jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yao Du | Xin Qu
Source: Preprints.org (preprint, 2025-11-04)
Field: Materials Science — Materials Chemistry

Borophene-Based Nanomaterials for Energy and Biomedical Applications: Progress, Challenges, and Outlook Since the first successful synthesis of borophene in 2015, this atomically thin boron allotrope has attracted extensive attention due to its polymorphic structures, metallic conductivity, and outstanding mechanical flexibility. As a new member of the two-dimensional (2D) materials family, borophene exhibits a unique triangular lattice with tunable hexagonal vacancies, leading to rich structural diversity and anisotropic physical properties. Recent breakthroughs in synthesis—particularly molecular beam epitaxy (MBE), chemical vapor deposition (CVD), and solvothermal-assisted liquid-phase exfoliation (S-LPE)—have significantly expanded the accessible structural phases and improved control over film quality and stability. Meanwhile, borophene’s distinctive combination of structural and electronic characteristics has enabled its rapid development in both energy and biomedical applications. In energy storage, borophene serves as a promising anode material for lithium/sodium-ion batteries and a lightweight medium for hydrogen storage and supercapacitors, owing to its metallic conductivity, high surface charge density, and large adsorption capacity. In biomedicine, borophene-based nanoplatforms exhibit excellent photothermal conversion efficiency, enabling multifunctional roles in cancer diagnosis and therapy. Despite these advances, several challenges—such as environmental instability, oxidation susceptibility, and limited scalable synthesis—continue to restrict practical implementation. Future progress will depend on chemical functionalization, surface passivation, and machine-learning-assisted materials design to achieve oxidation-resistant, large-area, and biocompatible borophene derivatives. This review summarizes recent advances in borophene synthesis, structural engineering, and multifunctional applications, while outlining key scientific challenges and future opportunities for the realization of borophene-based materials in next-generation energy and biomedical systems. Borophene ; Nanomaterials ; Chemical vapor deposition ; Graphene ; Nanomesh ; Phosphorene ; Biocompatible material Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry ; Organoboron and organosilicon chemistry
Authors: Seema Agarwal | Xiong Wei | Rampi Ramprasad
Source: ArXiv.org (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

polyRETRO: a Language Model Approach to predict Polymerization Class and Monomer(s) for a Target Polymer While machine learning has transformed polymer design by enabling rapid property prediction and candidate generation, translating these designs into experimentally realizable materials remains a critical challenge. Traditionally, the synthesis of target polymers has relied heavily on expert intuition and prior experience. The lack of automated retrosynthetic tools to assist chemists, limit the rapid practical impact of data-driven polymer discovery. To expedite lab-scale validation and beyond, we present a retrosynthetic framework that leverages large language models (LLMs) to guide polymer synthesis. Our approach, which we call polyRETRO, involves two key steps: 1) predicting the most likely polymerization reaction class of a target polymer and 2) identifying the underlying chemical transformation templates and the corresponding monomers, using primarily natural-language based constructs. This LLM-driven framework enables direct retrosynthetic analysis given just the target polymer SMILES string. polyRETRO constitutes a initial step towards a scalable, interpretable, and generalizable approach to bridge the gap between computational design and experimental synthesis. Retrosynthetic analysis ; Intuition ; Polymer ; Bridge (graph theory) ; Class (philosophy) ; Polymerization ; Property (philosophy) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Synthetic Organic Chemistry Methods
Authors: Kewei Wang
Source: Arch (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Scalable Training Strategy for Parallel Neural Network and Interpretable Materials Modeling Artificial neural network ; Training (meteorology) ; Scalability ; Training set ; Scale (ratio) ; Deep learning ; Key (lock) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: Ye Wei | Chao Yang | Bo Peng | Qingzhou Tao | Binhan Sun | Da Shu | Liuliu Han
Source: Research Square (preprint, 2025-08-01)
Field: Materials Science — Materials Chemistry

Designing alloy from 100% scrap with multi-modal Artificial Intelligence Scrap ; Materials science ; Ductility (Earth science) ; Ultimate tensile strength ; Alloy ; Impurity ; Microstructure ; Pipeline (software) ; Metallurgy ; Process engineering ; Mechanical engineering ; Computer science ; Engineering Machine Learning in Materials Science
Authors: 文創 商 | Wenchuang Shang | 義弘 合田 | Yoshihiro Gohda
Source: Institutional Repositories DataBase (IRDB) (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetostriction in Fe-Based Alloys: A First-principles and Machine Learning Study Magnetostriction ; Support vector machine ; Noise (video) ; Feature (linguistics) ; Population Magnetic Properties and Applications ; Magnetic properties of thin films ; Metallic Glasses and Amorphous Alloys
Authors: 商文創 | Wenchuang SHANG | 合田義弘 | Yoshihiro GOHDA
Source: Tokyo Tech Research Repository (Tokyo Institute of Technology) (article, 2025-10-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetostriction in Fe-Based Alloys: A First-principles and Machine Learning Study Magnetostriction ; Support vector machine ; Noise (video) ; Feature (linguistics) ; Population Magnetic Properties and Applications ; Magnetic properties of thin films ; Metallic Glasses and Amorphous Alloys
Authors: Liang Cao | Yuwen Zhao | Xialin Chen | Haoran Zhang | Ni Zhao | Yuqing Ding | Shuyun Duan | Mengyu Qian | Guoqing Ren | Zhenzhong Wang | Xiao Wei
Source: Measurement (article, 2025-09-23)
Field: Chemistry — Analytical Chemistry

Integrated quality evaluation of traditional Chinese medicine based on chemical composition and biological effects using high-level data fusion of near-infrared and ultraviolet spectroscopy combined with machine learning Random forest ; Support vector machine ; Sensor fusion ; Fusion ; Data pre-processing ; Preprocessor ; Feature selection ; Pattern recognition (psychology) ; Experimental data Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Traditional Chinese Medicine Studies
Authors: Dat Phat Nguyen | Quyet Minh Le
Source: Vietnam Journal of Chemistry (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Optimizing molecular property and reaction yield prediction with feature engineering and molecular fingerprints Abstract The accurate prediction of molecular properties and reaction outcomes has long been a main topic of research in computational chemistry. In this work, we explore the synergy between various molecular features and molecular fingerprints to enhance the predictive power of machine learning models for these tasks. Leveraging several machine learning frameworks, we systematically trained and evaluated models using different combinations of features to identify those that offer optimal predictive performance. The combination of diverse molecular features and molecular fingerprints demonstrated a marked improvement in predictions, providing deeper insights into the intricate relationship between molecular structure and its associated properties or reactions. To facilitate the practical application of these findings, we developed a user‐friendly interface, streamlining the process of feature extraction and model prediction. This research underscores the importance of feature engineering in cheminformatics and the potential of machine learning to enhance our predictive capabilities for complex molecular behaviors. Cheminformatics ; Molecular descriptor ; Fingerprint (computing) ; Feature (linguistics) ; Process (computing) ; Property (philosophy) ; Feature engineering ; Feature extraction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Yasmine Benabed | Pablo Roseiro | Kirk H. Bevan | Chisu Kim
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Combining Machine Learning and DFT to Design New Ionic Superconductor Halide Materials for All-Solid-State Lithium Batteries: A High-Throughput Study Solid electrolytes (SEs) are developed as a key element in all-solid-state lithium batteries. They are expected to overcome the safety issues associated with conventional liquid electrolytes and to unlock higher power densities, through the use of lithium metal and high-potential active materials. To this end, different families of solid-state superionic conductors have been investigated since 1990’s, including polymers, garnets, perovskites, NASICONs and sulfides. More recently, studies have put forward halides as very promising solid-state electrolytes. Some halide materials (e. g., Li 3 InCl 6 , LiYbCl 4 ) compete with oxide and sulfide SEs on several aspects, like the ionic conductivity, electrochemical stability window and moisture resistance, while circumventing their biggest disadvantages, such as the extreme sensitivity to moisture observed in sulfides and the high temperatures needed to process oxides. The main limitations of the best performing halides such as Li 3 InCl 6 are the high synthesis cost and its rather narrow electrochemical stability window. In order to find better performing halides, the battery community has turned to high-throughput screening based on computational tools. These methods have proven to be more efficient than the conventional trial-and-error approach, and to allow the exploration of extensive chemical spaces, at a fraction of the time used experimentally. In this presentation, we have used a high throughput workflow developed by our research group to design new halide materials, using Li 3 InCl 6 as structural framework, where In and Cl are substituted by more affordable elements that were proven to improve SEs performances. Throughput ; Halide ; Lithium (medication) ; Ionic bonding ; Solid-state ; Materials science ; Superconductivity ; Computer science ; Nanotechnology ; Ion ; Chemistry ; Inorganic chemistry ; Physical chemistry ; Physics ; Condensed matter physics ; Telecommunications ; Psychology ; Organic chemistry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Inorganic Fluorides and Related Compounds
Authors: Qian Liu | Yue Li | Ying Xie | Jian Zhang | Pai Li | Yue Yu | Zhe Liu | Feng Ding | Jin Zhang
Source: ArXiv.org (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Enabled Holistic Design of Catalysts Tailored for Semiconducting Carbon Nanotube Growth Catalyst design is crucial for materials synthesis, especially for complex reaction networks. Strategies like collaborative catalytic systems and multifunctional catalysts are effective but face challenges at the nanoscale. Carbon nanotube synthesis contains complicated nanoscale catalytic reactions, thus achieving high-density, high-quality semiconducting CNTs demands innovative catalyst design. In this work, we present a holistic framework integrating machine learning into traditional catalyst design for semiconducting CNT synthesis. It combines knowledge-based insights with data-driven techniques. Three key components, including open-access electronic structure databases for precise physicochemical descriptors, pre-trained natural language processing-based embedding model for higher-level abstractions, and physical - driven predictive models based on experiment data, are utilized. Through this framework, a new method for selective semiconducting CNT synthesis via catalyst - mediated electron injection, tuned by light during growth, is proposed. 54 candidate catalysts are screened, and three with high potential are identified. High-throughput experiments validate the predictions, with semiconducting selectivity exceeding 91% and the FeTiO3 catalyst reaching 98.6%. This approach not only addresses semiconducting CNT synthesis but also offers a generalizable methodology for global catalyst design and nanomaterials synthesis, advancing materials science in precise control. Catalysis ; Carbon nanotube ; Nanomaterials ; Nanoscopic scale ; Carbon fibers ; Nanoparticle ; Nanotube Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Sravan Kumar Josyula | Y. Noiman | E. J. Payton | Tommaso Giovannelli
Source: ArXiv.org (preprint, 2025-08-19)
Field: Materials Science — Materials Chemistry

Comparison of derivative-free and gradient-based minimization for multi-objective compositional design of shape memory alloys Designing shape memory alloys (SMAs) that meet performance targets while remaining affordable and sustainable is a complex challenge. In this work, we focus on optimizing SMA compositions to achieve a desired martensitic start temperature (Ms) while minimizing cost. To do this, we use machine learning models as surrogate predictors and apply numerical optimization methods to search for suitable alloy combinations. We trained two types of machine learning models, a tree-based ensemble and a neural network, using a dataset of experimentally characterized alloys and physics-informed features. The tree-based model was used with a derivative-free optimizer (COBYLA), while the neural network, which provides gradient information, was paired with a gradient-based optimizer (TRUST-CONSTR). Our results show that while both models predict Ms with similar accuracy, the optimizer paired with the neural network finds better solutions more consistently. COBYLA often converged to suboptimal results, especially when the starting guess was far from the target. The TRUST-CONSTR method showed more stable behavior and was better at reaching alloy compositions that met both objectives. This study demonstrates a practical approach to exploring new SMA compositions by combining physics-informed data, machine learning models, and optimization algorithms. Although the scale of our dataset is smaller than simulation-based efforts, the use of experimental data improves the reliability of the predictions. The approach can be extended to other materials where design trade-offs must be made with limited data. Shape-memory alloy ; Artificial neural network ; Minification ; SMA* ; Reliability (semiconductor) ; Focus (optics) ; Scale (ratio) Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: Sergei Manzhos
Source: Institutional Repositories DataBase (IRDB) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for large(r) scale materials modeling: simple's the best Scale (ratio) ; Feature (linguistics) ; Measure (data warehouse) ; Margin (machine learning) ; Stability (learning theory) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Composite Material Mechanics
Authors: Pablo Roseiro | Kirk H. Bevan | Chisu Kim | Yasmine Benabed
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Design of a High-Throughput Workflow to Accelerate the Discovery of New Solid Superionic Conductor Materials Solid electrolytes (SE) are the central element in all-solid-state lithium batteries. Recent studies have put forward several promising families of SEs, among them garnet, LISICON, halides... However, even the best performing SEs present drawbacks in terms of synthesis cost, electrochemical stability window, and stability against lithium metal, which motivates the need to develop better materials. Computational high-throughput screening has proven to be more efficient than the conventional trial-and-error approach when extensively investigating the chemical spaces of new materials, at a fraction of the time used experimentally. In this work, we developed a high-throughput workflow based on theoretical considerations, machine learning and DFT to design new materials. We have selected the promising family of halides to proof test our workflow. This workflow was designed to efficiently explore targeted chemical spaces by reducing computational costs and maintaining high accuracies. It was developed to be robust and versatile as it can be used at large scale, far beyond halide materials. Workflow ; Throughput ; Conductor ; Fast ion conductor ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Database ; Composite material ; Operating system ; Electrode Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Waleed M. Hamanah | Abdullrahman A. Al-Shamma’a | Ali. A. Salem | Ebrahim Al-wajih | Khaled Al-Soufi | Hassan M. Hussein Farh
Source: Scientific Reports (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Hybrid machine learning predictions of high voltage polymeric insulator pollution This study presents a comprehensive approach for predicting pollution levels on high-voltage insulators in the Eastern Region of Saudi Arabia using five machine learning techniques integrated with the Improved Harris Hawks Optimizer (IHHO). The focus is on accurately estimating two key pollution metrics: Equivalent Salt Deposit Density and Normalized Salt Deposit Density. IHHO was used for hyperparameter optimization to guarantee that all proposed models function adequately. The prediction model is constructed from one year’s worth of environmental data, which includes temperature, humidity, wind speed, solar radiation, elevation, dew point, precipitation, line voltage, and service period. Of all evaluated models, the hybrid IHHO-XGBoost model performed best, with an R2 of 0.993, a MAE of 0.0002, an RMSE of 0.00015, a MedAE of 3.563 × 10⁻5, an EV of 0.992, and an Adj R2 of 0.9923 with tenfold cross-validation. Model validation using Taylor diagram analysis confirmed a high degree of agreement between predicted and actual values. Furthermore, application of the SHAP (SHapley Additive Explanation) technique revealed that the most important predictors were wind speed, temperature, line voltage, and solar radiation. In addition, the results were compared to the results of other benchmark models to improve model explained accuracy and trustworthiness. Not only did the IHHO-XGBoost model best others in accuracy, it equally enhanced understanding of the environmental and operational factors that cause insulator contamination. These predictive capabilities support more effective condition monitoring and maintenance planning, ultimately contributing to improved reliability of electrical grid infrastructure in harsh environments. Hyperparameter ; Hyperparameter optimization ; Insulator (electricity) ; Predictive modelling ; Pollution ; Mean squared error ; Reliability (semiconductor) ; Grid High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Jiali Ren | Guanghao Zhang | Xuemei Wang | Ye Han
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Research on Machine Learning Interatomic Potentials for Titanium Oxide Ceramic Materials Ceramic ; Materials science ; Titanium ; Interatomic potential ; Oxide ; Titanium oxide ; Composite material ; Metallurgy ; Chemical engineering ; Chemistry ; Computational chemistry ; Engineering ; Molecular dynamics Machine Learning in Materials Science
Authors: Musa Husaini
Source: Journal of Applied Sciences and Environmental Management (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Machine Learning and Computational Nanochemistry for Predictive Adsorption Studies The integration of machine learning (ML) with computational nanochemistry is significantly advancing predictive adsorption studies by enabling faster, more precise, and scalable modeling of adsorption processes at the nanoscale. This review outlines recent developments in ML techniques including regression models, deep neural networks, and hybrid physics-informed approaches and their combination with computational methods such as density functional theory (DFT), molecular dynamics (MD), and Monte Carlo simulations. Critical steps in the workflow, including data collection, feature engineering, model training, and validation, are examined in detail, along with challenges such as limited data availability, issues with model interpretability, and the transferability of models across diverse material systems. Emerging innovations like graph neural networks and active learning are discussed as promising approaches to overcome these challenges. Applications spanning gas storage, catalysis, environmental remediation, and drug delivery showcase the broad impact of these integrated strategies. This comprehensive review aims to assist researchers in leveraging the strengths of ML and computational nanochemistry to accelerate adsorption research and guide materials design. Nanochemistry ; Scalability ; Transferability ; Artificial neural network ; Adsorption ; Prioritization ; Benchmark (surveying) ; Computational model Machine Learning in Materials Science ; Adsorption and biosorption for pollutant removal ; Surface Chemistry and Catalysis
Authors: Jin, Emily | Nica, Andrei Cristian | Galkin, Mikhail | Rector-Brooks, Jarrid | Lee, Kin Long Kelvin | Miret, Santiago | Arnold, Frances H. | Bronstein, Michael | Bose, Avishek Joey | Tong, Alexander | Liu, Cheng-Hao
Source: ArXiv.org (article, 2025-12-07)
Field: Materials Science — Materials Chemistry

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction Accurately predicting experimentally-realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called crystal structure prediction (CSP). Efficiently solving this problem has implications ranging from pharmaceuticals to organic semiconductors, as crystal packing directly governs the physical and chemical properties of organic solids. In this paper, we introduce OXtal, a large-scale 100M parameter all-atom diffusion model that directly learns the conditional joint distribution over intramolecular conformations and periodic packing. To efficiently scale OXtal, we abandon explicit equivariant architectures imposing inductive bias arising from crystal symmetries in favor of data augmentation strategies. We further propose a novel crystallization-inspired lattice-free training scheme, Stoichiometric Stochastic Shell Sampling ($S^4$), that efficiently captures long-range interactions while sidestepping explicit lattice parametrization -- thus enabling more scalable architectural choices at all-atom resolution. By leveraging a large dataset of 600K experimentally validated crystal structures (including rigid and flexible molecules, co-crystals, and solvates), OXtal achieves orders-of-magnitude improvements over prior ab initio machine learning CSP methods, while remaining orders of magnitude cheaper than traditional quantum-chemical approaches. Specifically, OXtal recovers experimental structures with conformer $\text{RMSD}_1<0.5$ Å and attains over 80\% packing similarity rate, demonstrating its ability to model both thermodynamic and kinetic regularities of molecular crystallization. Crystal structure prediction ; Ab initio ; Crystal (programming language) ; Scalability ; Lattice (music) ; Parametrization (atmospheric modeling) ; Crystal structure ; Homogeneous space Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods