jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruoyu Wang | Yuxiang Gao | Hongyu Wu | Zhicheng Zhong
Source: Physical Review Materials (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models Universal force fields generalizable across the periodic table represent a new trend in computational materials science. However, the applications of universal force fields in material simulations are limited by their slow inference speed and the lack of first-principles accuracy. Instead of building a single model simultaneously satisfying these characteristics, a strategy that quickly generates material-specific models from the universal model may be more feasible. Here, we propose a new workflow pattern, PFD (Pre-training, Fine-tuning, and Distillation), which automatically generates machine-learning force fields for specific materials from a pre-trained universal model through fine-tuning and distillation. By fine-tuning the pre-trained model, our PFD workflow generates force fields with first-principles accuracy while requiring one to two orders of magnitude less training data compared to traditional methods. The inference speed of the generated force field is further improved through distillation, meeting the requirements of large-scale molecular simulations. Comprehensive testing across diverse materials including complex systems, such as amorphous carbon, interface, etc., reveals marked enhancements in training efficiency, which suggests the PFD workflow a practical and reliable approach for force field generation in computational material sciences. Workflow ; Inference ; Force field (fiction) ; Field (mathematics) ; Table (database) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Yukari Katsura | Masaya Kumagai | Tomoya Mato | Yu Takada | Yuki Ando | Erina Fujita | Fumikazu Hosono | Eiji Koyama | Farhan Mudasar | T. Phuong | Noriko Saito | Yoshihiro Sakamoto | Atsumi Tanaka | Dewi Yana | Kaoru Kimura | Koji Tsuda | Masahiko Demura
Source: Science and Technology of Advanced Materials Methods (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Starrydata: from published plots to shared materials data We have developed the Starrydata2 web system, an open, web-based database for collecting and organizing experimental material property data from the literature. It assists users worldwide in extracting and sharing curve data from plot images in published papers, along with relevant sample information such as chemical compositions and fabrication methods. Starrydata2 streamlines the manual data collection process through partial automation. Currently, Starrydata encompasses over 194,000 curves extracted from more than 82,000 physical samples, as reported in over 13,000 publications on functional inorganic materials, including thermoelectric and magnetic materials. All data in Starrydata are openly accessible to the public for both commercial and non-commercial purposes. In this paper, we introduce the web interface, data curation workflow, data structure, and system architecture of Starrydata2. We then described in detail the datasets currently included in Starrydata2 and discuss their use cases. We also present the methods for applying the collected dataset, including a unique large-scale data representation method called ‘all-data plots’, which provides a comprehensive overview of the entire dataset. Finally, we report on how the collected datasets are being utilized in data-driven materials research through machine learning, modelling and simulation. Computer science ; Data science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Thermoelectric Materials and Devices
Authors: Shadi Askari | Haniyeh Salimifard | Reza Alavi | Mahnaz Eskandari | Atefeh Zarepour | Arezoo Khosravi | Siavash Iravani | Ali Zarrabi
Source: ACS Applied Nano Materials (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Two-Dimensional Nanomaterial-Based Aptasensors: Graphene and MXenes as Emerging Biosensing Platforms Recent advances in graphene- and MXene-based aptasensors have transformed biosensing by integrating the high specificity of aptamers with the unique physicochemical properties of two-dimensional (2D) nanomaterials. Graphene and its derivatives provide a large surface area, excellent conductivity, mechanical flexibility, and biocompatibility, enabling efficient aptamer immobilization, signal amplification, and sensor stability. MXenes, with their hydrophilic surfaces, high electrical conductivity, and versatile surface chemistry, offer ultrahigh sensitivity and tunability across diverse sensing platforms. This review presents a comparative analysis of graphene- and MXene-based aptasensors, covering synthesis and functionalization strategies, sensing mechanisms (electrochemical, fluorescent, colorimetric), and their integration for detecting biomarkers, pathogens, toxins, heavy metals, and small molecules. Particular attention is given to hybrid nanostructures, field-effect transistor (FET)-based devices, and nanocomposites that exploit synergistic effects to improve detection limits, selectivity, and real-time responsiveness. Critical challenges, including oxidation, nonspecific adsorption, reproducibility, large-scale fabrication, and device integration, are also addressed. Finally, we discuss future directions such as graphene–MXene heterostructures, machine learning-assisted signal processing, and wearable or implantable biosensors for personalized medicine and environmental monitoring. By consolidating current progress and highlighting knowledge gaps, this review aims to guide the design of next-generation aptasensors with real-world applicability. MXenes ; Graphene ; Nanotechnology ; Computer science ; Materials science MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Graphene and Nanomaterials Applications
Authors: Jie Li | Linxue Zhen | Zhichun Bi | Yu Ma | Hui Huang | Yongxin Li
Source: Chemical Engineering Journal (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Three-channel Sensor Array based on Switchable Nanozymatic Reactions of a Novel Trimetallic Nanozyme for Anti-interference Identification of Tea Adulteration Interference (communication) ; Identification (biology) ; Channel (broadcasting) ; Chemistry ; Computer science ; Telecommunications ; Biology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Gaia Meoni | Leonardo Tenori | Francesca Di Cesare | Stefano Brizzolara | Pietro Tonutti | Chiara Cherubini | Laura Mazzanti | Claudio Luchinat
Source: Computational and Structural Biotechnology Journal (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil This study investigates the potential of <sup>1</sup>H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. We applied Random Forest regression models to correlate NMR spectra with these parameters, achieving promising results, particularly for predicting major fatty acids, total polyphenols, and tocopherols. We have also found the collected data to be highly effective in classifying olive cultivars and the years of harvest. Our findings highlight the potential of NMR spectroscopy as a rapid, non-destructive, and environmentally friendly tool for olive oil quality assessment. This study introduces a novel approach that combines machine learning with <sup>1</sup>H NMR spectral analysis to correlate analytical data for predicting essential qualitative parameters in olive oil. By leveraging <sup>1</sup>H NMR spectra as predictive proxies, this methodology offers a promising alternative to traditional assessment techniques, enabling rapid determination of several parameters related to chemical composition, sensory attributes, and geographical origin of olive oil samples. Metabolomics ; Olive oil ; Chemistry ; Environmental science ; Environmental chemistry ; Food science ; Chromatography Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Isotope Analysis in Ecology
Authors: Kehan Guo | Yili Shen | Gisela Abigail Gonzalez-Montiel | Yi Huang | Yujun Zhou | Mihir Surve | Zhichun Guo | Partha Pratim Das | Nitesh V. Chawla | Olaf Wiest | Xiangliang Zhang
Source: ArXiv.org (preprint, 2025-02-14)
Field: Chemistry — Physical and Theoretical Chemistry

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data, referred to as Spectroscopy Machine Learning (SpectraML), remains relatively underexplored. Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent analysis beyond traditional expert-based workflows. In this survey, we provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks (molecule-to-spectrum prediction) and inverse tasks (spectrum-to-molecule inference). We trace the historical evolution of ML in spectroscopy, from early pattern recognition to the latest foundation models capable of advanced reasoning, and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods. Addressing key challenges such as data quality, multimodal integration, and computational scalability, we highlight emerging directions such as synthetic data generation, large-scale pretraining, and few- or zero-shot learning. To foster reproducible research, we also release an open-source repository containing recent papers and their corresponding curated datasets (https://github.com/MINE-Lab-ND/SpectrumML_Survey_Papers). Our survey serves as a roadmap for researchers, guiding progress at the intersection of spectroscopy and AI. Spectroscopy ; Chemistry ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Physics ; Astronomy Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies ; Water Quality Monitoring and Analysis
Authors: Xinyu Li | Xinyue Liu | Zhiqiang Kong | Lin Li | Li Long | Marie‐Laure Fauconnier | Minmin Li
Source: Trends in Food Science & Technology (article, 2025-05-27)
Field: Chemistry — Organic Chemistry

Thermal processing of Oilseeds: Multidimensional impacts on Maillard reaction chemistry, nutritional safety, and technological innovations in quality control Maillard reaction ; Quality (philosophy) ; Chemistry ; Biochemical engineering ; Food science ; Control (management) ; Process engineering ; Biotechnology ; Environmental science ; Agricultural engineering ; Engineering ; Computer science ; Biology ; Physics Edible Oils Quality and Analysis ; Advanced Glycation End Products research ; Potato Plant Research
Authors: Baozhuang Sun | Xiayan Wang | Yiran Li | Xiaokai Huang | Yue Pan | Long Yan | Yingxiao Zhang | Mingxian Sun | Lin Lü | Zhiyong Liu | Lin Fan | Xiaogang Li
Source: Corrosion Science (article, 2025-11-16)
Field: Materials Science — Metals and Alloys

Investigating the environmental factors on corrosion and stress corrosion cracking behavior of Ti-6Al-3Nb-2Zr-1Mo alloy in simulated seawater Corrosion ; Microstructure ; Stress corrosion cracking ; Alloy ; Welding ; Base metal ; Artificial seawater ; Seawater ; Cracking Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Tanaporn Na Narong | Zoe N. Zachko | Steven B. Torrisi | Simon J. L. Billinge
Source: npj Computational Materials (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Interpretable multimodal machine learning analysis of X-ray absorption near-edge spectra and pair distribution functions We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical environments of transition metal cations in oxides. Random forest models were trained on simulated XANES, PDF, and both combined to extract oxidation state, coordination number, and mean nearest-neighbor bond length. XANES-only models generally outperformed PDF-only models, even for structural tasks, although using the metal’s differential-PDFs (dPDFs) instead of total-PDFs narrowed this gap. When combined with PDFs, information from XANES often dominates the prediction. Our results demonstrate that XANES contains rich structural information and highlight the utility of species-specificity. This interpretable, multimodal approach is quick to implement with suitable databases and offers valuable insights into the relative strengths of different modalities, guiding researchers in experiment design and identifying when combining complementary techniques adds meaningful information to a scientific investigation. Distribution (mathematics) ; Spectral line ; Enhanced Data Rates for GSM Evolution ; Absorption (acoustics) ; X-ray ; Computer science ; Artificial intelligence ; Physics ; Optics ; Computational physics ; Biological system ; Mathematics ; Mathematical analysis ; Astronomy ; Biology Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography
Authors: Shashi Kant Bhatia | Anil Kumar Patel | Ganesh Dattatraya Saratale | Vinod Kumar | Yung-Hun Yang
Source: International Journal of Biological Macromolecules (review, 2025-08-07)
Field: Materials Science — Biomaterials

A review on upcycling waste cooking oil into polyhydroxyalkanoates (bioplastic): A pathway for sustainable material Bioplastic ; Polyhydroxyalkanoates ; Cupriavidus necator ; Raw material ; Pulp and paper industry ; Biomass (ecology) ; Food science ; Fermentation ; Biodegradable plastic ; Chemistry ; Biotechnology ; Biochemical engineering ; Waste management ; Organic chemistry ; Bacteria ; Biology ; Engineering biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Biopolymer Synthesis and Applications
Authors: S. Girish | M. Vinay | D. Saravana Bavan | Y. P. Ravitej | Dandapani
Source: Journal of The Institution of Engineers (India) Series D (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Thermal and Mechanical Properties Enhancement of Epoxy Composites Using Graphene, MWCNTs, and Biochar: A Machine Learning-Assisted Approach Thermal conductivity ; Graphene ; Filler (materials) ; Carbon nanotube ; Epoxy ; Thermal ; Vickers hardness test Carbon Nanotubes in Composites ; Polymer Nanocomposite Synthesis and Irradiation ; Fiber-reinforced polymer composites
Authors: Yuefeng Lei | Xiangyang Liu | Yaochen Yu | Haiyang Niu
Source: The Innovation (article, 2025-03-13)
Field: Materials Science — Materials Chemistry

Deep potential-driven structure exploration of ice polymorphs Ice, a ubiquitous substance in nature, exhibits diverse forms under varying temperature and pressure conditions. However, our understanding of ice polymorphs remains incomplete. The directional nature of hydrogen bonding and the complexity of the networks they form pose significant challenges to computational studies of ice structures. In this work, we present an extensive exploration of ice polymorphs under pressure conditions ranging from 1 bar to 10 GPa. We employ an advanced crystal-structure-prediction scheme that integrates an evolutionary algorithm, an active-learning deep neural network potential, and molecular dynamics simulations with <i>ab initio</i> accuracy. Among the 131,481 predicted structures, we successfully identify all experimentally known ice phases within the target pressure range, including the particularly challenging ice IV and V. These phases feature highly intricate H-bond networks, which have hindered previous efforts to fully explore ice structures. Additionally, we identify 34 new ice polymorphs that are potential candidates for experimental discovery. Notably, we predict the existence of a new stable ice phase, ice L, within the temperature range of 253-291 K and pressure range of 0.38-0.57 GPa, exhibiting a unique topology unseen in any known crystals. Our findings highlight the potential for experimental discovery of new ice phases. Furthermore, our approach can be applied to other complex systems, particularly those with network structures. Geology Machine Learning in Materials Science ; Methane Hydrates and Related Phenomena ; Hydrocarbon exploration and reservoir analysis
Authors: Mustapha Iddrisu | Ahmad Abulfathi Umar | Mohammad M. Hossain
Source: Arabian Journal for Science and Engineering (review, 2025-04-07)
Field: Materials Science — Materials Chemistry

Applications of Porous Organic Polymers and Frameworks for CO2 Capture: A Comprehensive Review Porosity ; Polymer ; Materials science ; Porous medium ; Nanotechnology ; Chemical engineering ; Engineering ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Nikhil K. Barua | Sangjoon Lee | Anton O. Oliynyk | Holger Kleinke
Source: Journal of Physics Energy (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery Abstract Machine learning models as part of artificial intelligence have enjoyed a recent surge in answering a long-standing challenge in thermoelectric materials research. That challenge is to produce stable, and highly efficient, thermoelectric materials for their application in thermoelectric devices for commercial use. The enhancements in these models offer the potential to identify the best solutions for these challenges and accelerate thermoelectric research through the reduction in experimental and computational costs. This perspective underscores and examines recent advancements and approaches from the materials community in artificial intelligence to address the challenges in the thermoelectric area. Besides, it explores the possibility for these advancements to surpass existing limitations. Additionally, it presents insights into the material features influencing model decisions for thermoelectric property predictions and in some cases new thermoelectric material discovery. In the end, the perspective addresses current challenges and future potential studies beyond classical ML studies for thermoelectric research. Artificial intelligence ; Computer science ; Materials science ; Machine learning ; Data science Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Yahui Huang | Ruizhi Zhang | Shanpeng Liu | Jian Peng | Yong Liu | Han Chen | Jian Zhang | Guoqiang Luo | Qiang Shen
Source: Defence Technology (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Graded density impactor design via machine learning and numerical simulation: Achieve controllable stress and strain rate The graded density impactor (GDI) dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons. The accuracy and timeliness of GDI structural design are key to achieving controllable stress-strain rate loading. In this study, we have, for the first time, combined one-dimensional fluid computational software with machine learning methods. We first elucidated the mechanisms by which GDI structures control stress and strain rates. Subsequently, we constructed a machine learning model to create a structure-property response surface. The results show that altering the loading velocity and interlayer thickness has a pronounced regulatory effect on stress and strain rates. In contrast, the impedance distribution index and target thickness have less significant effects on stress regulation, although there is a matching relationship between target thickness and interlayer thickness. Compared with traditional design methods, the machine learning approach offers a 10 4 –10 5 times increase in efficiency and the potential to achieve a global optimum, holding promise for guiding the design of GDI. • Numerical simulation revealed Al-Cu GDI stress-strain rate mechanisms under varying loads & structural parameters. • ML-based model predicted GDI structure-property relationships with 10 4 –10 5 efficiency gains over conventional methods. • Response surface models achieved GDI global optimization enabling precision stress/strain control for defense-grade extreme-condition performance. Materials science ; Stress (linguistics) ; Stress–strain curve ; Strain (injury) ; Computer simulation ; Strain rate ; Mechanical engineering ; Computer science ; Composite material ; Mechanics ; Simulation ; Engineering ; Deformation (meteorology) ; Physics High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Machine Learning in Materials Science
Authors: Ke Xu | Letao Yang | Jing Wang | Houbing Huang
Source: Journal of Materials Informatics (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Design of high energy storage ferroelectric materials by phase-field simulations The improvement in energy storage performance of ferroelectric (FE) materials requires both high electric breakdown strength and significant polarization change. The phase-field method can couple the multi-physics-field factors. It can realize the simulation of electric breakdown and polarization evolution. It is widely used to reveal the modification mechanism and guide experimental design. Starting with the models of electric breakdown and polarization evolution, this work reviews the latest theoretical progress on FE materials with high energy storage performance. Firstly, the enhancement mechanisms of electric breakdown strength are analyzed. Subsequently, the improvement strategies at domain scales are analyzed. Finally, this review summarizes and looks ahead to the development of theoretical models, such as machine learning models. Ferroelectricity ; Energy storage ; Materials science ; Phase (matter) ; Field (mathematics) ; Engineering physics ; Optoelectronics ; Physics ; Thermodynamics ; Dielectric ; Mathematics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Multiferroics and related materials