jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuan Li | Zhanke Zhou | Zongze Li | Jiangchao Yao | Yu Rong | Lu Zhang | Bo Han
Source: ArXiv.org (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning Large language models (LLMs) benefit substantially from supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) in reasoning tasks. However, these recipes perform poorly in instruction-based molecular optimization, where each data point typically provides only a single optimized reference molecule and no step-by-step optimization trajectory. We reveal that answer-only SFT on the reference molecules collapses reasoning, and RLVR provides sparse feedback under similarity constraints due to the model's lack of effective exploration, which slows learning and limits optimization. To encourage the exploration of new molecules while balancing the exploitation of the reference molecules, we introduce Reference-guided Policy Optimization (RePO), an optimization approach that learns from reference molecules without requiring trajectory data. At each update, RePO samples candidate molecules with their intermediate reasoning trajectories from the model and trains the model using verifiable rewards that measure property satisfaction under similarity constraints in an RL manner. Meanwhile, it applies reference guidance by keeping the policy's intermediate reasoning trajectory as context and training only the answer in a supervised manner. Together, the RL term promotes exploration, while the guidance term mitigates reward sparsity and stabilizes training by grounding outputs to references when many valid molecular edits exist. Across molecular optimization benchmarks, RePO consistently outperforms SFT and RLVR baselines (e.g., GRPO), achieving improvements on the optimization metric (Success Rate $\times$ Similarity), improving balance across competing objectives, and generalizing better to unseen instruction styles. Our code is publicly available at https://github.com/tmlr-group/RePO. Context (archaeology) ; Verifiable secret sharing ; Metric (unit) ; Fidelity ; Term (time) ; Reinforcement learning ; Trajectory ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Poonkodi Sathiyamoorthy
Source: Journal on Applied and Chemical Physics (article, 2026-03-24)
Field: Materials Science — Polymers and Plastics

Autonomous Self-Healing Polymer Composites: Harnessing Nature’s Inspiration for Resilient Engineering Materials using COPRAS Method Autonomic mending within polymer composites has attracted considerable attention in recent times owing to its potential to transform material design and engineering applications. This thorough review delves into the latest advancements in the domain, with a particular focus on the mechanisms, materials, and practical applications of autonomic healing in polymer composites. Recent strides in material synthesis, fabrication methodologies, and characterization techniques are also scrutinized to provide a comprehensive evaluation of healing efficiency and performance. Additionally, the review surveys a spectrum of applications where autonomic healing holds promise, spanning aerospace, automotive, construction, and biomedical sectors, accentuating its potential real-world impact. Addressing challenges and future trajectories in the field, such as scalability, cost-effectiveness, and environmental sustainability, serves to steer forthcoming research and development endeavours. This review serves as a valuable resource for researchers, engineers, and practitioners striving to propel the field of autonomic healing in polymer composites towards new horizons. COPRAS, short for Complex Proportional Assessment, is a multifaceted methodology within the realm of Multi-Criteria Decision Making (MCDM). It serves as a structured approach for assessing and prioritizing alternatives based on numerous criteria. Widely utilized across various sectors including engineering, management, environmental studies, and social sciences, COPRAS methodology furnishes decision-makers with a systematic framework to navigate intricate decision scenarios by factoring in both qualitative and quantitative considerations. Microcapsule-based Healing, Vascular Network, Intrinsic Healing, Thermoplastic Healing and Stimuli-Responsive Healing. Healing Efficiency, Healing Rate, Healing Reversibility, Compatibility with Base Polymer and Effect on Mechanical Properties. Ranking of Autonomic Healing of Polymer Composites. Intrinsic Healing is got the first rank whereas is the Microcapsule-based Healing is having the Lowest rank. Factoring ; Resource (disambiguation) ; Ranking (information retrieval) ; Resource efficiency ; Realm ; Broad spectrum Polymer composites and self-healing ; Microbial Applications in Construction Materials ; Hydrogels: synthesis, properties, applications
Authors: Olga Bunkova | Lorenzo Di Fruscia | Sophia Rupprecht | Schweidtmann, Artur M. | Marcel J. T. Reinders | Jana M. Weber
Source: ArXiv.org (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval Large Language Models (LLMs) can aid synthesis planning in chemistry, but standard prompting methods often yield hallucinated or outdated suggestions. We study LLM interactions with a reaction knowledge graph by casting reaction path retrieval as a Text2Cypher (natural language to graph query) generation problem, and define single- and multi-step retrieval tasks. We compare zero-shot prompting to one-shot variants using static, random, and embedding-based exemplar selection, and assess a checklist-driven validator/corrector loop. To evaluate our framework, we consider query validity and retrieval accuracy. We find that one-shot prompting with aligned exemplars consistently performs best. Our checklist-style self-correction loop mainly improves executability in zero-shot settings and offers limited additional retrieval gains once a good exemplar is present. We provide a reproducible Text2Cypher evaluation setup to facilitate further work on KG-grounded LLMs for synthesis planning. Code is available at https://github.com/Intelligent-molecular-systems/KG-LLM-Synthesis-Retrieval. Knowledge graph ; Language model ; Graph ; Hallucinating ; Scheme (mathematics) ; Code (set theory) ; Path (computing) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Scientific Computing and Data Management
Authors: Thomas Heine | Dinga A.D. Wonanke | Antonio Longa | Asha Pankajakshan | Ogar Joseph | Lauri Himanen | Alvin Ladine | José P. Márquez | Matthew Addicoat | Deborah L. Crittenden | Markus Scheidgen | Pietro Liò | Stefanie Dehnen | Christof Woell
Source: Research Square (preprint, 2026-02-02)
Field: Chemistry — Inorganic Chemistry

FAIR-MOFs: Structure-centred synthesis inference from three-dimensional structures of metal-organic frameworks Scalability ; Inference ; Synthetic data ; Expressive power ; Graph ; Artificial neural network ; Categorization ; Retrosynthetic analysis Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ilia Kevlishvili | Devmin Dorabawila
Source: ChemRxiv (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Taming T-REX: A Canonical Language for Geometry-Aware Generative Design of Transition-Metal Complexes Canonical string representations have transformed organic cheminformatics, yet transition-metal complexes (TMCs) lack an equivalent that captures coordination geometry, stereochemistry, and donor topology. We introduce Trans-pair Relations EXpression (T-REX), a canonical line notation encoding geometry, topology, and metal-centered chirality (@/@@, Δ/Λ) via trans-pair maps. Applied to 63,375 DFT-optimized structures from the tmQMg dataset, T-REX identifies five distinct isomer classes (coordination, enantiomeric, linkage, hemilabile, and geometric) and reveals that fewer than 1.2% of complexes capable of stereoisomerism are resolved as such in crystallographic data. Combinatorial enumeration expands these parent structures into 149,228 unique topological variants; modular ligand substitution generates millions of additional candidates. Across one bond-only baseline and four geometry-aware architectures, encoding the T-REX coordination map consistently improves prediction of HOMO, LUMO, gap, and dipole moment. Dipole moment shows the largest gains (R² = 0.845 vs. 0.715 for the baseline), and three architecturally distinct models with a direct coordination-sphere readout achieve equivalent performance, confirming that T-REX topology, not architecture choice, drives the improvement. Geometry-aware models reach equivalent accuracy with roughly four times less training data, positioning T-REX as both an interoperable data format and an ML-ready representation for transition-metal chemistry. Encoding (memory) ; String (physics) ; Representation (politics) ; Notation ; Substitution (logic) ; Modular design ; Multiset ; Chirality (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Bed Poudel | Aman Nanda | Debjyoti Bhattacharya | Nayeon Kang | Shankar Kunwar | Shashank Priya | Jungho Ryu | Wesley Reinhart | Michael Lanagan
Source: Research Square (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Design and Realization of High Performance Textured Lead-Free Piezoelectric Ceramics through Human-AI Collaboration Realization (probability) ; Coupling (piping) ; Piezoelectricity ; Power (physics) ; Sintering ; Property (philosophy) ; Ceramic Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Piezoelectric Materials
Authors: Mengfan Wu | Junfu Tan | Yu Zhu | Jie Ren
Source: arXiv (Cornell University) (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

KappaFormer: Physics-aware Transformer for lattice thermal conductivity via cross-domain transfer learning Machine learning has been widely used for predicting material properties. However, efficient prediction of lattice thermal conductivity ($κ_\mathrm{L}$) remains a long-standing challenge, primarily due to the scarcity of high-quality training data. Here we introduce KappaFormer, a physics-aware Transformer architecture that embeds the harmonic-anharmonic decomposition of $κ_\mathrm{L}$ within the network. KappaFormer comprises a harmonic branch pre-trained on large-scale elastic property data and an anharmonic branch fine-tuned on limited experimental $κ_\mathrm{L}$ data, enabling effective knowledge transfer and enhanced generalization. High-throughput screening with KappaFormer identifies multiple candidates with ultralow $κ_\mathrm{L}$, which are further confirmed by first-principles calculations. Physics interpretability further elucidates the vibrational mechanisms governing thermal transport suppression, linking structural motifs to strong anharmonicity. This study provides a generalizable framework for physics-guided machine learning to accelerate the discovery of new materials. Interpretability ; Anharmonicity ; Thermal conductivity ; Lattice (music) ; Transfer of learning ; Thermal ; Thermal conduction Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Source: International Journal on Cybernetics & Informatics (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

THE ROLE OF ARTIFICIAL INTELLIGENCE IN CHEMISTRY: ACCELERATING DISCOVERY AND INNOVATION Artificial intelligence (AI) is transforming chemistry by accelerating molecular simulations, exploring chemical spaces, and automating experimental design. This paper reviews AI applications in chemistry, highlighting machine learning and deep learning techniques for predicting molecular properties, retrosynthesis planning, and reaction optimization. AI integration enables novel molecule discovery, synthesis route optimization, and enhanced research efficiency. Key applications include drug discovery acceleration, materials design, and green chemistry advancement. The convergence of AI with experimental chemistry promises continued innovation in molecular design and chemical process optimization Retrosynthetic analysis ; Process (computing) ; Drug discovery ; Key (lock) ; Convergence (economics) ; Applications of artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Muhammad Qamar Bilal | Faisal Latif | Muhammad Hasnain | Muhammad Aamir Ali | Raziya Nadeem
Source: Preprints.org (preprint, 2026-02-27)
Field: Chemistry — Inorganic Chemistry

From Screening to Generative Design: Advances in ML- Assisted MOFs for Carbon Capture The accelerating climate crisis, driven by annual CO₂ emissions exceeding 37 billion metric tons, necessitates the rapid advancement of Carbon Capture and Storage (CCS) and Direct Air Capture (DAC) technologies. Metal–Organic Frameworks (MOFs) have emerged as highly promising sorbents due to their tunable pore architectures and exceptional surface areas; however, exploration of their vast chemical design space remains computationally prohibitive. This review systematically examines the expanding role of Machine Learning (ML) in accelerating CO₂ capture research within MOFs. Using a structured evaluation protocol, we assess state-of-the-art models across four key dimensions: predictive performance, descriptor physical relevance, mechanistic interpretability, and process-level applicability. Recent advances in Machine Learning Interatomic Potentials (MLPs) demonstrate that framework flexibility significantly influences adsorption thermodynamics and diffusivity, challenging conventional rigid-lattice assumptions. Generative approaches—including Deep Reinforcement Learning and transformer-based architectures—enable inverse design of high affinity frameworks, while physics-informed descriptor engineering improves predictive accuracy across pressure regimes (R² &amp;gt; 0.90). Importantly, the field is transitioning from isolated property prediction toward multiscale, process-integrated optimization, where ML models couple material features with industrial performance metrics such as CO₂ purity and recovery in pressure swing adsorption systems. Collectively, these developments indicate that future progress will depend on physics informed, interpretable architectures capable of bridging molecular-scale discovery with experimentally robust and water-stable materials suitable for industrial deployment. Generative grammar ; Flexibility (engineering) ; Bridging (networking) ; Metric (unit) ; Field (mathematics) ; Generative model ; Chemical space ; Uncertainty quantification Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Chenlei Xu | Tianhao Su | Jie Xiong | Yue Wu | Shuya Dong | Tian Jiang | Meilin He | Shuai Chen | Tong‐Yi Zhang
Source: npj Computational Materials (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

KAN-enhanced contrastive learning: the accelerator of crystal structure identification from XRD patterns Accurate crystal structure determination underpins materials discovery, yet powder X-ray diffraction (XRD) analysis still depends on expert-driven, iterative fitting that limits scalability for high-throughput and autonomous experiments. We introduce XRD-Crystal Contrastive Pretraining (XCCP), a physics-guided contrastive learning framework that aligns PXRD patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry inference. XCCP employs a dual-expert XRD encoder with a Kolmogorov-Arnold Network (KAN) projection head. A low-angle branch captures long-length-scale signatures, while a wide-angle branch encodes dense, symmetry-governed fingerprints. Attribution and perturbation analyses show that the KAN head concentrates evidence on physically meaningful Bragg reflections rather than background-dominated regions, improving robustness to peak-shape variations. We further introduce similarity-based confidence scores to flag potentially unreliable predictions in open-set settings. Without elemental priors, XCCP achieves 46.42% top-1 accuracy for structure retrieval and 60.85% accuracy for space-group identification. When chemical composition is available for elemental pre-screening, performance increases to 88.98% and 93.39%, respectively. XCCP also generalizes to compositionally similar multi-principal element alloys and enables zero-shot transfer to experimental patterns. These results establish XCCP as an interpretable, confidence-aware, and scalable paradigm for XRD analysis, enabling high-throughput screening, rapid candidate shortlisting, and integration with autonomous laboratory workflows. Embedding ; Scalability ; Diffraction ; Projection (relational algebra) ; Identification (biology) ; Encoder ; Space (punctuation) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Sonakshi Gupta | Akhlak Mahmood | Wei Xiong | Rampi Ramprasad
Source: arXiv (Cornell University) (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Retrieval Augmented Generation of Literature-derived Polymer Knowledge: The Example of a Biodegradable Polymer Expert System Polymer literature contains a large and growing body of experimental knowledge, yet much of it is buried in unstructured text and inconsistent terminology, making systematic retrieval and reasoning difficult. Existing tools typically extract narrow, study-specific facts in isolation, failing to preserve the cross-study context required to answer broader scientific questions. Retrieval-augmented generation (RAG) offers a promising way to overcome this limitation by combining large language models (LLMs) with external retrieval, but its effectiveness depends strongly on how domain knowledge is represented. In this work, we develop two retrieval pipelines: a dense semantic vector-based approach (VectorRAG) and a graph-based approach (GraphRAG). Using over 1,000 polyhydroxyalkanoate (PHA) papers, we construct context-preserving paragraph embeddings and a canonicalized structured knowledge graph supporting entity disambiguation and multi-hop reasoning. We evaluate these pipelines through standard retrieval metrics, comparisons with general state-of-the-art systems such as GPT and Gemini, and qualitative validation by a domain chemist. The results show that GraphRAG achieves higher precision and interpretability, while VectorRAG provides broader recall, highlighting complementary trade-offs. Expert validation further confirms that the tailored pipelines, particularly GraphRAG, produce well-grounded, citation-reliable responses with strong domain relevance. By grounding every statement in evidence, these systems enable researchers to navigate the literature, compare findings across studies, and uncover patterns that are difficult to extract manually. More broadly, this work establishes a practical framework for building materials science assistants using curated corpora and retrieval design, reducing reliance on proprietary models while enabling trustworthy literature analysis at scale. Domain (mathematical analysis) ; Context (archaeology) ; Construct (python library) ; Subject-matter expert ; Domain knowledge ; Statement (logic) ; Question answering Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Advanced Graph Neural Networks
Authors: S Meena Sankari | R Sagayayaraj | S Sebastian | A Amalorpavadoss | V Porkalai | S. Aravazhi | G. Chandrasekaran
Source: Research Square (preprint, 2026-01-12)
Field: Materials Science — Materials Chemistry

Enhanced Magnetic Softness and Specific Capacitance in Mg0.5Zn0.5Fe2O4 Nanoferrites Ferrimagnetism ; Coercivity ; Crystallite ; Annealing (glass) ; Analytical Chemistry (journal) ; Crystallinity ; Ferromagnetism ; Nanoparticle ; Fourier transform infrared spectroscopy ; Magnetic nanoparticles Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Nanoparticle-Based Drug Delivery
Authors: Nik Reeves-McLaren
Source: Dalton Transactions (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Automated bond valence sum analysis for crystallographic database quality assessment: a systematic study of rock salt oxides, halides, and chalcogenides Crystallographic databases are vital for research and increasingly serve as training data for machine learning in materials discovery, yet systematic quality assessment at database scale remains absent. Bond valence sum (BVS) analysis - testing whether calculated bond valences match expected oxidation states - provides one automated quality metric, but has not been systematically applied to quantify validation rates, characterise failure modes, or identify parameter inadequacies. Eir is an automated Python tool for high-throughput BVS analysis, applied here to 840 rock salt structures from the Inorganic Crystal Structure Database. Of 613 assessable structures, 50.8% validated under ambient data collection conditions, with systematic examination leaving no unexplained outliers. 'Failed' structures stratify into six categories (Types 1-6): Type 1, systematic parameter inadequacy (107 structures, 21.3%); Type 2, missing BVS parameters (154 structures, excluded from validation); Types 3-5, methodological limitations from diffraction-averaged geometries (65 structures, 12.9%); and Type 6, database quality issues (12 structures, 2.4%). Three main findings emerged: (1) alkaline earth oxides exhibit systematic parameter inadequacy (CaO, SrO, BaO: 100% failure, <1.5% variance); (2) oxide-chalcogenide validation inversion demonstrates anion-specific parameter quality; (3) multi-phase refinement contamination shows structures passing peer review may prove inappropriate as computational references. The open-source tool provides validated infrastructure for maintaining database integrity as crystallographic repositories serve AI-driven materials discovery at unprecedented scale. Valence (chemistry) ; Python (programming language) ; Crystal structure ; Data quality ; Quality (philosophy) ; Quality assessment Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials