jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Coveney, Peter | Highfield, Roger
Source: ArXiv.org (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

AI Needs Physics More Than Physics Needs AI Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures - large language models, reasoning models, and agentic AI - can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of 'Big AI': a synthesis of theory-based rigour with the flexibility of machine learning. Rigour ; Flexibility (engineering) ; Applications of artificial intelligence ; Thought experiment Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Explainable Artificial Intelligence (XAI)
Authors: Yukun Wu (424101) | Aikaterini Vriza (22660467) | Doga Ozgulbas (22660470) | Rafael Vescovi (8731606) | Jianing Zhou (12077429) | Zhiyang Wang (335420) | Shiyu Hu (3911563) | Yuepeng Zhang (7780724) | Qiaomu Yang (4217062) | Anna M. Österholm (1681945) | John R. Reynolds (1288521) | Subramanian K.
R. S. Sankaranarayanan (5175344) | Maria K. Y. Chan (7490150) | Ian T. Foster (9080417) | Jianguo Mei (1419895) | Henry Chan (1544731) | Jie Xu (34477)
Source: Figshare (article, 2025-11-21)
Field: Materials Science — Polymers and Plastics

Autonomous Synthesisand Inverse Design of ElectrochromicPolymers with High Efficiency and Accuracy The design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure–property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange. Comonomer ; Electrochromism ; Workflow ; Copolymer ; Polymer ; Key (lock) ; Inverse Conducting polymers and applications ; Machine Learning in Materials Science ; Transition Metal Oxide Nanomaterials
Authors: O.M. Yermakov | V.E. Volkova
Source: Modern construction and architecture (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

RESEARCH ON THE INFLUENCE OF MINERAL OILS ON THE STRENGTH AND AVERAGE DENSITY OF HEAVY CONCRETE The article investigates the effect of mineral oils on the strength and volumetric weight of heavy concrete. Heavy concrete is the main material in construction, used to create structures with increased requirements for strength and stability, however, the effect of chemical agents, such as mineral oils, on its properties remains insufficiently studied. The study of this aspect is important for improving the durability and safety of concrete structures, in particular those exposed to oils in industrial processes or accidents. The aim of the study was to study how different types of mineral oils (transformer oil T-1500, engine oil 10W-40, IGP-30, both new and used) affect the physical and mechanical characteristics of heavy concrete, in particular its strength and volumetric weight. The experiments were carried out on concrete samples measuring 100×100×100 mm, made of Portland cement, under standard storage conditions. Oils were applied by two methods: immersion of samples in oil and application with a brush. The results of the studies showed that the most significant effect on the strength and volumetric weight of concrete was the method of immersion of samples in new transformer oil T-1500, where after 30 days a significant increase in weight by 1.18% and compressive strength by 13.06% was observed. The use of waste oils and the method of applying oil with a brush led to a decrease in the strength of concrete, in particular, when using waste motor oil 10W-40, the strength decreased by 63.74% on day 30. At the same time, application of oil with a brush caused mainly superficial changes, without significant improvements or even a decrease in the strength of concrete. The results confirmed that the type of oil, its condition and the method of application have a significant effect on the mechanical properties of concrete. The study indicates the importance of choosing methods for protecting concrete structures from the negative effects of chemical agents and opens up prospects for further research in this area, in particular the development of technologies that reduce the impact of mineral oils on the durability and strength of concrete. Compressive strength ; Mineral oil ; Durability ; Experimental research ; Specific gravity ; Heavy metals ; Portland cement ; Industrial waste Structural mechanics and materials ; Environmental and Industrial Safety ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: В. А. Салина
Source: Расплавы / Melts (article, 2025-01-01)
Field: Materials Science — General Materials Science

TECHNOLOGY OF PRODUCING Mn-Si-Ba-Fe ALLOY BY CARBOTHERMAL METHOD The results of theoretical and experimental studies of the technology for producing a complex barium-containing ferroalloy based on manganese for out-of-furnace processing of metal products are presented. Ferroalloys containing barium are of the greatest interest due to the unique complex of its physical and physicochemical properties (solubility in liquid iron is 1.22·10 at.% at T=1600K; relative modifying capacity 69.86·10; T=983K; T=1910K; heat of interaction with iron is 212 kJ/mol; enthalpy of dissolution is 300 kJ/g-at at T=1700K). The diagrams of material ratios of the oxide BaO-SiO-MnO-FeO and metallic Ba-Si-Mn-Fe systems were constructed to determine the rational ratio of the components of the charge and the composition of the alloy by the method of thermodynamic-diagram analysis. This is a simple method for studying phase patterns of complex systems taking into account the features of state diagrams. The values of the change in Gibbs energy (ΔG) are determined in a homogeneous liquid-phase state using the Gibbs software package developed by scientists of the Chemical and Metallurgical Institute named after Zh. Abishev. The tie lines of coexisting phases are drawn according to Hess&amp;apos;s rule. It was established that the rational composition of the batch for obtaining an alloy with a barium content of more than 1.5% is located in the quasi-system FeSiO-BaSiO-MnSiO-SiO, and the composition of the alloy is in the region of the tetrahedron FeSi-BaSi-MnSi-Si. The large-scale laboratory tests of the smelting of a complex barium-containing ferroalloy based on manganese in an ore-thermal furnace with a transformer capacity of 200 kV×A by a slag-free carbothermal method were carried out on the basis of the results of a thermodynamic-diagram analysis. Manganese ore from the Mynaral deposit (71.67% MnO), barite ore from the Zhumanai deposit (74.35% BaSO), and quartzite from the Tekturmas deposit (97.05% SiO) were used as initial raw materials. Chinese coke (82.70% C) was used as a reducing agent. It was established that the transition of barium into the alloy is determined by the accompanying process of silicon reduction. It was determined that to obtain an alloy with 1.5% Ba, the ratio Si/(Mn+Fe) should be at the level of 0.6; for an alloy with a content of 10% Ba - 1.1. Ferroalloy ; Alloy ; Smelting ; Gibbs free energy ; Ferromanganese ; Manganese ; Bauxite ; Barium ; Yttrium ; Dissolution Metallurgical and Alloy Processes ; Metal Alloys Wear and Properties ; Magnetic Properties of Alloys
Authors: Bowen Liu | Xu Zhao | Jiyuan Guo | Fangcheng Lv
Source: Chemical Physics Letters (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Molecular dynamics-based synergistic enhancement of electro-thermo-mechanical properties of nanocomposite aramid insulating papers Nanocomposite ; Aramid ; Molecular dynamics ; Materials science ; Dynamics (music) ; Composite material ; Polymer science ; Chemical engineering ; Chemistry ; Computational chemistry ; Physics ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Cellulose Research Studies
Authors: Amauri J. Paula | Romana Petry | James M. Almeida | Andre Caetano | José Sales | Odair P. Ferreira | Diego S. T. Martinez | Henry Kobs | Andreia Filipa Pereira Faria
Source: ACS Applied Nano Materials (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Large-Scale Meta-Analysis of Nanomaterials Toxicity Based on Natural Language Processing of Scientific Articles Natural language processing (NLP) pipelines can mine the nanotoxicology literature at a scale and resolution that cannot be achieved by manual curation. Here, we established a NLP pipeline that coupled topic modeling and end point-specific extraction LLM prompts to convert ∼10<sup>6</sup> sentences extracted from abstracts of scientific articles into a structured knowledge base containing 13 nanotoxicology features. The pipeline is capable of analyzing and extracting nanomaterial descriptors such as size, ζ potential, and surface area, along with biological end points such as minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and lethal concentration 50% (LC<sub>50</sub>). Statistical convergence across multiple quantitative end points - MIC, MBC, microbial log reduction, and biofilm killing efficiency - shows that Ag-based nanomaterials are the most potent antimicrobial agents, showing lower MIC and MBC than ZnO, TiO<sub>2</sub> and Au analogs. This trend was also observed for individual pathogens such as <i>Escherichia coli</i> and <i>Staphylococcus aureus</i>. Most nanomaterials are within 1 to 100 nm, with nanoparticles featured in >80% of the studies. Although nanomaterials <50 nm often produce the lowest MIC and LC<sub>50</sub>, toxicity within a single size class spans orders of magnitude, underscoring the influence of surface chemistry, coatings, and colloidal behavior. In addition, adverse reproductive effects in <i>Caenorhabditis elegans</i> and <i>Daphnia magna</i>, and developmental abnormalities in <i>Danio rerio</i>, are predominantly related to Ag and TiO<sub>2</sub>. In general, our automated data extraction and the curation strategy transforms disparate literature into a machine-readable knowledge base that paves the way for data-driven predictions of nanomaterial hazards. Nanotoxicology ; Pipeline (software) ; Nanomaterials ; Knowledge base ; Extraction (chemistry) Nanoparticles: synthesis and applications ; Biomedical Text Mining and Ontologies ; Computational Drug Discovery Methods
Authors: Boris N. Slautin | Utkarsh Pratiush | Yongtao Liu | Hiroshi Funakubo | Vladimir V. Shvartsman | Doru C. Lupascu | Sergei V. Kalinin
Source: ArXiv.org (preprint, 2025-08-21)
Field: Materials Science — Materials Chemistry

Attention-Based Explainability for Structure-Property Relationships Machine learning methods are emerging as a universal paradigm for constructing correlative structure-property relationships in materials science based on multimodal characterization. However, this necessitates development of methods for physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure-property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations (SHAP) analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features. Interpretability ; Correlative ; Identification (biology) ; Domain (mathematical analysis) ; Artificial neural network ; Representation (politics) ; Complex system ; Contrast (vision) Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Ferroelectric and Piezoelectric Materials
Authors: Donghwa Lee | E. Kim | Jun Hyeong Gu | Gyeong Shin Hyo | Changhun Kim | Sangwoo Park
Source: Research Square (preprint, 2025-10-01)
Field: Materials Science — Materials Chemistry

Generative model for large-cell crystal structure Generative grammar ; Benchmark (surveying) ; Generative model ; Symmetry (geometry) ; Inverse ; Simple (philosophy) ; Ternary operation Crystallization and Solubility Studies
Source: NanoWorld Journal (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

6th International Conference on Materials Science &amp; Nanotechnology (Future Materials-2025) Andreas Hirsch Chemical functionalization of new carbon-allotropes such as the two dimensional (2D) graphene is of fundamental interest and opens the door to unprecedented materials applications. We will report on the development of efficient functionalization protocols for graphene using both covalent and non-covalent approaches. Surface modification ; Graphene ; Applications of nanotechnology ; Scientific discovery ; Foundation (evidence) Graphene research and applications ; 2D Materials and Applications ; Covalent Organic Framework Applications
Authors: Alyaa Abdalsalam | Noor Saadoon | Ali A. Ati | Shadab Dabagh | A. Salim
Source: Al-Qadisiyah Journal for Engineering Sciences (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Investigation of the structural, morphological, and electrochemical characteristics of MgFe2O4/ZnO nanocomposite In the current study, three distinct synthesis techniques, the gel, co-precipitation, and solid-state method, were employed to synthesize (MgFe2O4)x/ZnO1-x hetero-nanocomposites with varying magnesium ferrite contents (x = 0.03%, 0.06%, and 0.09%). To study the effect of the addition of magnesium ferrite nanoparticles on the structural, morphological, thermal, and electrochemical properties of zinc oxide. X–ray diffraction (XRD), Rietveld refinement technique, Fourier transform infrared (FT-IR) spectroscopy, field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), and cyclic voltammetry (CV) were used to investigate the samples. The formation of the spinel cubic structure and hexagonal wurtzite structure of the prepared magnesium ferrite/zinc oxide nanocomposites was confirmed by X-ray diffraction, and no extra phases were detected. The Rietveld-refined X-ray diffraction data revealed spinel cubic and hexagonal wurtzite structures with the P63mc and Fd-3m space group, respectively. The crystallite size decreased from 16 to 15 nm upon the substitution of magnesium ferrite nanoparticles, confirming the formation of nano-crystalline MgFe2O4/ZnO nanocomposites. FT-IR spectra were used to verify the absorption bands of MgFe2O4, ZnO, and their composites. FE-SEM images revealed the presence of a slight agglomeration of nanoparticles and a non-uniform size distribution. TEM analysis revealed nearly spherical morphologies for all prepared samples, with an average particle size of 19-22 nm. There is variation in the crystallite size as estimated from the instruments, which may be due to strain. The electrochemical behavior was investigated using cyclic voltammetry (CV) with a 0.5 M KCl aqueous solution as the electrolyte. The MgFe2O4/ZnO nanocomposite exhibited superior rate performance and cycle stability compared to the other samples when their electrochemical performance was analyzed using cyclic voltammetry (CV). According to the physical results, nanocomposite electrodes exhibited enhanced electrochemical performance, high reversibility, and cycle stability, with specific capacitances ranging from 1.87 F/g (0.01 V) to 7.63 F/g (0.002 V), making them promising candidates for pseudocapacitors. Wurtzite crystal structure ; Crystallite ; Cyclic voltammetry ; Spinel ; Nanocomposite ; Nanoparticle ; Rietveld refinement ; Transmission electron microscopy Magnetic Properties and Synthesis of Ferrites ; Magnesium Oxide Properties and Applications ; Nanoparticle-Based Drug Delivery
Authors: D. M. Armstrong | Zlatko Jončev | Andres M Bran | Philippe Schwaller
Source: ArXiv.org (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

SynthStrategy: Extracting and Formalizing Latent Strategic Insights from LLMs in Organic Chemistry Modern computer-assisted synthesis planning (CASP) systems show promises at generating chemically valid reaction steps but struggle to incorporate strategic considerations such as convergent assembly, protecting group minimization, and optimal ring-forming sequences. We introduce a methodology that leverages Large Language Models to distill synthetic knowledge into code. Our system analyzes synthesis routes and translates strategic principles into Python functions representing diverse strategic and tactical rules, such as strategic functional group interconversions and ring construction strategies. By formalizing this knowledge as verifiable code rather than simple heuristics, we create testable, interpretable representations of synthetic strategy. We release the complete codebase and the USPTO-ST dataset -- synthesis routes annotated with strategic tags. This framework unlocks a novel capability for CASP: natural language-based route retrieval, achieving 75\% Top-3 accuracy on our benchmark. We further validate our library through temporal analysis of historical trends and chemically intuitive route clustering that offers more granular partitioning than common previous methods. This work bridges the tactical-strategic divide in CASP, enabling specification, search, and evaluation of routes by strategic criteria rather than structure alone. Codebase ; Strategic planning ; Cluster analysis ; Python (programming language) ; Verifiable secret sharing ; Relational database Machine Learning in Materials Science ; Synthetic Organic Chemistry Methods ; Computational Drug Discovery Methods
Authors: Sanghoon Lee | Kevin Cruse | Viktoriia Baibakova | Gerbrand Ceder | Anubhav Jain
Source: ChemRxiv (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model Solid-state synthesis is widely used to obtain various inorganic materials, such as battery materials and bulk thermoelectrics. Despite its prevalence, the process remains challenging due to the lack of a general theory and well-understood underlying reaction mechanisms. While prior works have successfully extracted structured datasets from literature, they often neglect product phase purity or yield. In this work, we construct a solid-state synthesis dataset consisting of 80,823 syntheses extracted with a large language model (LLM), including 18,874 reactions with impurity phase(s). Our dataset not only validates expected thermodynamic trends for impurity phase formation but also identifies challenging cases where impurity phases emerge even when the target phase is significantly more stable. Impurity ; Solid-state ; State (computer science) ; Computer science ; Natural language processing ; Chemistry ; Algorithm ; Physical chemistry Machine Learning in Materials Science
Authors: Qian Tan | Dongzhan Zhou | Peng Xia | Wanhao Liu | Wanli Ouyang | Lei Bai | Yongping Li | Tianfan Fu
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

ChemMLLM: Chemical Multimodal Large Language Model Multimodal large language models (MLLMs) have made impressive progress in many applications in recent years. However, chemical MLLMs that can handle cross-modal understanding and generation remain underexplored. To fill this gap, we propose ChemMLLM, a unified chemical multimodal large language model for molecule understanding and generation. Also, we design five multimodal tasks across text, molecular SMILES strings, and image, and curate the datasets. We benchmark ChemMLLM against a range of general leading MLLMs and Chemical LLMs on these tasks. Experimental results show that ChemMLLM achieves superior performance across all evaluated tasks. For example, in molecule image optimization task, ChemMLLM outperforms the best baseline (GPT-4o) by 116.75\% (4.27 vs 1.97 property improvement). The code is publicly available at https://github.com/bbsbz/ChemMLLM.git. Benchmark (surveying) ; Code (set theory) ; Range (aeronautics) ; Language model ; Baseline (sea) ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Lorenzo Di Fruscia | Jana M. Weber
Source: ArXiv.org (preprint, 2025-05-08)
Field: Materials Science — Materials Chemistry

Leveraging Large Language Models for enzymatic reaction prediction and characterization Predicting enzymatic reactions is crucial for applications in biocatalysis, metabolic engineering, and drug discovery, yet it remains a complex and resource-intensive task. Large Language Models (LLMs) have recently demonstrated remarkable success in various scientific domains, e.g., through their ability to generalize knowledge, reason over complex structures, and leverage in-context learning strategies. In this study, we systematically evaluate the capability of LLMs, particularly the Llama-3.1 family (8B and 70B), across three core biochemical tasks: Enzyme Commission number prediction, forward synthesis, and retrosynthesis. We compare single-task and multitask learning strategies, employing parameter-efficient fine-tuning via LoRA adapters. Additionally, we assess performance across different data regimes to explore their adaptability in low-data settings. Our results demonstrate that fine-tuned LLMs capture biochemical knowledge, with multitask learning enhancing forward- and retrosynthesis predictions by leveraging shared enzymatic information. We also identify key limitations, for example challenges in hierarchical EC classification schemes, highlighting areas for further improvement in LLM-driven biochemical modeling. Adaptability ; Leverage (statistics) ; Key (lock) ; Multi-task learning ; Core (optical fiber) ; Language model ; Scope (computer science) ; Adaptation (eye) Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Bioinformatics and Genomic Networks