jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Oleksii Popov
Source: Periodicals of Engineering and Natural Sciences (PEN) (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Application of machine learning algorithms to predict mechanical properties of aluminum alloys Alloys used in aerospace, automotive industries and structural engineering have good strength to specific weight ratio, corrosion resistance and workability. Mechanical properties like tensile and yield strengths, elongation and hardness are vital to ensure integrity of structures. However, conventional techniques in measuring these properties are time consuming, tedious and destructive. This paper providesa framework in which limited experimental data &amp; Machine Learning algorithms are employed to evaluate the important mechanical properties of aerospace grade aluminum alloys. Data set of alloy compositions as well as processing parameters were obtained through material libraries to train and validate through techniques like Random Forest Regression (RFR), Artificial Neural Networks (ANN) and Support Vector Regression. The tensile strength was predicted with the best results for R – Squared &amp; Root Mean Squared Errors were Coefficient of Determination R2= 0.96, RMSE = 12.4 MPa using ANN and RFR were more efficient in predicting the elongation (R2&gt;0.93). The given approach demonstrated a better performance than the ordinary regression approaches with over 20 percent improvement and less experimental work up to about 60%. This approach provides the boosted &amp; scalable approach to greatly increase the pace of material design and mechanical characterization of applications in the aerospace industry. Artificial neural network ; Ultimate tensile strength ; Support vector machine ; Random forest ; Aerospace ; Mean squared error ; Test data ; Elongation ; Characterization (materials science) Machine Learning in Materials Science ; Material Selection and Properties ; Scientific and Engineering Research Topics
Authors: Adrian Krzyzanowski | Stephen D. Pickett | Péter Pogány
Source: ChemRxiv (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pre-training Data Augmentation Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pre-training data, and adversarial training on a BERT-based model for yield prediction of Buchwald-Hartwig and Suzuki-Miyaura coupling reactions using publicly available HTE datasets. We demonstrate that molecular representation choice (SMILES, DeepSMILES, SELFIES, Morgan fingerprint-based notation, IUPAC names) has minimal impact on model performance, while typically BPE and SentencePiece tokenization outperform other methods. WordPiece is strongly discouraged for SELFIES and fingerprint-based notation. Furthermore, pre-training with relatively small data sets (<100K reactions) achieves comparable performance to larger data sets containing millions of examples. A use of artificially generated domain-specific pre-training data is proposed. The artificially generated sets prove to be a good surrogate to the reaction schemes extracted from reaction data sets such as Pistachio or Reaxys. The best performance was observed for hybrid pre-training sets combining the real and the domain-specific, artificial data. Finally, we show that a novel adversarial training approach, perturbing input embeddings dynamically, improves model robustness and generalisability for yield and reaction success prediction. These findings provide valuable insights for developing robust and practical machine learning models for yield prediction in synthetic chemistry. GSK’s BERT training code base is made available to the community with this work. Lexical analysis ; Yield (engineering) ; Representation (politics) ; Training (meteorology) ; Training set ; Computer science ; Artificial intelligence ; Natural language processing ; Machine learning ; Geography ; Materials science ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Malik M. Mustafa | Shahad Falih Hassan | Hasan Sh. Majdi
Source: Periodicals of Engineering and Natural Sciences (PEN) (article, 2025-12-04)
Field: Materials Science — Biomaterials

Case of corrosion control in a closed cooling system The cooling system of the food is one of the main essential systems to maintain food quality in the food processing plant. The using of corrosion inhibitor for the cooling systems is a familiar practice to control the corrosion which is happening due to a different kind of parameters like water quality and the type of materials for the cooling system, in this work, the case of adding corrosion and scale inhibitor to a cooling system containing carbon steel and stainless steel and copper was investigated. The inhibitor was mixed of two materials which are (Sodium Tripolyphosphate as a scale inhibitor and Sodium Polyphosphate as a corrosion inhibitor). The results demonstrate that there is an improvement in reducing the corrosion rate for iron after adding the inhibitor, and the copper ions in the solution reduced if the PH values below 9.5. Corrosion ; Water cooling ; Polyphosphate ; Corrosion inhibitor ; Copper ; Carbon steel Calcium Carbonate Crystallization and Inhibition ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yunhua Lu | Mingyue Chen | Qingwei Zhang | Junan Zhang | Chao Zhang | Shiai XU | Qiuyan Bi
Source: npj Computational Materials (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Adaptive edge-aware graph convolutional with multi-task learning for simultaneous prediction of material properties The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties. For boron-doped graphene (BDG), both the band gap and work function critically influence performance in electronic and catalytic applications, yet existing machine learning (ML) approaches typically focus on single-property prediction and rely on hand-crafted features, limiting their generality. Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning (AEGCNN-MTL) for simultaneous prediction of multiple material properties. On a DFT-computed BDG dataset of 2613 structures, AEGCNN-MTL achieved high accuracy (R² = 0.9905 for band gap and 0.9778 for work function), and under identical training budgets, outperformed representative single-task GNN baselines. When transferred to the QM9 benchmark, the framework delivered competitive performance across 12 diverse quantum chemical properties, demonstrating strong generalization capability. These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput, multi-property screening and the data-driven discovery of multifunctional materials. Generalization ; Graph ; Scalability ; Convolutional neural network ; Limiting ; Function (biology) ; Focus (optics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Covalent Organic Framework Applications
Authors: H. Lam | Jiang Qiu | Xiuyuan Hu | Wentao Li | Fankun Zeng | Siwei Fu | Hao Zhang | Xiaonan Wang
Source: ArXiv.org (preprint, 2025-11-23)
Field: Materials Science — Materials Chemistry

CycleChemist: A Dual-Pronged Machine Learning Framework for Organic Photovoltaic Discovery Organic photovoltaic (OPV) materials offer a promising path toward sustainable energy generation, but their development is limited by the difficulty of identifying high performance donor and acceptor pairs with strong power conversion efficiencies (PCEs). Existing design strategies typically focus on either the donor or the acceptor alone, rather than using a unified approach capable of modeling both components. In this work, we introduce a dual machine learning framework for OPV discovery that combines predictive modeling with generative molecular design. We present the Organic Photovoltaic Donor Acceptor Dataset (OPV2D), the largest curated dataset of its kind, containing 2000 experimentally characterized donor acceptor pairs. Using this dataset, we develop the Organic Photovoltaic Classifier (OPVC) to predict whether a material exhibits OPV behavior, and a hierarchical graph neural network that incorporates multi task learning and donor acceptor interaction modeling. This framework includes the Molecular Orbital Energy Estimator (MOE2) for predicting HOMO and LUMO energy levels, and the Photovoltaic Performance Predictor (P3) for estimating PCE. In addition, we introduce the Material Generative Pretrained Transformer (MatGPT) to produce synthetically accessible organic semiconductors, guided by a reinforcement learning strategy with three objective policy optimization. By linking molecular representation learning with performance prediction, our framework advances data driven discovery of high performance OPV materials. Photovoltaic system ; Generative grammar ; Artificial neural network ; Deep learning ; Classifier (UML) ; Organic solar cell ; Transformer Machine Learning in Materials Science ; Photovoltaic System Optimization Techniques ; Explainable Artificial Intelligence (XAI)
Authors: Christophe Bajan | G. Lambard
Source: arXiv (Cornell University) (preprint, 2025-01-08)
Field: Materials Science — Materials Chemistry

Exploring the Expertise of Large Language Models in Materials Science and Metallurgical Engineering The integration of artificial intelligence into various domains is rapidly increasing, with Large Language Models (LLMs) becoming more prevalent in numerous applications. This work is included in an overall project which aims to train an LLM specifically in the field of materials science. To assess the impact of this specialized training, it is essential to establish the baseline performance of existing LLMs in materials science. In this study, we evaluated 15 different LLMs using the MaScQA question answering (Q&amp;A) benchmark. This benchmark comprises questions from the Graduate Aptitude Test in Engineering (GATE), tailored to test models' capabilities in answering questions related to materials science and metallurgical engineering. Our results indicate that closed-source LLMs, such as Claude-3.5-Sonnet and GPT-4, perform the best with an overall accuracy of ~84%, while the open-source models, Llama3-70b and Phi3-14b, top at ~56% and ~43%, respectively. These findings provide a baseline for the raw capabilities of LLMs on Q&amp;A tasks applied to materials science, and emphasize the substantial improvement that could be brought to open-source models via prompt engineering and fine-tuning strategies. We anticipate that this work could push the adoption of LLMs as valuable assistants in materials science, demonstrating their utility in this specialized domain and related sub-domains. Engineering ; Computer science ; Engineering ethics ; Construction engineering Machine Learning in Materials Science
Authors: Wei Zhang | Yidong Zhang | Jiacheng Xiong | Mingyue Zheng | Kaixian Chen
Source: Scientia Sinica Chimica (review, 2025-06-25)
Field: Materials Science — Materials Chemistry

The converging moment of chemistry and artificial intelligence: a review and outlook 人工智能和化学领域的交叉融合正在变革化学研究范式。本文系统梳理了人工智能在化学数据提取、化学反应预测和优化、合成路径规划以及化学智能体等方面的最新进展，并结合实际应用案例，展示其辅助实验设计、提高实验效率、推动研究迈向智能化与自动化等方面的显著优势。同时，本文围绕数据困境、模型局限、实验自动化等现有挑战，探讨了未来发展方向，以期为智能化学的全链路构建提供参考和启发，推动新药开发、绿色合成和材料设计等领域的发展。 Moment (physics) ; Chemistry ; Management science ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kiran Mehmood | Rusul Alabada | Muhammad Imran Arshad | Nehal Ali | Nasir Amin | Ehtsham Ul Haq | M.S. Al-Buriahi | S.F. Mahmoud | S. Mumtaz | Abd ur Rehman
Source: Journal of Ovonic Research (article, 2025-06-15)
Field: Materials Science — Materials Chemistry

Magneto-dielectric, optical and electrical behavior of Zn0.5Co0.25Cu0.25Re0.125Fe1.875O4 (Re = La3+ and Ce3+) spinel ferrites In this study, we investigate the structural, optical, and electrical properties of Zn0.5Co0.25Cu0.25Fe2O4 (ZCCF) spinel ferrites (SFs) and its La³⁺- and Ce³⁺-substituted counterparts (ZCCLF and ZCCCF) SFs, synthesized using the sol-gel auto combustion method. The primary focus is on analyzing the temperature-dependent resistivity behavior of these SFs. All samples exhibit semiconducting behavior, with resistivity decreasing as temperature increases from 400 K to 650 K. Undoped ZCCF shows the lowest resistivity, while doping with La³⁺ and Ce³⁺ significantly increases resistivity due to disruptions in charge carrier mobility caused by lattice distortions. Ce³⁺-doped ZCCCF exhibits the highest resistivity at all temperatures, highlighting the stronger influence of Ce³⁺ compared to La³⁺ on the ferrite’s electrical properties. The magnetic properties of ZCCF, ZCCLF, and ZCCCF were analyzed, revealing distinct behaviors. ZCCF exhibited the highest saturation magnetization (68.68 emu/g) and resonance frequency (15.18 GHz), making it suitable for high-frequency applications. ZCCLF showed the highest coercivity (934.18 Oe) and anisotropy, ideal for permanent magnets, while ZCCCF displayed the lowest coercivity (46.43 Oe), indicating soft magnetic characteristics for easy magnetization switching. Spinel ; Magneto ; Materials science ; Dielectric ; Optoelectronics ; Metallurgy ; Electrical engineering ; Engineering ; Voltage Magnetic Properties and Synthesis of Ferrites ; Magneto-Optical Properties and Applications ; Magnetic Properties and Applications
Authors: Shifu Hou | Rick Johnson | Ramandeep Makhija | Lingwei Chen | Yanfang Ye
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

AutoFEA: Enhancing AI Copilot by Integrating Finite Element Analysis Using Large Language Models with Graph Neural Networks Large Language Models (LLMs) have demonstrated significant potential across various applications, but their use as AI copilots in complex and specialized tasks is often hindered by AI hallucinations, where models generate outputs that seem plausible but are incorrect. To address this challenge, we develop AutoFEA, an intelligent system that integrates LLMs with Finite Element Analysis (FEA) to automate the generation of FEA input files. Our approach features a novel planning method and a graph convolutional network (GCN)-Transformer Link Prediction retrieval model, which enhances the accuracy and reliability of the generated simulations. The AutoFEA system proceeds with key steps: dataset preparation, step-by-step planning, GCN-Transformer Link Prediction retrieval, LLM-driven code generation, and simulation using CalculiX. In this workflow, the GCN-Transformer model predicts and retrieves relevant example codes based on relationships between different steps in the FEA process, guiding the LLM in generating accurate simulation codes. We validate AutoFEA using a specialized dataset of 512 meticulously prepared FEA projects, which provides a robust foundation for training and evaluation. Our results demonstrate that AutoFEA significantly reduces AI hallucinations by grounding LLM outputs in physically accurate simulation data, thereby improving the success rate and accuracy of FEA simulations and paving the way for future advancements in AI-assisted engineering tasks. Computer science ; Finite element method ; Graph ; Artificial neural network ; Artificial intelligence ; Engineering ; Theoretical computer science ; Structural engineering Machine Learning in Materials Science ; Model-Driven Software Engineering Techniques ; Natural Language Processing Techniques
Authors: Yi Luo | Zhong‐Qun Tian | Jinghong Li | Jun Jiang | Bin Jiang | Linjiang Chen | Song Wang | Shuo Feng | Meng Huang | Xiangjian Shen
Source: Bulletin of National Natural Science Foundation of China (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Substance Science Insight Based on Spectroscopic Large Model The traditional substance science theories based on structural coordinates are confined to studying limited dimensions，static structures，and microscopic details，making it difficult to handle high-dimensional，dynamically complex chemical systems. To develop new substance science theories that do not rely on structural coordinates and to establish novel approaches for chemical innovation，the National Natural Science Foundation of China successfully organized the 374<sup>th</sup> Shuangqing Forum titled “Cognitive Material Science Based on Spectral Big Models”. This article aims to summarize the consensus reached at the forum and analyze the recent research progress and trend in the field of integrating spectroscopy and artificial intelligence domestically and abroad. It focuses on the significant opportunities and challenges currently faced in this field. The paper distills the key scientific frontier and core technologies for the future 5~10<?A3B2 pi129?>years in this field，such as establishing standardized in-situ spectroscopic database，development of intelligent algorithm for spectroscopic data analysis，construction and application of spectroscopic large model，and development of efficient substance research platform driven by spectroscopic large models. In addition，the potential scientific research funding strategy is also suggested. Natural science ; Field (mathematics) ; Big data ; Frontier ; Molecular spectroscopy ; Key (lock) ; Core (optical fiber) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Youngchun Kwon | Hyunjeong Jeon | Joon Hyuk Choi | Youn-Suk Choi | Seokho Kang
Source: Journal of Cheminformatics (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop In synthesis planning, identifying and optimizing chemical reactions are important for the successful design of synthetic pathways to target substances. Chemical reaction databases assist chemists in gaining insights into this process. Traditionally, searching for relevant records from a reaction database has relied on the manual formulation of queries by chemists based on their search purposes, which is challenging without explicit knowledge of what they are searching for. In this study, we propose an intelligent chemical reaction search system that simplifies the process of enhancing the search results. When a user submits a query, a list of relevant records is retrieved from the reaction database. Users can express their preferences and requirements by providing binary ratings for the individual retrieved records. The search results are refined based on the user feedback. To implement this system effectively, we incorporate and adapt contrastive representation learning, dimensionality reduction, and human-in-the-loop techniques. Contrastive learning is used to train a representation model that embeds records in the reaction database as numerical vectors suitable for chemical reaction searches. Dimensionality reduction is applied to compress these vectors, thereby enhancing the search efficiency. Human-in-the-loop is integrated to iteratively update the representation model by reflecting user feedback. Through experimental investigations, we demonstrate that the proposed method effectively improves the chemical reaction search towards better alignment with user preferences and requirements. Scientific contribution This study seeks to enhance the search functionality of chemical reaction databases by drawing inspiration from recommender systems. The proposed method simplifies the search process, offering an alternative to the complexity of formulating explicit query rules. We believe that the proposed method can assist users in efficiently discovering records relevant to target reactions, especially when they encounter difficulties in crafting detailed queries due to limited knowledge. Computer science ; Process (computing) ; Representation (politics) ; Chemical database ; Human-in-the-loop ; Dimensionality reduction ; Information retrieval ; Loop (graph theory) ; Data mining ; Feedback loop ; Database ; Artificial intelligence ; Machine learning ; World Wide Web ; Bioinformatics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Lixin Xuan | Zhiqiang Wang | Xuan Yang | Wu Xiao | Junjiao Yang | Shijun Zheng
Source: Materials (article, 2025-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on Preventing High-Density Materials from Settling in Liquid Resin The applications of magnetic particles in anti-counterfeiting and anti-absorbing coatings and other functional materials are becoming increasingly widespread. However, due to their high density, the magnetic particles rapidly settle in organic resin media, significantly affecting the quality of the related products. Thereby, reducing the density of the particles is essential. To achieve this goal, high-density magnetic particles were coated onto the surface of hollow silica using anion-cation composite technology. Further, the silane coupling agent N-[3-(trimethoxysilyl)propyl]ethylenediamine was bonded to the surface of magnetic particles to form an amino-covered interfacial layer with a pH value of 9.28, while acrylic acid was polymerized and coated onto the surface of hollow silica to form a carboxyl-covered interfacial layer with a pH value of 4.65. Subsequently, the two materials were compounded to obtain a low-density composite magnetic material. The morphologies and structural compositions of the magnetic composite materials were studied by FTIR, SEM, SEM-EDS, XRD, and other methods. The packing densities of the magnetic composite materials were compared using the particle packing method, thereby solving the problem of magnetic particles settling in the resin solution. Materials science ; Composite number ; Settling ; Magnetic nanoparticles ; Composite material ; Chemical engineering ; Particle (ecology) ; Silane ; Layer (electronics) ; Fourier transform infrared spectroscopy ; Ethylenediamine ; Nanotechnology ; Nanoparticle ; Chemistry ; Organic chemistry Electromagnetic wave absorption materials ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Yonghong Tian | Zijun Chen | Yu Wang | Liuzhenghao Lv | Hao Li | Zhen Lin | Li Yuan
Source: Research Square (preprint, 2025-01-20)
Field: Materials Science — Materials Chemistry

Leveraging Domain Motif Assembler for Multi-objective, Multi-domain and Explainable Molecular Design Motif (music) ; Domain (mathematical analysis) ; Computer science ; Computational biology ; Art ; Biology ; Aesthetics ; Mathematics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation