jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Zhang | Zhaorui Liu | Ben Ni | Quan Wang
Source: Advanced Functional Materials (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Large Language Models (LLMs) for Materials Design Abstract In recent years, rapid advances in large language models (LLMs) have been witnessed, while materials scientists have quickly adapted to exploit their potential. This review surveys the latest developments at the intersection of LLMs and materials science. Both general‐purpose and materials‐specific LLMs, as well as their theoretical and technical backgrounds, are first discussed. Their core capabilities are then detailed, including domain‐specific question answering for materials design, automated data extraction, semantic‐driven material design, synthesis planning, property prediction, and emphasize on inverse materials design as well as integration with materials simulation (e.g., density function theory and crystal structure prediction) workflows. These capabilities are illustrated through case studies in metals/alloys, metal organic frameworks, glasses/ceramics, photovoltaics, catalysis, and batteries, followed by a discussion of advances in retrieval‐augmented generation (RAG) and agent systems for materials science. Finally, current challenges and promising future directions of LLMs for materials science are outlined, especially physics‐informed transformer (PIT) or materials‐informed transformer (MIT) architecture that may effectively bridge the gap between materials science and LLMs (i.e., “LLMs for materials” and “materials for LLMs”). Exploit ; Transformer ; Intersection (aeronautics) ; Bridge (graph theory) ; Material Design ; Property (philosophy) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; CO2 Reduction Techniques and Catalysts
Authors: Zeyu Xia | Jinzhe Ma | Congjie Zheng | Shufei Zhang | Yuqiang Li | Hang Su | P. Hu | Changshui Zhang | Xingao Gong | Wanli Ouyang | Lei Bai | Dongzhan Zhou | Mao Su
Source: ArXiv.org (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

An Agentic Framework for Autonomous Materials Computation Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery, yet their static knowledge and hallucination issues hinder autonomous research applications. Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific workflows. Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations. By embedding domain expertise, the agent ensures physically coherent multi-step workflows and consistently selects convergent, well-posed parameters, thereby enabling reliable end-to-end computational execution. A new benchmark of diverse computational tasks demonstrates that our system significantly outperforms standalone LLMs in both accuracy and robustness. This work establishes a verifiable foundation for autonomous computational experimentation and represents a key step toward fully automated scientific discovery. Workflow ; Key (lock) ; Domain (mathematical analysis) ; Benchmark (surveying) ; Automation ; Verifiable secret sharing ; Computational model Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Michiko Yoshitake | Takahiro Nagata
Source: Applied Sciences (article, 2025-09-28)
Field: Materials Science — Materials Chemistry

A Method for LLM-Based Construction of a Materials Property Knowledge Graph: A Case Study In the field of materials science, experimental data or simulation results on material properties are often unevenly distributed. In addition to the vast unexplored material space, properties of lesser interest have not been measured even for well-studied materials, as exemplified by the discovery of the superconductivity of the long-known MgB2. To overcome such challenges, utilizing relationships among material properties based on scientific principles can be beneficial. We have been constructing a knowledge graph of material property relationships using natural language-processing techniques for years. Now, with the surprising development of large language models, constructing a knowledge graph has become much easier. This article explains what a knowledge graph of material property relationships is, presents several types of applications for the knowledge graph, and describes how the constructed knowledge graph can be implemented in machine learning for predicting material property values. We also demonstrate the construction of a knowledge graph of material property relationships and a search system using ChatGPT, without any programming, which will be made publicly available. Knowledge graph ; Property (philosophy) ; Graph ; Knowledge extraction ; Domain knowledge ; Field (mathematics) ; Natural materials Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Janghoon Ock | Radheesh Sharma Meda | Tirtha Vinchurkar | Yayati Jadhav | Amir Barati Farimani
Source: Digital Discovery (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Adsorb-Agent: autonomous identification of stable adsorption configurations <i>via</i> a large language model agent Adsorb-Agent: an LLM-powered agent for determining the most stable adsorption configurations using reasoning and prior knowledge. Identification (biology) ; Multi-agent system ; Adsorption ; Stability (learning theory) ; Autonomous agent Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions
Authors: Md Musaddaqul Hasib | Sumin Jo | Harsh Sinha | Jifeng Song | Arun Das | Zhentao Liu | Hugh Galloway | Huey Huang | Kexun Zhang | Shou-Jiang Gao | Yu-Chiao Chiu | Lei Li | Yufei Huang
Source: Research Square (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

A Process-Centric Survey of AI for Scientific Discovery Through the EXHYTE Framework Scientific discovery ; Bridge (graph theory) ; Perspective (graphical) ; Sequence (biology) ; Work (physics) ; Knowledge extraction Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Supriyo Chakraborty
Source: Preprints.org (preprint, 2025-10-15)
Field: Chemistry — Physical and Theoretical Chemistry

Bridging the Reality Gap: Machine Learning, Live Analytics, and the New Practice of Predictive Chemistry Every chemist knows the small heartbreak: the calculation looks beautiful; the flask does not. This paper takes that feeling seriously and names it—the reality gap—and then shows how to cross it on purpose. Our thesis is straightforward: predictive chemistry emerges when we let theory and experiment argue in public, with machine learning acting as the translator that keeps the debate honest.We first map where neat first‑principles wobble in the wild: bonds stretching and breaking, surfaces choosing a pathway, solvents shifting free energies just enough to matter, and spin states reorganizing the landscape. We then show how to correct those edges without discarding physics: hybrid QM/ML methods that learn systematic errors, uncertainty that travels with every prediction so we know when to trust and when to measure, and chemistry‑aware transfer learning so models trained on idealized inputs remain useful on real instruments.The loop closes when models talk to tools and tools talk back. Process‑analytical technologies feed real‑time signals to Bayesian optimization, multi‑objective workflows make trade‑offs visible (yield, selectivity, cost, greenness), and autonomy becomes conditional by design—robots execute, chemists steer. We focus on validation that survives deployment, not convenience: splits that reflect how chemistry varies in practice, calibrated confidence, and structured logging that treats failures as first‑class data. Finally, we detail what this buys in real laboratories: faster cycles, reproducible and information‑rich datasets, greener routes—and decisions made with eyes open.The message is practical and hopeful. Keep the physics where it is strong. Teach it where it is stubbornly wrong. Carry uncertainty forward. Let instruments help decide where to look next. Do that, and the calculation and the flask still won’t always agree—but they will disagree productively, more often, and for reasons we can understand. That is predictive chemistry in practice. Bridging (networking) ; Workflow ; Autonomy ; Chemist ; Feeling ; Focus (optics) ; Bayesian probability ; Arrow Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Muyu Lu | Siyu Hu | Chengxi Zhao | Chen Wang | Weile Jia | Guangming Tan | Jun Jiang | Linjiang Chen
Source: ChemRxiv (preprint, 2025-08-05)
Field: Materials Science — Materials Chemistry

Efficient fine‑tuning of foundation atomistic models for reversible polymorphic phase transitions in organic molecular crystals Foundation atomistic models—pretrained, general-purpose machine learning interatomic potentials—offer a promising path toward universal, data-efficient atomistic simulation across chemistry and materials science. However, their ability to capture rare, collective phenomena in complex molecular systems remains largely untested. Here, we systematically evaluate the transferability and fine-tuning of four state-of-the-art foundation atomistic models (MACE-MP-0b, MACE-OFF-small, SevenNet, and CHGNet) for simulating the reversible α⇌β polymorphic phase transition in the prototypical organic molecular crystal 2,4,5-triiodo-1H-imidazole (tIIm). Out-of-the-box, none of the models can reproduce the complete reversible transition, reflecting their limitations in capturing subtle and finely balanced non-covalent interactions outside their pretraining domain. By contrast, targeted fine-tuning—especially using our recently developed parameter-efficient equivariant low-rank adaptation (ELoRA) method—enables robust simulation of both transition directions and recovers the full collective dynamics of the phase change. Mechanistic analysis reveals a stepwise transition pathway with pronounced asymmetry in energy barriers, consistent with experimental observations. Our results establish practical guidelines for adapting foundation atomistic models to challenging materials phenomena and demonstrate that efficient fine-tuning protocols are essential for realizing the promise of universal, transferable machine learning interatomic potentials in molecular materials simulation. Foundation (evidence) ; Phase (matter) ; Phase transition ; Chemical physics ; Materials science ; Polymorphism (computer science) ; Fine-tuning ; Crystallography ; Condensed matter physics ; Chemistry ; Physics ; Organic chemistry ; Quantum mechanics ; Political science ; Law Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Polymer Synthesis and Characterization
Authors: Yixi Shen | L. L. Wang | Yan Huang | Xiaolong Zhang | Meng Huang | Huirong Li | Jing He | Aoran Cai | Yang Wang | Pieter E. S. Smith | Jun Jiang | Zhuoying Zhu | Linjiang Chen
Source: ChemRxiv (preprint, 2025-08-12)
Field: Materials Science — Materials Chemistry

Unlocking azobenzene isomerization mechanisms via an LLM agent-driven workflow integrating simulation, experiment, and machine learning Bridged azobenzene derivatives are important photo-responsive molecular switches with wide-ranging applications in optoelectronics and information storage, owing to their reversible isomerization around the –N=N– bond. However, elucidating the microscopic mechanisms underlying their isomerization pathways remains challenging, as conventional spectroscopic approaches often struggle to resolve complex intramolecular coupling effects and to establish robust structure–spectrum correlations. Here, we present an integrated, autonomous research workflow that combines robotic experimentation, infrared (IR) and Raman spectroscopy, quantum chemical simulation, and machine learning (ML) for structural analysis of bridged azobenzenes. Central to our approach is the attention-based 1D convolutional neural network (ATT-CNN), which quantitatively predicts the critical C–N=N–C dihedral angle directly from vibrational spectra, achieving a correlation coefficient of r = 0.99 and a mean absolute error of just 5°. By leveraging pre-trained models and transfer learning, our method demonstrates strong generalizability across different chemical environments and experimental conditions. The attention mechanism within ATT-CNN further enables holistic, interpretable evaluation of vibrational features, ensuring reliable structure determination even in the presence of routine experimental variations. Orchestrated by the ChemAgents multi-agent platform, the workflow tightly integrates experimental design, automated computational and experimental data acquisition, and machine learning analysis in a closed-loop, data-driven manner. This strategy not only advances fundamental understanding of azobenzene isomerization mechanisms, but also establishes a generalizable blueprint for intelligent, automated laboratory research in dynamic molecular systems. Azobenzene ; Isomerization ; Workflow ; Computer science ; Chemistry ; Catalysis ; Organic chemistry ; Molecule Machine Learning in Materials Science ; Perovskite Materials and Applications ; Photoreceptor and optogenetics research
Authors: Guo-Xiong Xu | Liu Ji-Ren | Zeng Gao | Miao-Jiong Tang | Yi-Zhou Sun | Z. Z. Li | X. J. Liu | Shuo‐Qing Zhang | Xin Hong
Source: Nature Communications (article, 2025-12-23)
Field: Chemistry — Organic Chemistry

Unveiling mechanistic patterns of copper-catalyzed radical bond formation through linear free energy relationship Copper-catalyzed radical transformations have seen extensive investigations over recent decades. Due to the complexity of controlling factors and the diversity of reaction components, accurately delineating the bond formation mechanisms-namely reductive elimination, outer-sphere radical substitution, and ion pair-type bond formation-poses a formidable challenge. This study reveals the linear free energy relationships (LFER) for the C-C bond formation of Cu-catalyzed radical transformations. Leveraging this discovery, we formulate an accurate LFER model for predicting radical bond formation mechanisms in this catalytic system. By establishing LFER scales for ligands, radicals, and nucleophiles, the bond formation mechanisms across 132,300 potential Cu-catalyzed radical transformations are revealed, offering a detailed mechanistic guide for future C-C bond designs. This research advances the mechanistic understanding of Cu-catalyzed radical transformations, providing systematic and quantitative insights into the mechanistic preference of radical bond formations. Bond ; Taft equation ; Radical ; Radical ion ; Free-energy relationship ; Hydroxyl radical ; Catalysis Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Qingkun Zhao | Yuying Liu | Liwei Hu | H. M. Duan | Mingxing Li | Yan‐Hui Liu | Jing Jiang | Zhen Lu | Weihua Wang
Source: Advanced Science (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Chromatic‐Zone Mapping for Rapid Discovery of Antibacterial Alloys with Nanostructured Surfaces Addressing antibiotic-resistant bacteria requires the efficient development of antibiotic-free antimicrobial materials. Herein, a high-throughput parallel chromatic-zone screening strategy is developed that enables the simultaneous screening of composition and surface structure, thereby optimizing antimicrobial performance. As an example, a MgCuPdGd alloy library with diverse compositions and nanostructures, consisting of 229 samples with continuous compositional gradients and varied nanostructured morphologies is constructed by integrating magnetron co-sputtering and chemical dealloying. The dealloyed samples exhibit distinct chromatic zones-red, yellow, and green-each associated with unique compositional and microstructural features. Among these regions, the red Cu-rich region demonstrates the most outstanding antibacterial performance, achieving a 95% reduction of viable Staphylococcus aureus (S. aureus). Comprehensive characterization confirms that the superior antimicrobial efficiency originates from the synergistic contribution of the CuPd alloy and the optimized nanostructure. Furthermore, the observed correlation among surface color, composition, morphology, and antibacterial performance highlights the predictive capability of the chromatic-zone screening approach, thereby reducing characterization requirements by 90% and enabling morphology and performance estimation across large material libraries. This work not only offers a rapid and cost-effective strategy for identifying antimicrobial materials but also provides a versatile platform adaptable to the development of functional materials for broader biomedical and environmental applications. Characterization (materials science) ; Antimicrobial ; Alloy ; Antibacterial agent ; Surface modification Nanoporous metals and alloys ; Copper-based nanomaterials and applications ; Nanomaterials for catalytic reactions
Authors: Andres M. Bran | Tong Xie | Shai Pranesh | Jeffrey Meng | Xuan Thang Nguyen | Jeremy Goumaz | David Ming Segura | Ruizhi Xu | Dongzhan Zhou | Wenjie Zhang | Bram Hoex | Philippe Schwaller
Source: ArXiv.org (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement learning succeeds only when the base model already assigns non-negligible probability to correct answers -- a property we term 'latent solvability'. This work investigates the emergence of chemical reasoning capabilities and what these prerequisites mean for chemistry. We identify two necessary conditions for RL-based chemical reasoning: 1) Symbolic competence, and 2) Latent chemical knowledge. We propose mid-stage scientific training (MiST): a set of mid-stage training techniques to satisfy these, including data-mixing with SMILES/CIF-aware pre-processing, continued pre-training on 2.9B tokens, and supervised fine-tuning on 1B tokens. These steps raise the latent-solvability score on 3B and 7B models by up to 1.8x, and enable RL to lift top-1 accuracy from 10.9 to 63.9% on organic reaction naming, and from 40.6 to 67.4% on inorganic material generation. Similar results are observed for other challenging chemical tasks, while producing interpretable reasoning traces. Our results define clear prerequisites for chemical reasoning training and highlight the broader role of mid-stage training in unlocking reasoning capabilities. Scientific reasoning ; Set (abstract data type) ; Property (philosophy) ; Lift (data mining) ; Model-based reasoning ; Training set ; Training (meteorology) ; Automated reasoning Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Shulin Tan
Source: ArXiv.org (preprint, 2025-01-19)
Field: Materials Science — Materials Chemistry

Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors In this thesis, I explored the use of several machine learning techniques, including neural networks, simulation-based inference, and generative flow networks, on predicting CNTFETs performance, probing the conductivity properties of CNT network, and generating CNTFETs processing information for target performance. Carbon nanotube ; Carbon nanotube field-effect transistor ; Nanotechnology ; Field-effect transistor ; Transistor ; Field (mathematics) ; Engineering physics ; Materials science ; Engineering ; Electrical engineering ; Mathematics Machine Learning in Materials Science
Authors: Chengxi Zhao | Zhaojia Ma | Daojin Fan | Siyu Hu | Leping Wang | Weile Jia | En Shao | Guangming Tan | Jun Jiang | Linjiang Chen
Source: ChemRxiv (preprint, 2025-08-25)
Field: Materials Science — Materials Chemistry

Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction Molecular crystal structure prediction (CSP) faces a persistent computational bottleneck: it requires exhaustive sampling of vast packing landscapes while resolving energy differences of only a few kJ·mol-1. We introduce BOMLIP-CSP, an open-source Python framework that integrates machine learning interatomic potentials (MLIPs) with a tailored batched optimization strategy, enabling rapid, unbiased structure prediction across the full crystal density range. By introducing tailored parallelism into modern MLIPs, BOMLIP-CSP achieves a ~2.1–2.3× acceleration in large-scale CSP searches without compromising accuracy. In benchmarks covering 34 experimental structures from six CSP blind tests, over 50% of experimental crystals are recovered with foundational MLIPs (namely, MACE-OFF-small and SevenNet-0-D3), rising above 70% with judicious MLIP selection. Importantly, we show that MLIPs with comparable equilibrium energy accuracy can yield strikingly different CSP outcomes, underscoring that not only local energy fidelity but also the global topology of the crystal lattice energy landscape governs predictive success. Together, these results establish BOMLIP-CSP as a broadly accessible platform for accelerated CSP and provide new insight into the interplay between MLIP characteristics and crystal structure discovery. Interatomic potential ; Computer science ; Crystal structure ; Crystal structure prediction ; Computational science ; Artificial intelligence ; Machine learning ; Algorithm ; Crystallography ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Anita A Najan
Source: International Journal of Therapeutic Innovation. (article, 2025-12-30)
Field: Materials Science — Biomaterials

Nanoparticles as a drug delivery system: physicochemical properties, disease treatment Due to quick process in nanotechnology,the medical feild has been using nanomaterials a lot recently. Because they have qualities, such as tiny size adjustable surfaces strong bonding with other molecules, they are both water-loving, water-hating. they are ideally prepared for target specific and controlled delivery of micro and macromolecules in disease state. they have and outstanding performance towards bioavailability, bio-efficacy and pharmacokinetics. Nanotechnology has a great impact on global economy and global standards of living. Nanotechnology has revolutionaries in all aspects of day to living from medical industry to food industry. This review summarizes the characteristics of nanoparticles that have potentially toxic effects. The applications in other biological fields and the nanoparticle drug delivery system in disease treatment. Drug delivery ; Nanoparticle ; Targeted drug delivery ; Nanomaterials ; Disease treatment ; Drug ; Drug carrier ; Applications of nanotechnology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Cancer Research and Treatment