jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Changmao Chen | Yu Tang | Yi Yang | Xiuhua Guo | Yubi Zhou | Jie Wang
Source: International Journal of Food Science (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Amino Acid and Mineral Element Analysis and Quality Evaluation of Different Color Fruits of <i>Nitraria</i> in Qinghai Only three species of <i>Nitraria</i> fruits are distributed in Qinghai Province, which serve as important berry resources in the desert ecosystem of Qinghai. These fruits are rich in nutrients; however, the differences in nutritional characteristics and quality evaluation among different species and fruit colors have not been systematically clarified. To explore the nutritional quality of <i>Nitraria</i> fruits in Qinghai, this study investigated red and purple fruits of <i>Nitraria roborowskii</i>, <i>N. tangutorum</i>, and <i>N. sibirica</i>. The contents of 17 amino acids and 27 mineral elements were determined, analyzed by multivariate chemometric methods, and compared with existing research results for verification. The results showed that the contents of total amino acids, essential amino acids, nonessential amino acids, and total mineral elements in <i>N. sibirica</i> were significantly higher than those in <i>N. roborowskii</i> and <i>N. tangutorum</i>. Among them, the proportion of essential amino acids in purple fruits of <i>N. sibirica</i> reached 0.22, close to the FAO ideal protein standard, and it was rich in functional mineral elements such as zinc and iron, showing high nutritional value. These results are consistent with the existing research on the nutritional specialization of desert berries. Fruit color exerted differential effects on nutritional components: nonessential amino acids and total amino acids were positively affected in red fruits, while mineral element contents were not significantly influenced by color, which agrees with studies on metabolic competition related to anthocyanin biosynthesis. Synergistic accumulation was observed between amino acids and mineral elements. Species inheritance was the dominant factor determining nutritional quality. Al, Val, Ca, V, and Cr were screened as core signature components, which improved the indicator system for the quality evaluation of <i>Nitraria</i> plants. Comprehensive evaluation demonstrated that <i>N. sibirica</i> possessed the best overall nutritional quality, and red fruits were generally superior to purple fruits, with purple fruits of <i>N. sibirica</i> exhibiting the optimal nutritional quality. The results of this study provide a theoretical basis for the precise development of <i>Nitraria</i> germplasm resources in Qinghai, the breeding of superior genotypes, and the research and development of distinctive functional foods from desert plants. Amino acid ; Mineral ; Anthocyanin ; Protein quality ; Berry ; Essential amino acid ; Zinc Advanced Synthetic Organic Chemistry ; Plant chemical constituents analysis ; Phytochemistry and biological activities of Ficus species
Authors: Mikhail A. Zarudskikh | S. А. Beznosyuk
Source: Izvestiya of Altai State University (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Application оf Machine Learning in Physicochemistry of Active Substances The paper presents the latest advances in machine learning for solving pressing problems in the physicochemistry of active substances. It discusses how data mining and neural network models help overcome the limitations of traditional methods. It reviews systems capable of detecting unknown reactions in archived mass spectrometry data (MEDUSA), predicting molecular structures from mass spectra (Spec2Mol), and generating spectra for new compounds, thereby addressing the shortage of reference samples. In quantum chemistry, the success of hybrid methods (AIQM1) is demonstrated. These methods combine quantum mechanical calculations with neural networks to achieve high accuracy while significantly reducing computational costs. The application of machine learning in materials science for the automatic analysis of microscopic images to study defects and the organization of nanoparticles is also discussed. In the context of organic synthesis and drug design, approaches using machine learning to predict the biological activity of molecules (MI-QSAR) and the yields of chemical reactions (Yield-BERT) are presented. In conclusion, it is emphasized that machine learning is a powerful tool that complements classical approaches and accelerates scientific discoveries. Artificial neural network ; Economic shortage ; Context (archaeology) ; Active learning (machine learning) ; Quantum chemical ; Quantum Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Shujian Zhang | Sihan Wu | Hang Su | Hanqiang Han | Jinjin Li
Source: Materials Today Electronics (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

AI-driven multi-property prediction and manufacturability-aware design for electronic materials and devices Electronics ; Electronic materials ; Electronic systems ; Electronic equipment ; Work (physics) Machine Learning in Materials Science ; Physical Unclonable Functions (PUFs) and Hardware Security ; Advanced Neural Network Applications
Authors: Zihui Li | Shendong Tan | Chaoyuan Ji | Junhong Liao | Yaoshu Xie | Lu Jiang | Tingzheng Hou
Source: Materials Today Energy (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Reinforcing transformer-based polymer electrolyte discovery through high-fidelity fine-tuning and application-oriented screening Polymer ; Electrolyte ; Branching (polymer chemistry) ; Polymer electrolytes ; Copolymer ; Ionic bonding ; Electrochemistry ; Arylene ; Ionic liquid Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Block Copolymer Self-Assembly
Authors: Kewei Zhu | Cameron Wilson | Bartosz Mazur | Yi Li | Ashleigh M. Chester | Peyman Z. Moghadam
Source: ChemRxiv (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning Systematic chemical names, such as IUPAC-style nomenclature for metal-organic frameworks (MOFs), contain rich structural and compositional information in a standardized textual format. Here we introduce ReadMOF, which is, to our knowledge, the first nomenclature-free machine learning framework that leverages these names to model structure-property relationships without requiring atomic coordinates or connectivity graphs. By employing pretrained language models, ReadMOF converts systematic MOF names from the Cambridge Structural Database (CSD) into vector embeddings that closely represent traditional structure-based descriptors. These embeddings enable applications in materials informatics, including property prediction, similarity retrieval, and clustering, with performance comparable to geometry-dependent methods. When combined with large language models, ReadMOF also establishes chemically meaningful reasoning ability with textual input only. Our results show that structured chemical language, interpreted through modern natural language processing techniques, can provide a scalable, interpretable, and geometry-independent alternative to conventional molecular representations. This approach opens new opportunities for language-driven discovery in materials science. Nomenclature ; Property (philosophy) ; Similarity (geometry) ; Natural language ; Taxonomy (biology) ; Support vector machine Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Xinyao Li | Hangwei Qian | Jingjing Li | Ivor Tsang
Source: Open MIND (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

Learning ORDER-Aware Multimodal Representations for Composite Materials Design Artificial intelligence (AI) has shown remarkable success in materials discovery and property prediction, particularly for crystalline and polymer systems where material properties and structures are dominated by discrete graph representations. Such graph-central paradigm breaks down on composite materials, which possess continuous and nonlinear design spaces that lack well-defined graph structures. General composite descriptors, e.g., fiber volume and misalignment angle, cannot fully capture the fiber distributions that fundamentally determine microstructural characteristics, necessitating the integration of heterogeneous data sources through multimodal learning. Existing alignment-oriented multimodal frameworks have proven effective on abundant crystal or polymer data under discrete, unique graph-property mapping assumptions, but fail to address the highly continuous composite design space under extreme data scarcity. In this work, we introduce ORDinal-aware imagE-tabulaR alignment (ORDER), a multimodal pretraining framework that establishes ordinality as a core principle for composite material representations. ORDER ensures that materials with similar target properties occupy nearby regions in the latent space, which effectively preserves the continuous nature of composite properties and enables meaningful interpolation between sparsely observed designs. We evaluate ORDER on a public Nanofiber-enforced composite dataset and an internally curated dataset that simulates the construction of carbon fiber T700 with diverse fiber distributions. ORDER achieves consistent improvements over state-of-the-art multimodal baselines across property prediction, cross-modal retrieval, and microstructure generation tasks. Composite number ; Interpolation (computer graphics) ; Fiber ; Graph ; Property (philosophy) ; Exploit ; Data-driven Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Epoxy Resin Curing Processes
Authors: Xinyao Li | Hangwei Qian | Jingjing Li | Ivor Tsang
Source: ArXiv.org (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Learning ORDER-Aware Multimodal Representations for Composite Materials Design Artificial intelligence (AI) has shown remarkable success in materials discovery and property prediction, particularly for crystalline and polymer systems where material properties and structures are dominated by discrete graph representations. Such graph-central paradigm breaks down on composite materials, which possess continuous and nonlinear design spaces that lack well-defined graph structures. General composite descriptors, e.g., fiber volume and misalignment angle, cannot fully capture the fiber distributions that fundamentally determine microstructural characteristics, necessitating the integration of heterogeneous data sources through multimodal learning. Existing alignment-oriented multimodal frameworks have proven effective on abundant crystal or polymer data under discrete, unique graph-property mapping assumptions, but fail to address the highly continuous composite design space under extreme data scarcity. In this work, we introduce ORDinal-aware imagE-tabulaR alignment (ORDER), a multimodal pretraining framework that establishes ordinality as a core principle for composite material representations. ORDER ensures that materials with similar target properties occupy nearby regions in the latent space, which effectively preserves the continuous nature of composite properties and enables meaningful interpolation between sparsely observed designs. We evaluate ORDER on a public Nanofiber-enforced composite dataset and an internally curated dataset that simulates the construction of carbon fiber T700 with diverse fiber distributions. ORDER achieves consistent improvements over state-of-the-art multimodal baselines across property prediction, cross-modal retrieval, and microstructure generation tasks. Composite number ; Interpolation (computer graphics) ; Fiber ; Graph ; Property (philosophy) ; Exploit ; Data-driven Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Epoxy Resin Curing Processes
Authors: Ari Harrison
Source: ChemRxiv (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Molecular Targets Shape Reasoning Topology: Bidirectional Validation of Chemistry-Inspired Bond Classification for AI Reasoning Chains Recent work classified AI reasoning chains using chemistry-inspired bond types, namely covalent (deductive), hydrogen (reflective), and van der Waals (exploratory), showing that structural stability of reasoning predicts training data quality (Direction A). We test the complementary direction: does the molecular target being reasoned about shape the topology of reasoning itself (Direction B)? Across 478 reasoning traces from five ADMET endpoints, we find that it does, with large effect sizes (Cohen's d = 0.94 and 1.75, both p < 10 −9, surviving Bonferroni correction). We collect chain-of-thought traces from Claude Opus 4.6 performing ADMET property predictions across CYP2D6, CYP3A4, CYP2C9, hERG, and P-gp, using live computational chemistry tools via the Model Context Protocol. A four-signal Semantic Bond Classifier, using sentence embeddings, discourse markers, positional distance, and an energy proxy, classifies reasoning bonds without requiring attention weights from open-source models. Of five pre-registered hypotheses, aggregate reasoning structure does not predict prediction accuracy (H1-H4, all p > 0.12). However, molecular targets strongly determine reasoning topology (H5): CYP2D6 reasoning (2D substructure-driven) shows significantly more covalent bonds than hERG reasoning (3D conformation-dependent), while hERG reasoning shows more van der Waals bonds than CYP2D6. Classifier ablation reveals semantic similarity as the dominant signal (∆ = 13.2 percentage points when removed). These results close a bidirectional loop: chemistry metaphors explain reasoning structure, and chemistry problems shape reasoning structure. The finding that molecular target type determines reasoning topology, with large, interpretable effect sizes, has implications for understanding how AI systems adapt their problem-solving approach to domain-specific demands. Context (archaeology) ; Automated reasoning ; Psychology of reasoning ; Model-based reasoning ; Property (philosophy) ; Non-monotonic logic ; Deductive reasoning ; Semantic reasoner Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Karol Baran | Adam Kloskowski
Source: Journal of Chemical Information and Modeling (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Exploring Chemoinformatics Aspects of Few-Shot Meta-Learning by Example of an Infinite Dilution Activity Coefficient in Ionic Liquid Prediction Collection of substantial training sets for structure-property modeling often poses a significant challenge, especially for niche sorption systems. This study provides computational experiments with meta-learning that presents a compelling solution to the data scarcity challenge in molecular property prediction. Our analysis utilized infinite dilution activity coefficients for several ionic liquid-solute systems, with coefficient prediction for systems with a particular solute constituting a task. The systems were modeled predominantly using model-agnostic meta-learning (MAML), supported by a study on Reptile and its modified variants. The obtained results provide promising insights into training MAML models by expanding the adaptation set size. Metrics such as <i>R</i><sup>2</sup>, RMSE, and MAE indicate comparable performance to graph neural networks, even when trained on only 64 or 128 data points. The versatility of the fine-tuned models suggests that, in certain cases, performance on a single task may be achieved at the expense of reduced model versatility (catastrophic forgetting). The similarity between the test and training tasks (as approximated by Tanimoto similarity of the solutes' molecules) was identified as a factor affecting performance on the test task. Consequently, Task Similarity-Aware Reptile (TSA-Reptile) was proposed to target those dissimilar tasks. This novel method scales the loss function by similarity to the closest training task. It was shown to outperform MAML on out-of-distribution tasks. Beyond providing the comparative analysis of meta-learning and traditional deep learning, potential strengths of both MAML and TSA-Reptile are discussed. Similarity (geometry) ; Cheminformatics ; Task (project management) ; Dilution ; Training set ; Artificial neural network ; Test set ; Set (abstract data type) ; Function (biology) Machine Learning in Materials Science ; Ionic liquids properties and applications ; Computational Drug Discovery Methods
Authors: Emily Zhou | Charles Zhou
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-01)
Field: Materials Science — Materials Chemistry

Spectral Encoding of Protein Binding Pockets via Deterministic Oscillatory Superposition: Toward an Audio Modality for Molecular Representation Protein structure prediction has advanced dramatically, yet a fundamental gap persists between static three-dimensional coordinates and the dynamic, oscillatory reality of molecules in cellular environments. Current computational representations of protein binding pockets—whether graphbased, image-based, or sequence-derived—operate in modalities that lack an intrinsic temporal dimension, limiting their capacity to encode the distributed spatial–chemical organization and conformational dynamics that govern molecular recognition. Building on our molecular sonification framework (Zhou and Zhou, 2026, 2014), we introduce a deterministic, training-free method that represents protein binding pockets as audio-like waveforms: structural elements are mapped to localized oscillatory functions whose superposition produces interference patterns encoding collective pocket organization. Spectral transformation of these waveforms yields compact embedding vectors. Across 79 protein–ligand co-crystal structures spanning four diverse protein families, the method achieves 77.2% leave-one-out classification accuracy (Cohen’s d = 0.748), a 2.4-fold effect size improvement over conventional aggregation using identical input features. Within-family kinase subfamily discrimination (p = 2.6°ø10−7) and conformational state sensitivity to DFG-in/out rearrangements (p = 0.014) demonstrate that the encoding captures higher-order structural relationships inaccessible to feature pooling. Together with our companion work on multi-modal molecular sonification for small-molecule property prediction (Zhou and Zhou, 2026), these results establish audio as a new modality for molecular AI and lay groundwork for leveraging pretrained audio models in structural bioinformatics and drug discovery. Encoding (memory) ; Superposition principle ; Embedding ; Representation (politics) ; Feature (linguistics) ; Modality (human–computer interaction) ; ENCODE ; Property (philosophy) ; Protein structure ; Limiting Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Machine Learning in Bioinformatics
Authors: Emily Zhou | Charles Zhou
Source: Open MIND (preprint, 2026-03-01)
Field: Materials Science — Materials Chemistry

Spectral Encoding of Protein Binding Pockets via Deterministic Oscillatory Superposition: Toward an Audio Modality for Molecular Representation Protein structure prediction has advanced dramatically, yet a fundamental gap persists between static three-dimensional coordinates and the dynamic, oscillatory reality of molecules in cellular environments. Current computational representations of protein binding pockets—whether graphbased, image-based, or sequence-derived—operate in modalities that lack an intrinsic temporal dimension, limiting their capacity to encode the distributed spatial–chemical organization and conformational dynamics that govern molecular recognition. Building on our molecular sonification framework (Zhou and Zhou, 2026, 2014), we introduce a deterministic, training-free method that represents protein binding pockets as audio-like waveforms: structural elements are mapped to localized oscillatory functions whose superposition produces interference patterns encoding collective pocket organization. Spectral transformation of these waveforms yields compact embedding vectors. Across 79 protein–ligand co-crystal structures spanning four diverse protein families, the method achieves 77.2% leave-one-out classification accuracy (Cohen’s d = 0.748), a 2.4-fold effect size improvement over conventional aggregation using identical input features. Within-family kinase subfamily discrimination (p = 2.6°ø10−7) and conformational state sensitivity to DFG-in/out rearrangements (p = 0.014) demonstrate that the encoding captures higher-order structural relationships inaccessible to feature pooling. Together with our companion work on multi-modal molecular sonification for small-molecule property prediction (Zhou and Zhou, 2026), these results establish audio as a new modality for molecular AI and lay groundwork for leveraging pretrained audio models in structural bioinformatics and drug discovery. Encoding (memory) ; Superposition principle ; Embedding ; Representation (politics) ; Feature (linguistics) ; Modality (human–computer interaction) ; ENCODE ; Property (philosophy) ; Protein structure ; Limiting Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Machine Learning in Bioinformatics
Authors: Pan Tao | Pengzhan Wang | Dushuo Feng | Qiyue Wang | Fangyuan Li | Daishun Ling
Source: Chemistry - A European Journal (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Artificial Intelligence‐Guided Design of Fluorescent Probes for Biomedical Applications Fluorescence imaging (FLI) has become a cornerstone for biomedical research owing to its non-invasive, high sensitivity, and exceptional spatial resolution. The effectiveness of FLI is primarily governed by the properties of fluorescent probes, including photophysical characteristics, targeting capabilities, and responsiveness. However, current probe development in FLI relies on empirical trial-and-error strategies or quantum-chemical calculations, both of which are time-consuming, labor-intensive, and often insufficient for precisely deciphering complex structure-property-function relationships. Recent advances in artificial intelligence (AI) have opened a new avenue for rational probe design through rapid property prediction, high-throughput molecular screening, and performance-guided inverse design. Nevertheless, a systematic and comprehensive review elucidating AI-guided design of fluorescent probes in biomedical applications is still lacking. This review focuses on recent advances in AI-guided design and performance optimization of fluorescent probes for bioimaging applications. First, the conceptual workflow underlying AI-based predictive frameworks is elucidated. Second, the impact of AI on optimizing key probe properties-including optical performance, targeting, and responsive capabilities-is thoroughly elaborated. Third, the biomedical applications of AI-guided probes in imaging, sensing, and therapy are reviewed. Finally, the current challenges and future perspectives to accelerate the development of AI-guided fluorescent probe design are briefly discussed. Rational design ; Workflow ; Fluorescence ; Design elements and principles ; Optical imaging Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Nanoplatforms for cancer theranostics