jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thang D. Pham | Aditya Tanikanti | Murat Keçeli
Source: Communications Chemistry (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

ChemGraph as an agentic framework for computational chemistry workflows Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen-2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables GPT-4o to reach perfect accuracy and smaller LLMs to match or exceed single-agent GPT-4o's performance in these benchmarks. Workflow ; Task (project management) ; Benchmark (surveying) ; Cheminformatics ; Computational model ; Simple (philosophy) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Graph Neural Networks
Authors: Minglei Liu | Yichuan Zhou | Xiaohan Mei | Zehao Yu | Boyun Guan | Yi Xiao | S S Liu | Hao Wang | Yanguo Qin
Source: Frontiers in Cell and Developmental Biology (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

AI-driven biomaterial design: an intelligent closed loop from reverse design to biological response Traditional approaches to biomaterial design face numerous challenges, including high trial-and-error costs, long development cycle, and the difficulty in deciphering the complex relationship between material properties and biological responses. With the rise of artificial intelligence (AI) technology, its capabilities in processing high-dimensional data and constructing complex mapping relationships have brought revolutionary changes to biomaterial design. This article reviews the four core applications of AI in the design of biomaterials. Firstly, based on the therapeutic needs of diseases, the functions of materials are clarified and formulations are generated. Secondly, high-throughput prediction and virtual screening of material properties using AI models significantly reduce development costs. Furthermore, the performance of materials and production efficiency can be enhanced by optimizing material formulas and processing techniques through AI. Finally, AI is used to predict the interaction between materials and cells or tissues, and to assess the safety and efficacy of the materials. This paper systematically explores how AI empowers biomaterial design, driving its advancement toward precision and intelligence, thereby providing robust support for the realization of personalized and precision medicine. Biomaterial ; Biocompatible material ; Realization (probability) ; Closed loop ; Biological materials ; Face (sociological concept) Machine Learning in Materials Science ; 3D Printing in Biomedical Research ; Topology Optimization in Engineering
Authors: Alexius Wadell | Anoushka Bhutani | Venkatasubramanian Viswanathan
Source: Journal of Chemical Information and Modeling (preprint, 2026-01-23)
Field: Chemistry — Inorganic Chemistry

Tokenization for Molecular Foundation Models Text-based foundation models have become an important part of scientific discovery, with molecular foundation models accelerating advancements in material science and molecular design. However, existing models are constrained by closed-vocabulary tokenizers that capture only a fraction of molecular space. In this work, we systematically evaluate 35 tokenizers, including 20 chemistry-specific tokenizers, and reveal significant gaps in their coverage of the SMILES molecular representation. To assess the impact of tokenizer choice, we introduce n-gram language models as a low-cost proxy and validate their effectiveness by pretraining and finetuning 18 RoBERTa-style encoders for molecular property prediction. To overcome the limitations of existing tokenizers, we propose two new tokenizers─Smirk and Smirk-GPE─with full coverage of the OpenSMILES specification. The proposed tokenizers systematically integrate nuclear, electronic, and geometric degrees of freedom, facilitating applications in pharmacology, agriculture, biology, and energy storage. Our results highlight the need for open-vocabulary modeling and chemically diverse benchmarks in cheminformatics. Foundation (evidence) ; Biology ; Computational biology ; Political science ; Bioinformatics ; Law Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Organometallic Complex Synthesis and Catalysis
Authors: Mariangeles Salas | Anand Singh | Carlos Pignataro | Lokendra Pal
Source: Communications Materials (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing Abstract Recent advances in artificial intelligence (AI) offer significant opportunities to drive industrial transformation by addressing growing societal demands for products, techno-economic efficiency, and reduced carbon footprints. This review presents a structured framework for building transparent, scalable, and sustainable AI-driven infrastructures spanning conceptualization to commercialization for materials discovery and advanced manufacturing. The framework traces the evolution of materials development from empirical approaches toward integrated AI-enabled platforms, emphasizing open-source tools that unify data acquisition, modeling, simulation, and deployment to democratize access, foster collaboration, and enhance reproducibility. Key enabling components include self-driving laboratories for real-time optimization, advanced computational approaches for high-fidelity data, and blockchain-based mechanisms for secure data sharing, provenance, and supply-chain traceability. The review further discusses the importance of machine learning for materials property prediction, synthesis and process optimization, together with scalable cloud–edge architectures that improve efficiency and reduce latency. Emphasis is placed on lifecycle-aware design, techno-economic analysis, and ethical AI principles to align industrial development with global sustainability goals. Commercialization ; Software deployment ; Conceptualization ; Process (computing) ; Sustainability ; Scalability Machine Learning in Materials Science ; Digital Transformation in Industry ; Big Data and Digital Economy
Authors: Ying Cao | Fu Hong | Jian Lu | Yuejiao Yang Claudio Migliaresi Danaa Ganbat Jie Chen | Titao Jing | Fan Xia | Bingang Xu
Source: Nano-Micro Letters (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence (AI). AI has successfully been applied for predicting the formability, revealing the properties, and guiding the experimental synthesis of materials. Rapid progress has been made in the integration of increasing database and improved computing power. Though some reviews present the development from their unique aspects, reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few. Here, the newest development is systematically reviewed in the field of AI empowered materials, reflecting advanced design of the intelligent systems for discovery, synthesis, prediction and validation of materials. First, background and mechanisms are briefed, after which the design for the AI systems with data, machine learning and automated laboratory included is illustrated. Next, strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials, and then, the design thought for future AI systems in material science is pointed out. Finally, some perspectives are put forward. Field (mathematics) ; Applications of artificial intelligence ; Intelligent decision support system ; Expert system ; Cover (algebra) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Selection and Properties
Authors: Antonio del Bosque | Pablo Fernández-Arias | Diego Vergara
Source: Machine Learning and Knowledge Extraction (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Machine Learning for Nanomaterial Discovery and Design Machine learning (ML) has become a transformative tool in nanomaterial research, driven by the rapid growth of data-intensive experimental techniques, multiscale simulations, and computational modeling. This study provides a bibliometric analysis to characterize how ML has been integrated into nanomaterial discovery and design. Following a PRISMA-guided workflow, research articles published between 2010 and 2025 were retrieved from Scopus and Web of Science, yielding a curated dataset of 4432 peer-reviewed documents. Here, performance indicators, citation patterns, and network analyses were examined to reveal publication growth, leading journals, productive institutions, and country-level contributions. The results show an exponential increase in scientific output since 2017 and a research landscape dominated by China, the United States, India, and Iran. Keyword co-occurrence and thematic mapping reveal four major research clusters: (i) ML-assisted nanoparticle synthesis, (ii) ML-driven nanocomposite design, (iii) data-driven modeling of carbon-based nanomaterials, and (iv) ML-supported catalysis and nanoscale chemistry. These results demonstrate the rapid consolidation of ML-enabled nanomaterial research and highlight emerging opportunities and challenges. The review provides an integrated summary of the field and highlights key future opportunities for advancing data-driven nanomaterial research. Transformative learning ; Scopus ; Thematic map ; Field (mathematics) ; Bibliometrics ; Web of science ; Nanomaterials Machine Learning in Materials Science ; Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods
Authors: Alon Grinberg Dana | Kevin M. Van Geem | Carlo Cavallotti | William H. Green
Source: ACS Engineering Au (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next Chemical kinetic modeling plays a foundational role in fields ranging from energy to environmental science, pharmaceuticals, and advanced materials. The past two decades have seen remarkable progress, particularly in modeling gas-phase reactions for thermochemical processes, leading to impactful industrial applications such as steam cracking and air quality management. However, new challenges are emerging. The successful development of systematic methodologies for the description of gas-phase kinetics opens the possibility to apply the same approach to the study of more challenging systems. Here, we review recent advances, including ab initio transition state theory-based master equation estimation of elementary rates, automated mechanism generation, machine-learning-assisted kinetics, and uncertainty quantification, and discuss the advances needed to apply the same methodological approach in areas such as heterogeneous catalysis, electrochemistry, liquid-phase and solid-state reactivity, and multiscale model integration. We advocate for the development of targeted tools, especially methods that go beyond empirical tuning toward first-principles-based predictions. We highlight the need for accessible software and AI-augmented workflows to democratize modeling for industry and academia alike. In this perspective, we call attention to not only what has worked but also what remains unsolved, advocating to avoid overemphasizing successes in scientific works at the expense of realism. The next decade should focus on predictive capability, physical accuracy, and community infrastructure (e.g., databases and services) to enable innovation across diverse fields. We argue that kinetic modeling, properly equipped, can accelerate discovery far beyond its traditional domains. Workflow ; Focus (optics) ; Quality (philosophy) ; Lead (geology) ; Work (physics) ; Software ; Empirical research ; Scientific modelling Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hongliang Xin | John Kitchin | Núria López | Neil M. Schweitzer | Nongnuch Artrith | Fanglin Che | Lars C. Grabow | G. T. Kasun Kalhara Gunasooriya | Heather J. Kulik | Teodoro Laino | H. C. Li | Suljo Linic | Andrew J. Medford | Randall J. Meyer | Jiayu Peng | C. B. Phillips | Jin Qian | Long Qi | Wendy J. Shaw | Zachary W. Ulissi | Siwen Wang | Xiaonan Wang
Source: Nature Catalysis (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Roadmap for transforming heterogeneous catalysis with artificial intelligence Interpretability ; Applications of artificial intelligence ; Heterogeneous catalysis ; Generalizability theory Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Tengyu Gui | Yuchen Wang | Jin Li | Zhongyuan Qiao | Zhu Wu | Yawen Sheng | Longchao Zhuo | Yinghong Wu | Imran Shakir | Xiujuan Wu | Ligang Feng | Xuping Sun | Xijun Liu
Source: Advanced Energy Materials (article, 2026-02-18)
Field: Chemistry — Inorganic Chemistry

Machine‐Learning‐Driven Design of Dynamically Adaptive MOF Catalysts: Structural Evolution Mechanisms and Industrial Translation Pathways in Seawater Electrolysis ABSTRACT Metal‐organic frameworks (MOFs) have emerged as promising electrocatalysts for seawater electrolysis, primarily owing to their structural tunability and high porosity. However, the practical application of MOFs is hindered by severe structural degradation under harsh seawater conditions, which includes chloride corrosion, microbial attack, pH fluctuations, and electric field‐induced reconstruction. This review systematically elucidates the dynamic evolution mechanisms of MOFs during seawater electrolysis, with a focus on corrosion‐driven degradation, electric field‐mediated valence oscillation, and ligand reconfiguration. We explore adaptive design strategies such as self‐healing, heterojunction engineering, defect control, coordination modulation, and spatial confinement that enhance the durability and activity of MOF‐based catalysts. Notably, the paper highlights the transformative role of machine learning in accelerating the discovery and optimization of corrosion‐resistant MOFs through high‐throughput screening, inverse design, and dynamic descriptor integration. By bridging fundamental mechanisms with industrial‐scale applications, this work provides a comprehensive roadmap for designing dynamically adaptive MOF catalysts, thereby paving the way for sustainable and efficient green hydrogen production from seawater. Seawater ; Durability ; Hydrogen production ; Electrolysis ; Microscale chemistry ; Adaptation (eye) ; Water splitting Metal-Organic Frameworks: Synthesis and Applications ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Satoshi Okamoto | Justin Llandro | Andrew Gibbons | Mohamed Arfaoui | Daisuke Hashizume | Junji Watanabe
Source: Macromolecular Rapid Communications (article, 2026-01-20)
Field: Materials Science — Polymers and Plastics

Orthogonal Staggered Alignment of Molecular Chains in Aramid Whiskers: A Game‐Changer for Sustainable, High‐Performance Composites We report the first discovery of polymer single crystal whiskers with a fundamentally unprecedented architecture: prismatic poly(p-benzamide) (PBA) whiskers with practical dimensions (∼17 µm length, ∼1 µm width, ∼0.5 µm thickness). Within these whiskers, polymer chains are oriented perpendicular to the long axis while maintaining high crystallinity. This morphology diverges from conventional lamellar structures, representing a new class of staggered polymer crystalline architecture where adjacent chains interpenetrate without layered periodicity. The configuration exposes chain ends at whisker surfaces, significantly enhancing dispersibility in organic solvents and polymer matrices due to the high-energy surface. Composites incorporating these whiskers exhibit uniform microstructures, exceptional thermal stability, and high modulus. PBA whiskers offer a lightweight, organic alternative to traditional inorganic fillers. These findings not only introduce a sustainable strategy but also redefine structural design principles in polymer crystallography for high-performance materials via self-organization pathways. Whiskers ; Lamellar structure ; Whisker ; Polymer ; Aramid ; Perpendicular ; Thermal ; Crystal (programming language) Polymer composites and self-healing ; Crystallography and molecular interactions ; Polymer crystallization and properties
Authors: Davina In | Androulla N. Miliotou | Panoraia I. Siafaka | Yiannis Sarigiannis
Source: Gels (article, 2026-01-02)
Field: Materials Science — Biomaterials

Ultra-Short Peptide Hydrogels as 3D Bioprinting Materials Ultra-short peptides (USPs; ≤7-8 amino acids) emerge as minimal self-assembling building blocks for hydrogel-based biomaterials. Their intrinsic biocompatibility, straightforward synthesis, and ease of tunability make them particularly attractive candidates for potential use in bioprinting. This review provides an overview of the properties of USPs along with their applications in three-dimensional (3D) bioprinting. We first discuss how peptide sequence, terminal and side-chain modifications, and environmental triggers govern USPs' self-assembly into nanofibers and 3D networks and how these supramolecular features translate into key rheological properties such as shear-thinning, rapid gelation, and mechanical tunability. We then survey reported applications in tissue engineering, wound healing, and organotypic models, as well as emerging ultra-short peptide-based systems for drug delivery, biosensing, and imaging, highlighting examples where printed constructs support cell viability, differentiation, and matrix deposition. Attention is given to hybrid and multi-material formulations in which USPs provide bioactivity while complementary components contribute structural robustness or additional functionality. Finally, this review outlines the main challenges that currently limit widespread adoption, including achieving high print fidelity with cytocompatible crosslinking, controlling batch-to-batch variability, and addressing the scalability, cost, and sustainability of peptide manufacturing. We conclude by discussing future opportunities such as AI-assisted peptide design, adaptive and multi-material bioprinting workflows, and greener synthetic routes, which together may accelerate the translation of ultra-short peptide-based bioinks from proof-of-concept studies to clinically and industrially relevant platforms. 3D bioprinting ; Self-healing hydrogels ; Peptide ; 3d printed ; Biofabrication ; Nanofiber ; Robustness (evolution) Supramolecular Self-Assembly in Materials ; 3D Printing in Biomedical Research ; Hydrogels: synthesis, properties, applications
Authors: Jiaxiang Li | Jiwen Feng | Jie Luo | Bowen Jiang | Xiangyu Zheng | Qi Song | Jian Lv | Keith T. Butler | Hanyu Liu | Congwei Xie | Yu Xie | Yanming Ma
Source: npj Computational Materials (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Self-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design Abstract Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled neural network potential to address these limitations. The generalizability of the potential is achieved by sampling regions across the local minima of the potential energy surface, where the self-evolving pipeline autonomously refines the potential iteratively while minimizing human intervention. The workflow is validated on Mg-Ca-H ternary and Be-P-N-O quaternary systems by exploring nearly 10 million configurations, demonstrating substantial speedup compared to first-principles calculations. These results underscore the effectiveness of our approach in accelerating the exploration and discovery of complex multi-component functional materials. Workflow ; Maxima and minima ; Pipeline (software) ; Complex system ; Generalization ; Crystal structure prediction ; Artificial neural network ; CASP ; Generalizability theory Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography