints. Through case studies, ranging from green electro catalysts to Schiff base-derived antimicrobial and anticancer agents, this review highlights how AI balances theoretical precision with real-world applicability. Current challenges such as data scarcity and model interpretability are discussed, along with emerging directions aimed at advancing sustainable and intelligent organometallic design. Interpretability ; Scarcity ; Cognition ; Generative grammar ; Cognitive computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Pallavi B. Mote | Mahesh S. Jangale | Kiran B. Kharad
Source: International Journal of Engineering Research and Science & Technology (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Chemistry: A Comprehensive Review of Methods, Applications, and Future Perspectives Artificial intelligence (AI) is increasingly reshaping the chemical sciences by supporting data-centric discovery, predictive simulations, and automated laboratory workflows. Contemporary AI approaches such as machine learning, deep neural networks, graph-based models, and natural language processing enable the systematic interpretation of intricate chemical datasets and molecular structures. These technologies have found extensive use in areas including pharmaceutical development, reaction forecasting, computer-assisted retrosynthesis, materials engineering, catalyst design, and spectral data analysis. The combination of AI with high-throughput screening techniques and robotic platforms has accelerated the emergence of autonomous, self-operating laboratories. Nevertheless, important limitations persist, particularly regarding data reliability, transparency of model decisions, reproducibility of results, and the adaptability of models across different chemical domains. Ongoing progress in algorithm development, dataset standardization, and collaborative interaction between human expertise and intelligent systems is anticipated to foster more sustainable, efficient, and innovative advancements in chemical research. Adaptability ; Transparency (behavior) ; Applications of artificial intelligence ; Interpretation (philosophy) ; Artificial neural network ; Relevance (law) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: H. I-Lieh Huang | Pingwei Liu
Source: ChemRxiv (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

HHT: A Polymer Property Prediction Framework Leveraging Natural Language Predicting the macroscopic properties of polymers solely from 1D chemical sequences (e.g., SMILES) is inherently an ill-posed problem, as it neglects critical higher-order factors such as chain topology, condensed-state morphology, and processing history. To bridge this "semantic gap" between low-level structural representations and high-level physical functionalities, we propose HHT (Highly Hybrid Text), a novel framework that integrates Large Language Models (LLMs) into polymer property prediction. HHT employs a dual-stream architecture that synergizes implicit topological features extracted by a pre-trained chemical language model (PolyBERT) with explicit mechanistic reasoning generated by a scientific LLM. A dynamic Adaptive Gating Mechanism is introduced to weigh the contribution of structural versus semantic features based on task-specific dependencies. Extensive evaluations on seven benchmark datasets demonstrate that HHT outperforms many baseline models. Crucially, HHT transforms the traditional "black-box" fitting paradigm into a "whitebox" interpretable system. We first validate the model's focus on mechanistic logic via semantic attention visualization, confirming its ability to prioritize physical descriptors over statistical noise. Furthermore, through counterfactual sensitivity analysis, we demonstrate that the model correctly infers property shifts induced by textual morphological perturbations, demonstrating that natural language can serve as a high-dimensional, physically consistent feature modality for materials science. Counterfactual thinking ; Property (philosophy) ; Natural language ; Benchmark (surveying) ; Natural language understanding ; Component (thermodynamics) ; Feature (linguistics) ; Focus (optics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jinfeng Zhang | António Benjamim Mapossa | Yuxin Liu | Uttandaraman Sundararaj
Source: Applied Sciences (article, 2026-03-01)
Field: Materials Science — Polymers and Plastics

AI-Driven Design of Sustainable Flame-Retardant Biodegradable Polymer Composites The growing demand for lightweight, high-performance, and fire-safe polymer materials has accelerated research into advanced flame-retardant composites. Traditional experimental approaches to designing sustainable flame-retardant biodegradable polymer composites still rely heavily on empirical formulation and iterative testing, which are time-consuming and costly, and they often struggle to capture the coupled effects of chemical composition, processing conditions, and material performance. Recent advances in artificial intelligence (AI) provide opportunities to address these challenges by learning formulation–structure–performance relationships from curated datasets and by translating materials chemistry and flame-retardant mechanisms into data-ready descriptors and targets. This review summarizes recent progress of AI-assisted approaches to design sustainable flame-retardant biodegradable polymer composites, emphasizing machine learning, deep learning, and active learning methods for predicting and optimizing key fire performance metrics, including limiting oxygen index and heat release-related parameters. Biodegradable-specific limitations, including narrow processing window, thermal degradation, and moisture sensitivity, are discussed in the content of descriptor selection and constraint-aware optimization, together with the role of interpretable/explainable models in supporting experimentally actionable guidance. Current challenges such as limited data availability, protocol variability, model transferability, and interpretability are highlighted, and emerging solutions, including data harmonization, standardized fire testing, and physics-informed models are outlined. AI-assisted strategies are expected to play a central role in accelerating efficient, sustainable, halogen-free, and performance-driven development of next-generation flame-retardant biodegradable polymer composites. Interpretability ; Limiting ; Biodegradable polymer ; Empirical modelling ; Polymer ; Selection (genetic algorithm) Flame retardant materials and properties ; Fire dynamics and safety research ; Polymer composites and self-healing