jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guangbao Mi | Yuanzhi Sun | Ruochen Sun | Peijie Li | Nan Sui | Liangju He | Fuli Dong
Source: Materials & Design (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Machine learning assisted optimization design and microstructure-property evolution mechanisms of 600 °C high temperature burn resistant titanium alloys • A machine learning and genetic algorithm integrated approach was employed to design a novel titanium alloy with superior oxidation resistance, demonstrating a 42% reduction in the parabolic oxidation rate constant compared to the conventional burn resistant Ti-alloys. • The designed alloy exhibits outstanding tensile strength at both room temperature and 600 °C, attributed to a dual-phase synergistic strengthening mechanism. • The alloy shows significantly enhanced burn-resistant performance compared to conventional titanium alloys, with superior anti-ignition capability at temperatures above 600 °C. • A universal alloy design framework was established, which is applicable to other titanium-based systems and validated by close agreement between simulation and experimental results. This study establishes a machine learning assisted optimization design framework for 600 °C high temperature burn resistant titanium alloys, integrating domain expertise and experimental data analysis. An high-precision predictive machine learning model(XGBoost) for oxidation resistance (R 2 = 0.98) combined with genetic algorithm performed global compositional optimization in the Ti-V-Cr-Al-Si-C-Mo-Nb system, minimizing the parabolic oxidation rate (kp) to 1.02 × 10 −6 mg 2 /cm 4 ·s. A representative composition of a burn resistant titanium alloy (TF600) capable of withstanding 600 °C was ultimately identified. This alloy exhibits well-balanced properties with a kp value as low as 1.02 × 10 −6 mg 2 /cm 4 ·s. After optimization of the solution and aging treatment process, the room temperature and 600 °C tensile strengths of TF600 reached 1177 MPa and 970 MPa respectively. Through microstructural characterization of the TF600 alloy and cellular automata simulations, the strengthening mechanism was elucidated as being dominated by the pinning effect of 50–150 nm lath α phase interfaces and dispersion strengthening by 20–80 nm Cr/V-rich Laves phases. The burn resistant mechanism was attributed primarily to the barrier effect of a mixed oxide film composed of V 2 O 5 and Cr 2 O 3 . This research provides a novel pathway for the machine learning assisted optimization and precise design of advanced high-temperature titanium alloys. Titanium alloy ; Alloy ; Ultimate tensile strength ; Titanium ; Burn-in ; Cellular automaton ; Reduction (mathematics) Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Zhenglu Yang | Biao Yuan | Yanling Liu | Pan Wu | Changjun Liu | Wei Jiang
Source: ACS Agricultural Science & Technology (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Synthesis of Urea-Formaldehyde Fertilizers for Precision Agriculture: Tailoring Nitrogen Release via ANN-GA Optimization Urea-formaldehyde (UF), a prominent slow-release nitrogen fertilizer, faces challenges in production optimization to efficiently meet the varying slow-release needs of different crops. This study employed response surface methodology (RSM) analysis combined with an artificial neural network-genetic algorithm (ANN-GA) prediction to refine the UF polymerization process. Key factors influencing the polymerization process and the slow-release properties of UF products were identified as the urea/formaldehyde molar ratio (U/F) and reaction pH. The ANN-GA model demonstrated superior prediction accuracy over the RSM model, achieving coefficient of determination (R2) values of 0.9968 for cold water-insoluble substances (CWI) and 0.9979 for hot water-insoluble substances (HWI), representing improvements of 0.6% and 0.43%, respectively. By utilizing a fitness function that incorporated the UF activity index as the objective, the model optimized process parameter combinations, yielding relative errors below 4% between predicted and experimental values. The ANN-GA model facilitated precise control over UF polymerization, enabling the synthesis of short-cycle slow-release UF derived from methylenediurea (MDU) for rapid nutrient delivery and long-cycle UF based on trimethylenetraurea (TMTU) for sustained nutrient release. This study introduces a novel framework for regulating fertilizer manufacturing processes in precision agriculture, employing a “demand-driven → algorithmic optimization → targeted synthesis” approach that provides quick and adaptive solutions. Urea ; Urea-formaldehyde ; Nitrogen ; Formaldehyde ; Agriculture ; Computer science ; Materials science ; Process engineering ; Environmental science ; Biochemical engineering ; Chemical engineering ; Chemistry ; Engineering ; Nanotechnology ; Organic chemistry ; Biology Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Gaobo Lin | Haoan Fan | Jie Zhu | Xuezhi Zhao | Shan Qin | Ying Ren | Zhenyu Zhang | Bolong Li | Jianghao Wang | Huiping Ji | Weiyu Song | Jie Fu
Source: Small (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Screening Advanced Single‐Atom Anchored MXenes Electrocatalyst for Hydrogen Evolution Reaction MXene-supported single-atom catalyst (MXene-SACs) systems are considered promising potential electrocatalysts for the hydrogen evolution reaction (HER) due to their excellent conductivity, stability, and hydrophilicity. However, the complex composition of MXene-SACs and the unclear structure-activity relationship limit the rational design of efficient HER catalysts. To address this challenge, a high-throughput screening strategy that integrates theoretical calculations, machine learning (ML), and experimental validation to efficiently identify MXene-SACs with outstanding HER performance is developed. Using a database constructed from theoretical calculations as input, a ML model to screen a batch of potential high-efficiency HER catalysts is built. Based on the distribution pattern of hydrogen evolution barriers predicted by ML and electronic structure analysis, a novel structural descriptor Φ, which can be easily calculated using corresponding properties from the periodic table is derived. The descriptor provides insights into the underlying HER mechanism of MXene-SACs, where electron transfer from surrounding coordinating atoms to the single atom effectively shifts the d-band center to an optimal level (≈ -2.7 eV), minimizing the hydrogen evolution barrier. Guided by this descriptor, Cr<sub>2</sub>CO<sub>2</sub>-Pt is synthesized, which exhibits outstanding HER performance, achieving a current density of 1 A cm<sup>-2</sup> at an overpotential of 150.7 mV and maintaining long-term stability over 130 h. Overpotential ; MXenes ; Electrocatalyst ; Stability (learning theory) ; Catalysis ; Hydrogen ; Water splitting ; Mechanism (biology) MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Xin Huang | Petr Plecháč | Mattias Sandberg | Anders Szepessy
Source: BIT Numerical Mathematics (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Convergence rates for random feature neural network approximation in molecular dynamics Abstract Random feature neural network approximations of the potential in Hamiltonian systems yield approximations of molecular dynamics correlation observables that have the expected error $$\mathcal {O}\left( \left( K^{-1}+J^{-\frac{1}{2}}\right) ^{\frac{1}{2}}\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mfenced> <mml:msup> <mml:mfenced> <mml:msup> <mml:mi>K</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>J</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:mrow> </mml:msup> </mml:mfenced> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:msup> </mml:mfenced> </mml:mrow> </mml:math> , for networks with K nodes using J data points, provided the Hessians of the potential and the observables are bounded. The loss function is based on the least squares error of the potential and regularizations, with the data points sampled from the Gibbs density. The proof uses a new derivation of the generalization error for random feature networks that does not apply the Rademacher or related complexities. Convergence (economics) ; Artificial neural network ; Feature (linguistics) ; Dynamics (music) ; Mathematics ; Applied mathematics ; Statistical physics ; Computer science ; Artificial intelligence ; Algorithm ; Physics ; Economics Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: Minqiang Guo | Hong Lin | Feng Hua | Limin Cao | Jianxin Sui | Xiudan Wang | Kaiqiang Wang
Source: Food Chemistry (article, 2025-07-21)
Field: Chemistry — Analytical Chemistry

Deep learning algorithm-assisted non-destructive detection of TBARS values of salmon flesh using multi-modal molecular spectra fusion Flesh ; Modal ; Artificial intelligence ; Algorithm ; Pattern recognition (psychology) ; Fusion ; Computer science ; Chemistry ; Biological system ; Biology ; Food science ; Philosophy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ingo Dierking | Adam Moyle | Gabriele Maria Cepparulo | Katherine Skingle | Laura B. Hernández | Juhan Raidal
Source: Crystals (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles Machine learning-based image recognition is employed to investigate the annihilation dynamics of umbilic defects induced in systems of nematic liquid crystals doped with nanoparticles. A machine learning methodology based on a YOLO algorithm is trained and optimized to identify defects of strength s = ±1 and determine their trajectories during the annihilation process of umbilics of opposite sign. Universal scaling laws describing the distance between two defects as a function of time to annihilation are determined, and average scaling exponents α are calculated for an ensemble of events. It is observed that the defect annihilation scaling exponents deviate from the theoretically predicted value of α = 1/2 when nanoparticles of varying size and concentration are introduced to the system. Scaling laws of the form D~tα do not yield the typical square-root law normally observed, but the experiments suggest a decrease in the exponent to saturation values of approximately α = 0.38 ± 0.01 as the size, particle concentration, and mass concentration of the nanoparticles is increased. Interestingly, the defect density itself is not affected, which implies that the nanoparticles do not act as defect formation sites. Liquid crystal ; Annihilation ; Materials science ; Nanoparticle ; Chemical physics ; Condensed matter physics ; Crystallography ; Nanotechnology ; Physics ; Chemistry ; Optoelectronics ; Nuclear physics Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Theoretical and Computational Physics
Authors: Muhammad Shalahuddin Al Ja’farawy | Vo Thi Nhat Linh | ChaeWon Mun | Jun-Young Yang | Jun Young Kim | Rowoon Park | Sung‐Gyu Park | Dongho Kim | Min‐Young Lee | Ho Sang Jung
Source: Advanced Science (article, 2025-05-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic Molecular Entrapment for Label‐Free Methylated DNA Detection and Machine‐Learning Assisted Quantification Epigenetic DNA methylations are linked to the activation of oncogenes and inactivation of tumor suppressor genes. A reliable and label-free method to quantitatively measure DNA methylation levels is essential for diagnosing and monitoring methylation-related diseases. Herein, plasmonic molecular entrapment (PME) method assisted SERS as facile strategy for trapping and label-free sensing of DNA methylation, utilizing in situ surface growth of plasmonic particle in the presence of target analytes, are developed. This highly sensitive and adaptable technique forms hotspot sites around target analytes, overcoming mismatch geometrical properties and producing a strong electromagnetic field that leads to significant SERS signal enhancement. The PME method effectively profiles and quantifies DNA methylation, demonstrating robust capabilities for DNA analysis. A logistic regression (LR)-based machine learning accurately quantifies and classifies methylation levels in clinical serum samples of colorectal cancer and normal patients with high sensitivity, specificity, and accuracy, highlighting the feasibility of this technique. The developed PME method combined with machine learning offers promising sensing techniques for disease screening and diagnosis, marking a significant advancement in disease detection and patient care. Plasmon ; Entrapment ; Nanotechnology ; Computer science ; Chemistry ; Artificial intelligence ; Materials science ; Optoelectronics ; Medicine Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Yongqian Peng | Zhouran Zhang | Kai Hu | Yicong Ye | Shuxin Bai
Source: Materials Today Communications (article, 2025-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Morphology prediction and controlled synthesis of silver nanomaterials based on ensemble machine learning models Materials science ; Nanomaterials ; Nanotechnology ; Machine learning ; Artificial intelligence ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Shenglong Teng | Yiwen Song | Yu Qiu | Xinyu Li | Y.-W. Peter Hong | Jian Zuo | Dewang Zeng | Kai Xu
Source: Sustainable Energy & Fuels (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-throughput screening of high-activity oxygen carriers for chemical looping argon purification <i>via</i> a machine learning – density functional theory method A new method based on machine learning and DFT calculations for screening oxygen carriers during chemical looping argon purification is proposed in this study. Oxygen ; Density functional theory ; Throughput ; Argon ; Chemical looping combustion ; High-throughput screening ; Chemistry ; Materials science ; Computer science ; Computational chemistry ; Biochemistry ; Telecommunications ; Organic chemistry Catalytic Processes in Materials Science ; Chemical Looping and Thermochemical Processes ; Advancements in Solid Oxide Fuel Cells
Authors: Mona Amrihesari | Manali Banerjee | Raul Olmedo | Blair Brettmann
Source: Macromolecular Rapid Communications (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Role of High Fidelity Vs. Low Fidelity Experimental Data in Machine Learning Model Performance for Predicting Polymer Solubility Reliable classification of polymer-solvent compatibility is essential for solution formulation and materials discovery. Applying machine learning (ML) and artificial intelligence to this task is of growing interest in polymer science, but the effectiveness of such models depends on the quality/nature of the training data. This study evaluates how experimental data fidelity, as set by the experimental method, influences ML model performance by comparing classifiers trained on two experimental datasets: one generated from turbidity-based measurements using a Crystal16 parallel crystallizer as a high-fidelity source and another derived from visual solubility inspection as a low-fidelity dataset. Both datasets were encoded using one-hot encoding for polymers and Morgan fingerprints for solvents and modeled using XGBoost classifiers to predict solubility labels as soluble, insoluble, and partially soluble. Confusion matrices showed that models trained on high-fidelity data better captured partially soluble behavior and more clearly distinguished between classes, highlighting the advantage of quantitative measurements over subjective classification. We also found that adding temperature as a feature improved prediction accuracy for the low-fidelity dataset-a key consideration for literature-derived data, which often lacks this information. These findings underscore the importance of experimental rigor and completeness when developing generalizable ML-based tools for polymer solubility prediction. Fidelity ; Computer science ; Machine learning ; Solubility ; Artificial intelligence ; Confusion ; Experimental data ; Polymer ; Data mining ; Materials science ; Chemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Calcium Carbonate Crystallization and Inhibition
Authors: Akash Gutal | Manikandan Paranjothy
Source: The Journal of Chemical Physics (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Quasi-classical trajectory study of the CN− + CH3I reaction on a high-dimensional neural network PES The gas-phase dynamics of bimolecular nucleophilic substitution (SN2) reactions have been extensively studied by both experimental and theoretical groups due to their broad applicability and the emergence of new mechanistic insights. The reaction between CN- and CH3I is particularly intriguing, as it can yield two isomeric products (NCCH3 or CNCH3 + I-) owing to the ambident nature of the CN- nucleophile. Previous velocity map imaging experiments revealed predominantly direct rebound dynamics and high internal energy excitation in the reaction products. In another study, direct dynamics simulations were performed employing the PM7 semi-empirical method and B3LYP/aug-cc-pVDZ/ECP density functional theory (DFT); however, these approaches were limited by an insufficient number of DFT trajectories (due to high computational costs) and an overestimation of hydrogen transfer reactions with PM7. In the present study, a high-dimensional neural network-based potential energy surface (PES) was developed using extensive electronic structure data. The PES was rigorously validated using established benchmarks and subsequently employed in quasi-classical trajectory simulations. A substantial number of reactive trajectories were generated, enabling a detailed analysis of the reaction dynamics. The simulation results are consistent with previous findings and provide new insights into the mechanistic pathways of this SN2 reaction. SN2 reaction ; Trajectory ; Potential energy surface ; Yield (engineering) ; Density functional theory ; Molecular dynamics ; Reaction dynamics ; Artificial neural network ; Potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Yitao Si | Yiding Ma | Tao Yu | Yucheng Wu | Yingzhe Liu | Weipeng Lai | Zhixiang Zhang | Jinwen Shi | Liejin Guo | Oleg V. Prezhdo | Maochang Liu
Source: npj Computational Materials (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Transition state structure detection with machine learningś Transition structure calculations via quantum chemistry methods have become a staple in modern chemical reaction research. Yet, success rates in optimizing transition structures rely heavily on rational initial guesses and expert supervision. We develop a machine learning approach that utilizes a bitmap representation of chemical structures to generate high-quality initial guesses for modeling transition states of chemical reactions. The core of the approach comprises a convolutional neural network methodology with a genetic algorithm. An extensive dataset derived from quantum chemistry computations is built, providing sufficient data on which the model can be trained, validated and tested. By applying the method to typical bi-molecular hydrogen abstraction reactions involving hydrofluorocarbons, hydrofluoroethers, and hydroxyl radicals—reactions critical in atmospheric fluoride degradation and global warming potential evaluation, yet extremely challenging to model, we achieve transition state optimizations with an impressive, verified success rate of 81.8% for hydrofluorocarbons and 80.9% for hydrofluoroethers. The reported work demonstrates the effectiveness of employing visual representation in chemical space exploration tasks and opens new avenues for the transition structure modeling. Transition (genetics) ; State (computer science) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Programming language Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Abrar U. Hassan | Mamduh J. Aljaafreh
Source: Optical Materials (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

A machine learning-quantum chemical quest to predict the photovoltaic properties of 2D molybdenum sulfide super structures Molybdenum ; Sulfide ; Quantum chemical ; Photovoltaic system ; Materials science ; Engineering physics ; Chemistry ; Physics ; Metallurgy ; Engineering ; Organic chemistry ; Molecule ; Electrical engineering Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: S. Sridharan | Ramamoorthy Velayutham | S. N. Behera | Jayaprakash Murugesan
Source: Journal of Materials Engineering and Performance (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Temperature-Induced Phase Transformation Temperature Prediction of Ti-Based High-Temperature Shape Memory Alloy Materials science ; Shape-memory alloy ; Alloy ; Transformation (genetics) ; Phase (matter) ; Metallurgy ; Composite material Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience ; Titanium Alloys Microstructure and Properties
Authors: Longjian Li | Gaojie Liu | Hongrui Yu | Zeyuan Chen | Huijun Kang | ZongNing CHEN | Enyu Guo | Jinchuan JIE | TongMin WANG
Source: Journal of Material Science and Technology (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

A machine learning strategy to achieve strength-conductivity-ductility synergy of high‐performance Cu-Ni-Co-Si alloys via rolling and aging process Alloy ; Microstructure ; Texture (cosmology) ; Process (computing) ; Ultimate tensile strength ; Feature (linguistics) ; Brass ; Precipitation Microstructure and mechanical properties ; Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties