jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas Michael Pruyn | Amro Aswad | Sartaaj Takrim Khan | Robert Black | Seyed Mohamad Moosavi
Source: ChemRxiv (preprint, 2025-06-02)
Field: Chemistry — Inorganic Chemistry

MOF-ChemUnity: Unifying metal-organic framework data using large language models Artificial intelligence (AI) is transforming materials research in metal-organic frameworks (MOFs), where models trained on structured computational data routinely predict new materials and optimize their properties. This raises a central question: What if we could leverage the full breadth of MOF knowledge, not just structured datasets, but also the scientific literature? For human researchers, the literature remains the primary source of knowledge, yet much of its content, including experimental data and expert insight, remains underutilized by AI systems. We introduce MOF-ChemUnity, a structured, extensible, and scalable knowledge graph that unifies MOF chemical data by linking literature-derived insights to crystal structures and computational datasets. By disambiguating MOF names in the literature and connecting them to crystal structures in the Cambridge Structural Database, MOF-ChemUnity unifies experimental and computational sources and enables cross-document knowledge extraction and linking. We showcase how this enables multi-property machine learning across simulated and experimental data, compilation of complete synthesis records for individual compounds by aggregating information across multiple publications, and expert-guided materials recommendations via structure-based embeddings. When used as a knowledge source to augment large language models (LLMs), MOF-ChemUnity enables a literature-informed AI assistant that operates over the full scope of MOF knowledge. Expert evaluations show improved accuracy, interpretability, and trustworthiness across tasks such as retrieval, inference of structure-property relationships, and materials recommendation, outperforming standard LLMs. This work lays the foundation for literature-informed materials discovery, enabling both human scientists and AI systems to reason over the full landscape of MOF knowledge in a new way. Metal-organic framework ; Computer science ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ziqing Wang | Kexin Zhang | Zihan Zhao | Yibo Wen | Abhishek Pandey | Han Liu | Kan Ding
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic notations, with emerging extensions to incorporate multi-modal inputs. To advance the new field of LLM for molecular discovery, this survey provides an up-to-date and forward-looking review of the emerging use of LLMs for two central tasks: molecule generation and molecule optimization. Based on our proposed taxonomy for both problems, we analyze representative techniques in each category, highlighting how LLM capabilities are leveraged across different learning settings. In addition, we include the commonly used datasets and evaluation protocols. We conclude by discussing key challenges and future directions, positioning this survey as a resource for researchers working at the intersection of LLMs and molecular science. A continuously updated reading list is available at https://github.com/REAL-Lab-NU/Awesome-LLM-Centric-Molecular-Discovery. Intersection (aeronautics) ; Field (mathematics) ; Key (lock) ; Resource (disambiguation) ; Natural language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Jinming Hu | Hassan Nawaz | Yuting Rui | Lijie Chi | Arif Ullah | Pavlo O. Dral
Source: ChemRxiv (preprint, 2025-05-13)
Field: Materials Science — Materials Chemistry

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations We have developed Aitomia – a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running the atomistic simulations, monitoring their computation status, analyzing the simulation results, and summarizing them for the user in text and graphical forms. We achieve these goals by exploiting fine-tuned open-source large language models (LLMs), rule-based agents, and a retrieval-augmented generation (RAG) system. Aitomia leverages the versatility of our MLatom ecosystem for AI-enhanced computational chemistry. This intelligent assistant is going to be integrated into the Aitomistic Hub and XACS online computing services, with some functionality already publicly available as described at http://mlatom.com/aitomia. Aitomia is expected to lower the barrier to performing atomistic simulations, accelerating research and development in the relevant fields. Quantum chemical ; Computer science ; Quantum ; Human–computer interaction ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Pedro S.F. Mendes | Daniel Costa | Matteo Manica | Teodoro Laino | M.F. Ribeiro
Source: Research Square (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Automated LLM based Extraction of Standardized Synthesis Procedures: an All-Domain, Zero-Shot Approach Workflow ; Generalization ; Variety (cybernetics) ; Domain (mathematical analysis) ; Action (physics) ; Sequence (biology) ; Open domain ; Information extraction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Zeolite Catalysis and Synthesis
Source: Journal of Materials and Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Research Advances on the Role of Deep Learning in Materials Informatics Deep materials informatics is a rapidly evolving field that employs deep learning techniques to develop predictive models for materials science. It involves the use of large datasets, advanced algorithms, and highperformance computing to extract key features from complex materials data. The aim of deep materials informatics is to speed up the process of locating materials with desired attributes, by taking advantage of machine learning.There are numerous applications of this technology, and it can forecast the properties of materials at all levels -from the atomic to the macroscopic.For instance, deep materials informatics are used to predict the electrical, thermal and mechanical characteristics of materials, which are critical for designing new materials for various applications.This paper offers a thorough examination of the fundamentals of deep learning, its benefits and drawbacks, and its current usage in the analysis of numerous materials datasets.Additionally, it can be utilized to optimize the processing parameters for creating materials with desired characteristics. Informatics ; Data science ; Computer science ; Engineering ethics ; Cognitive science ; Psychology ; Engineering Machine Learning in Materials Science
Authors: Konstantinos D. Vogiatzis
Source: ChemRxiv (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

Design of CO2-philic Molecular Units with Large Language Models The integration of large language models (LLMs) into chemical sciences presents a transformative approach for molecular design. In this study, we explore the capabilities of LLMs for generating novel molecular structures with enhanced CO₂ affinity for the development of novel physisorptionbased carbon capture technologies. A hybrid workflow was employed, combining pretrained LLMs (GPT-4o, Llama-3, and Gemini 2.0) with computational chemistry techniques. LLMs were prompted to propose molecular candidates, which were subsequently evaluated using density functional theory (DFT) to assess their interaction energies with CO₂. Through iterative model refinement and expert-guided modifications, a dataset of 95 molecular units was generated, revealing several promising candidates with CO₂ interaction energies exceeding the -7 kcal/mol threshold for optimal physisorption. Notably, LLM-generated structures showcased emergent design strategies, such as cooperative binding motifs, that aligned with domain knowledge and experimental precedent. This study highlights the potential of LLMs as powerful tools for molecular discovery while underscoring the need for expert oversight and rigorous validation in AI-driven chemical research. Computer science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Carbon Dioxide Capture Technologies
Authors: Emily Lu | Gabriel Perez | Peter Baker | Daniel Irving | Santosh Kumar | Verónica Celorrio | Sylvia Britto | Thomas F. Headen | Miguel A. Gomez‐Gonzalez | Connor Wright | C. S. Green | R. S. Young | O. Kirichek | Ali Mortazavi | Sarah J. Day | Isabel Christine Antony | Zoe M. Wright | Thomas W. Wood | T. P. Snow | Jeyan Thiyagalingam | Paul D. Quinn | Martin O. Jones | William I. F. David | James Le Houx
Source: Greenwich Academic Literature Archive (University of Greenwich) (preprint, 2025-12-29)
Field: Materials Science — Materials Chemistry

Autonomous battery research: Principles of heuristic operando experimentation Unravelling the complex processes governing battery degradation is critical to the energy transition, yet the efficacy of operando characterisation is severely constrained by a lack of Reliability, Representativeness, and Reproducibility (the 3Rs). Current methods rely on bespoke hardware and passive, pre-programmed methodologies that are ill-equipped to capture stochastic failure events. Here, using the Rutherford Appleton Laboratory's multi-modal toolkit as a case study, we expose the systemic inability of conventional experiments to capture transient phenomena like dendrite initiation. To address this, we propose Heuristic Operando experiments: a framework where an AI pilot leverages physics-based digital twins to actively steer the beamline to predict and deterministically capture these rare events. Distinct from uncertainty-driven active learning, this proactive search anticipates failure precursors, redefining experimental efficiency via an entropy-based metric that prioritises scientific insight per photon, neutron, or muon. By focusing measurements only on mechanistically decisive moments, this framework simultaneously mitigates beam damage and drastically reduces data redundancy. When integrated with FAIR data principles, this approach serves as a blueprint for the trusted autonomous battery laboratories of the future. Bespoke ; Heuristic ; Battery (electricity) ; Metric (unit) ; Transient (computer programming) ; Interface (matter) ; Benchmark (surveying) ; Blueprint Machine Learning in Materials Science ; Radiation Detection and Scintillator Technologies ; Nuclear physics research studies
Authors: Teruyasu Mizoguchi
Source: Journal of the Ceramic Society of Japan (review, 2025-11-30)
Field: Materials Science — Materials Chemistry

A review of simulation, characterization, and informatics in inorganic materials Ceramic materials exhibit a wide range of functional properties that are strongly influenced by their atomic structures, chemical bonding, and lattice defects, including interface and surface. A deep understanding and precise control of these factors are essential for advancing materials design. In this review, I summarize recent progress from my group on computational, experimental, and informatics approaches aimed at elucidating structures and defects, as well as guiding the development of inorganic materials. First, I describe defect simulations accelerated by machine learning. Bayesian optimization was shown to drastically reduce the computational cost of interface structure determination, identifying stable interface configurations with only a fraction of the calculations required by conventional approaches. In addition, adsorption properties at surfaces were effectively predicted using descriptors derived from the individual electronic structures of isolated systems, demonstrating a new pathway to high-throughput surface analysis. Next, I highlight nano-scale characterization of oxides using electron microscopy and spectroscopy. We demonstrated that the nano-scale characterization enables quantitative determination of coordination number distributions in disordered glass systems. High-temperature in-situ imaging was used to track the evolution of phase separation in glass, and a high-resolution analysis revealed the local thermal expansion behavior at the crystalline interface at the nano-scale. Finally, I introduce the data-driven materials discovery of layered intercalated compounds. By analyzing more than 9,000 host–guest combinations, we established a quantitative stability rule based on hard-soft acid-base interactions and Coulombic size effects, which can also predict the stability of previously unreported compounds. Furthermore, we identified promising graphite intercalation compounds expected to exhibit exotic properties such as superconductivity. Consequently, I emphasize the importance of methodological developments as essential foundations for advancing ceramic science. Looking forward, the convergence of simulations, characterizations, and informatics, together with emerging artificial intelligence (AI) technologies, is expected to open new directions for materials research. Characterization (materials science) ; Bayesian optimization ; Interface (matter) ; Materials informatics ; Thermal stability ; Graphite ; Ceramic ; Stability (learning theory) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Junren Li | Luhua Lai
Source: ArXiv.org (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

SynCraft: Guiding Large Language Models to Predict Edit Sequences for Molecular Synthesizability Optimization Generative artificial intelligence has revolutionized the exploration of chemical space, yet a critical bottleneck remains that a substantial fraction of generated molecules is synthetically inaccessible. Current solutions, such as post-hoc filtering or projection-based methods, often compromise structural novelty or disrupt key pharmacophores by forcing molecules into pre-defined synthetic templates. Herein, we introduce SynCraft, a reasoning-based framework that reframes synthesizability optimization not as a sequence translation task, but as a precise structural editing problem. Leveraging the emergent reasoning capabilities of Large Language Models, SynCraft navigates the "synthesis cliff" where minimal structural modifications yield significant gains in synthetic feasibility. By predicting executable sequences of atom-level edits rather than generating SMILES strings directly, SynCraft circumvents the syntactic fragility of LLMs while harnessing their chemical intuition. Extensive benchmarks demonstrate that SynCraft outperforms state-of-the-art baselines in generating synthesizable analogs with high structural fidelity. Furthermore, through interaction-aware prompting, SynCraft successfully replicates expert medicinal chemistry intuition in editing PLK1 inhibitors and rescuing high-scoring but previously discarded RIPK1 candidates in previous molecular generation literatures. Executable ; Key (lock) ; Generative grammar ; Language model ; Heuristics ; Intuition ; Bridging (networking) ; Pharmacophore Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Qi Li | Kun Li | Haozhi Han | Honghui Shang | Xinfu He | Yunquan Zhang | An Hong | Ting Cao | Mao Yang
Source: ArXiv.org (preprint, 2025-05-26)
Field: Materials Science — Materials Chemistry

SwarmThinkers: Learning Physically Consistent Atomic KMC Transitions at Scale Can a scientific simulation system be physically consistent, interpretable by design, and scalable across regimes--all at once? Despite decades of progress, this trifecta remains elusive. Classical methods like Kinetic Monte Carlo ensure thermodynamic accuracy but scale poorly; learning-based methods offer efficiency but often sacrifice physical consistency and interpretability. We present SwarmThinkers, a reinforcement learning framework that recasts atomic-scale simulation as a physically grounded swarm intelligence system. Each diffusing particle is modeled as a local decision-making agent that selects transitions via a shared policy network trained under thermodynamic constraints. A reweighting mechanism fuses learned preferences with transition rates, preserving statistical fidelity while enabling interpretable, step-wise decision making. Training follows a centralized-training, decentralized-execution paradigm, allowing the policy to generalize across system sizes, concentrations, and temperatures without retraining. On a benchmark simulating radiation-induced Fe-Cu alloy precipitation, SwarmThinkers is the first system to achieve full-scale, physically consistent simulation on a single A100 GPU, previously attainable only via OpenKMC on a supercomputer. It delivers up to 4963x (3185x on average) faster computation with 485x lower memory usage. By treating particles as decision-makers, not passive samplers, SwarmThinkers marks a paradigm shift in scientific simulation--one that unifies physical consistency, interpretability, and scalability through agent-driven intelligence. Benchmark (surveying) ; Scalability ; Consistency (knowledge bases) ; Reinforcement learning ; Scale (ratio) ; Computation ; Fidelity ; Representation (politics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Eldar Sultanow | Madjid Tehrani | Siddhant Dutta | William J. Buchanan | Muhammad Shahbaz Khan
Source: ArXiv.org (preprint, 2025-06-02)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum Agents This paper explores the intersection of quantum computing and agentic AI by examining how quantum technologies can enhance the capabilities of autonomous agents, and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum-agentic platforms. Our work introduces a formal definition of quantum agents and outlines potential architectures that integrate quantum computing with agent-based systems. As a proof-of-concept, we develop and evaluate three quantum agent prototypes that demonstrate the feasibility of our proposed framework. Furthermore, we discuss use cases from both perspectives, including quantum-enhanced decision-making, quantum planning and optimization, and AI-driven orchestration of quantum workflows. By bridging these fields, we aim to chart a path toward scalable, intelligent, and adaptive quantum-agentic ecosystems. Quantum ; Intersection (aeronautics) ; Quantum computer ; Bridging (networking) ; Quantum technology ; Quantum information science ; Quantum information ; Quantum network Various Chemistry Research Topics
Authors: Shijie Xia | Sun Yuhan | Pengfeï Liu
Source: ArXiv.org (preprint, 2025-10-13)
Field: Materials Science — Materials Chemistry

SR-Scientist: Scientific Equation Discovery With Agentic AI Recently, Large Language Models (LLMs) have been applied to scientific equation discovery, leveraging their embedded scientific knowledge for hypothesis generation. However, current methods typically confine LLMs to the role of an equation proposer within search algorithms like genetic programming. In this paper, we present SR-Scientist, a framework that elevates the LLM from a simple equation proposer to an autonomous AI scientist that writes code to analyze data, implements the equation as code, submits it for evaluation, and optimizes the equation based on experimental feedback. Specifically, we wrap the code interpreter into a set of tools for data analysis and equation evaluation. The agent is instructed to optimize the equation by utilizing these tools over a long horizon with minimal human-defined pipelines. Empirical results show that SR-Scientist outperforms baseline methods by an absolute margin of 6% to 35% on datasets covering four science disciplines. Additionally, we demonstrate our method's robustness to noise, the generalization of the discovered equations to out-of-domain data, and their symbolic accuracy. Furthermore, we develop an end-to-end reinforcement learning framework to enhance the agent's capabilities. Structural equation modeling ; Baseline (sea) ; Code (set theory) ; Equation solving ; Set (abstract data type) ; Simple (philosophy) ; Robustness (evolution) Machine Learning in Materials Science ; Evolutionary Algorithms and Applications ; Topic Modeling
Authors: Khalid Ferji
Source: ChemRxiv (preprint, 2025-04-16)
Field: Materials Science — Materials Chemistry

Basic Concepts and Tools of Artificial Intelligence in Polymer Science In recent years, artificial intelligence (AI) has emerged as a transformative tool for addressing scientific and technical challenges across various disciplines. AI enables data-driven predictions, uncovers hidden patterns, and automates labor-intensive tasks, offering unprecedented opportunities for innovation. However, its rapid rise has been disruptive, and many scientific fields—including polymer science—were not fully prepared for its integration. The complexity of polymer systems, coupled with the traditionally empirical nature of the field, has made AI adoption particularly challenging. Many polymer scientists still face significant barriers, including technical complexity, and a lack of interdisciplinary training. This perspective serves as an entry point for researchers seeking to integrate AI into polymer science by presenting real-world applications, practical tools, and key challenges. Rather than providing an exhaustive review for specialists, it aims to familiarize polymer scientists with AI’s capabilities and encourage further exploration. By lowering entry barriers and fostering interdisciplinary dialogue, this work bridges the gap between conventional polymer research and data-driven innovation, paving the way for future advancements. Artificial intelligence ; Computer science ; Management science ; Engineering Machine Learning in Materials Science