jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rukiye Tekİn Unver | Cihan Bayraktar | Bilge Demir
Source: Applied Physics A (article, 2025-03-24)
Field: Materials Science — Biomaterials

The regression analysis of dry - wet wear outcomes and materials properties of biodegradable MgCu and MgZn, made by P/M, using machine learning models Abstract Incorporating Cu and Zn into Mg as a biomaterial offers a unique opportunity to exploit their antibacterial performance and biodegradability. The main challenge in this area is understanding the ratio and effects of these elements. To achieve this, the present work, based on two separate studies, aims to develop a regression model and apply machine learning (ML) to predict the wear behaviors using the effects of Cu and Zn elements doped into Mg matrix at low ratios on wear and micro and nanostructure properties (Grain size, density, hardness, Crystallite Size, microstrain, dislocation density). The wear behavior of the samples was investigated under 5–20 N loads at a constant sliding speed of 42 mm/s. Auto Sklearn library was used to generate training models that accurately predict the wear loss, friction coefficient, and specific wear rate values. The model showed satisfactory explanatory power and reliability in predicting the volume loss target. It also exhibited remarkable capability in predicting the friction coefficient and specific wear rate targets. The results of sample wear tests (MgZn2 under 15 N) conducted to generate data not included in the dataset showed a high degree of agreement with the ML results. Sensitivity analyses confirmed that Load, Environment, Hardness, and Grain Size are the most influential factors in predicting wear behavior, further validating the model’s reliability and interpretability. Regression analysis ; Regression ; Materials science ; Statistics ; Mathematics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Orthopaedic implants and arthroplasty
Authors: Jonas Hänseroth | Christian Dreßler
Source: The Journal of Chemical Physics (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Optimizing machine learning interatomic potentials for hydroxide transport: Surprising efficiency of single-concentration training We investigate the transferability of machine learning interatomic potentials across concentration variations in chemically similar systems, using aqueous potassium hydroxide solutions as a case study. Despite containing identical chemical species (K+, OH-, and H2O) across all concentrations, models fine-tuned on specific KOH concentrations exhibit surprisingly poor transferability to others, with force prediction errors increasing dramatically from 30 meV Å-1 (at training concentration) to 90 meV Å-1 (at very different concentrations). This reveals a critical limitation when applying such models beyond their training domain, even within chemically homogeneous systems. We demonstrate that strategic selection of training data can substantially overcome these limitations without requiring extensive computational resources. Models fine-tuned on intermediate concentrations (6.26 mol l-1) exhibit remarkable transferability across the entire concentration spectrum (0.56-17.89 mol l-1), often outperforming more computationally expensive models trained on multiple concentration datasets. This approach enables accurate simulation of hydroxide transport dynamics across varying electrolyte conditions while maintaining near-quantum accuracy. Our simulations further reveal the emergence of hydroxide-hydroxide hydrogen bonding at high concentrations-a phenomenon not explicitly represented in dilute training data but successfully captured by our intermediate-concentration model. This work establishes practical guidelines for developing broadly applicable machine learning force fields with optimal transferability, challenging the assumption that diverse training datasets are always necessary for robust performance in similar chemical environments. Training (meteorology) ; Computer science ; Chemistry ; Materials science ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Spectroscopy and Quantum Chemical Studies
Authors: Md Rajib Khan Musa | Yichen Qian | Jie Peng | David Cereceda
Source: Scripta Materialia (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications. Monte Carlo method ; Materials science ; Statistical physics ; Sampling (signal processing) ; Energy (signal processing) ; Computational science ; Computer science ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; High-pressure geophysics and materials
Authors: Jing Wang | Yusheng Jia | Rui Li | Yupeng Zhang | Caiping Ma | Yang Zhang | Riguang Zhang | Xiaofeng Li | Baojun Wang | Lixia Ling
Source: ACS Catalysis (article, 2025-05-30)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Accelerated Prediction of Lewis Acid Site Positioning for Long-Chain Mono-olefin Aromatization on Zn<sup>2+</sup>/HZSM-5 Catalysts Long-chain mono-olefin aromatization is an important reaction during synthesis of coal to aromatics, in which the positioning of Lewis acid in ZSM-5 zeolite determines the performance. This study aims to reveal the relationship between the location of Lewis acid and catalytic performance during the aromatization of C6–C8 mono-olefins and to rapid-screen the optimized positioning of Lewis acid to enhance the efficiency and selectivity of aromatic production. Based on density functional theory (DFT) to obtain high-quality data sets of C6 and C7 dehydrogenation reactions in Zn2+/HZSM-5 catalysts, a data-driven ML model is developed to predict the C8 dehydrogenation energy barriers and screen the Lewis acid sites T5-T3 or T11-T3 with high dehydrogenation activity, while DFT confirms the accuracy of the results. The BTX (benzene, toluene, and xylene) selectivity reached 64.1% among various products via microkinetic analysis after investigating the cyclization process and cracking reactions on the Lewis acid site positioning, where the dehydrogenation activity of the catalyst is optimal. The six-membered ring is found to be the key cyclic intermediate due to the pore confinement effect, which effectively stabilizes cyclic intermediates, and toluene and p-xylene show a small steric effect through weak interactions analysis. This can provide important clues and research paradigms for the rational design of ZSM-5 zeolite modified with different metal species. Aromatization ; Catalysis ; Olefin fiber ; Chemistry ; Lewis acids and bases ; Organic chemistry Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Heena S. Mulla | Digambar S. Sawant | Satish V. Gaikwad | Akash V. Fulari | R.K. Nimat | Deepak P. Dubal | G. M. Lohar
Source: ChemSusChem (review, 2025-05-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning a Predictive Tool for the Analysis of NiCo<sub>2</sub>S<sub>4</sub>/Graphene Composites for Supercapacitor Nickel cobalt sulfide (NiCo<sub>2</sub>S<sub>4</sub>) has considerable potential electrode material for supercapacitors owing to its distinct physical and chemical characteristics. However, the practical applications of pristine NiCo<sub>2</sub>S<sub>4</sub> have been limited by issues such as small specific surface area, agglomeration, and volume changes during cycling, leading to low specific capacitance/capacity and cyclic stability at high rates. Several efforts have been taken to address these challenges. Among those the design and development of NiCo<sub>2</sub>S<sub>4</sub>-graphene-based composites have been widely investigated. This review explores the effect of NiCo<sub>2</sub>S<sub>4</sub> architecture and its nanocomposite with graphene on the electrochemical properties. How the various preparative parameters such as synthesis methods, precursors, experimental conditions contributed to efficiently accelerating charge transport kinetics is outlined. Finally, the effect of introduction of graphene on the electrochemical performance of NiCo<sub>2</sub>S<sub>4</sub> is discussed using density functional theory (DFT). Also, machine learning (ML) models are used to analyze the specific capacitance variation with respect to different synthesis parameters, morphology, energy density, and power density. ML models identify limitations and scope of work for working on NiCo<sub>2</sub>S<sub>4</sub>/graphene composites. It is found that most influencing parameter is annealing time that can alter specific capacitance. The review outlines future research directions, challenges, and opportunities in NiCo<sub>2</sub>S<sub>4</sub>/graphene-based supercapacitor. Supercapacitor ; Graphene ; Composite material ; Materials science ; Nanotechnology ; Electrochemistry ; Chemistry ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Ben Ma | Chen Dang | Jung Hun Song | Zhaohui Chen | Yingke Zhou
Source: Fuel (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Machine learning–accelerated discovery of novel high‑entropy spinel oxide cathodes for solid oxide fuel cells Spinel ; Oxide ; Chemical engineering ; Materials science ; Fuel cells ; Metallurgy ; Engineering Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells
Authors: Lier Deng | Sirui Guo | Ruohan Liu | Tianqing Jiang | Devyani Srivastava | Abhinav Kumar | Jiayi Gao | Jian Qiang Liu
Source: European Journal of Medicinal Chemistry (review, 2025-07-24)
Field: Materials Science — Materials Chemistry

Advances and challenges of Zeolitic imidazolate frameworks (ZIFs) for diagnosis and treatment of kidney diseases and diabetes Zeolitic imidazolate frameworks (ZIFs) exhibit exceptional properties such as high sensitivity, specificity, and biocompatibility, positioning them as promising materials for diagnosing and treating kidney diseases and diabetes. In kidney disease management, ZIFs are utilized to develop biosensors for precise biomarker detection, serve as targeted drug delivery systems, and facilitate the removal of uremic toxins. For diabetes, ZIF-based sensors enable non-invasive monitoring of glucose, acetone, and insulin, while ZIF carriers enable controlled insulin release and promote diabetic wound healing through antibacterial and angiogenic effects. To further enhance ZIF functionality for these biomedical applications, artificial intelligence (AI) is increasingly integrated to optimize ZIF design, predicting parameters like ZIF-8 morphology via machine learning algorithms (e.g., neural networks, random forest) to guide synthesis. Current challenges include improving the biocompatibility and targeted delivery of ZIFs, alongside enhancing AI model interpretability and expanding datasets for robust predictions. Future advancements should prioritize AI-driven engineering of ZIFs to refine diagnostic precision and therapeutic efficacy in renal and diabetic care. Zeolitic imidazolate framework ; Chemistry ; Diabetes mellitus ; Imidazolate ; Nanotechnology ; Kidney disease ; Pharmacology ; Metal-organic framework ; Intensive care medicine ; Internal medicine ; Endocrinology ; Organic chemistry ; Medicine ; Adsorption Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Sulfur Compounds in Biology
Authors: S. Ali | Didier Talamona | Asma Perveen
Source: Scientific Reports (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Machine learning based prediction modeling of micro-EDM of Ti–29Nb–13Ta–4.6Zr (TNTZ) Although Ti-6Al-4V stands out as one of the best in biomedical, automotive, and aerospace applications due to its low density and higher corrosion resistance, the toxicity associated with Al and V elements is shifting the usage of Ti-based alloys with reduced toxic content but with more biocompatible elements (Zr, Ta, and Nb). Despite the advancements in the era of micromachining, machining these alloys with traditional machining methods is highly tedious. The present research evaluates the micromachining performance of the TNTZ (Ti-29Nb-13Ta-4.6Zr) alloy employing a tungsten carbide electrode using the µ-EDM (micro-electro-discharge-machining). The primary input parameters examined are voltage (80-130 V) and capacitance (10-400nF), with a feed rate of 0.09 mm/s during the experiments. The output responses assessed include VMR, OC, CErr, and SFR. Meanwhile, due to the complexity of the µ-EDM process, it presents significant challenges in predicting performance across different machining settings. The interactions between key process parameters, such as C and V, amplify their parametric sensitivity, making conventional simulation approaches inadequate for accurately modeling these interdependencies. To address these challenges, the latter part of this study explores machine learning techniques, particularly Multiple linear regressor (MLR), decision tree regressor (DTR), and artificial neural network (ANN) for predictive accuracy. The models are evaluated using two key performance metrics: normalized root mean squared error (NRMSE) and R-squared (R<sup>2</sup>). The ANN demonstrated superior capability in handling experimental variability based on the prediction results. It has the highest R<sup>2</sup> of 0.99, the lowest NRMSE of 0.0245, and the percentage of prediction error is less than 5%. Computer science ; Artificial intelligence ; Materials science Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Advanced Machining and Optimization Techniques
Authors: Tianyi Li | Zhengyuan Chen | Zhen Zhang | Zhenhua Wei | Gan‐Ji Zhong | Zhong‐Ming Li | Han Liu
Source: Polymers (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Predicting Stress–Strain Curve with Confidence: Balance Between Data Minimization and Uncertainty Quantification by a Dual Bayesian Model Driven by polymer processing-property data, machine learning (ML) presents an efficient paradigm in predicting the stress-strain curve. However, it is generally challenged by (i) the deficiency of training data, (ii) the one-to-many issue of processing-property relationship (i.e., aleatoric uncertainty), and (iii) the unawareness of model uncertainty (i.e., epistemic uncertainty). Here, leveraging a Bayesian neural network (BNN) and a recently proposed dual-architected model for curve prediction, we introduce a dual Bayesian model that enables accurate prediction of the stress-strain curve while distinguishing between aleatoric and epistemic uncertainty at each processing condition. The model is trained using a Taguchi array dataset that minimizes the data size while maximizing the representativeness of 27 samples in a 4D processing parameter space, significantly reducing data requirements. By incorporating hidden layers and output-distribution layers, the model quantifies both aleatoric and epistemic uncertainty, aligning with experimental data fluctuations, and provides a 95% confidence interval for stress-strain predictions at each processing condition. Overall, this study establishes an uncertainty-aware framework for curve property prediction with reliable, modest uncertainty at a small data size, thus balancing data minimization and uncertainty quantification. Bayesian probability ; Confidence interval ; Balance (ability) ; Strain (injury) ; Dual (grammatical number) ; Uncertainty quantification ; Stress (linguistics) ; Statistics ; Econometrics ; Mathematics ; Materials science ; Internal medicine ; Medicine Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Zhi Huang | Jiang Yu | Wei He | Peng Huang | Siwei Deng | Jiang Hong-bin | Jie Yu | Chun Yang | Cheng Han | Yinying Jiang | Weiwei Zhu | Can Liu
Source: Journal of Hazardous Materials (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Radical-Net: A chemistry-enhanced transformer for elementary radical reactions in pollutant chemistry Interpretability ; Radical ; Hazardous waste ; Pollutant ; Elementary reaction ; Generalizability theory Machine Learning in Materials Science ; Radical Photochemical Reactions ; Computational Drug Discovery Methods
Authors: Marco Bertani | Alfonso Pedone
Source: The Journal of Physical Chemistry C (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Atomic Structure of Na<sub>4</sub>P<sub>2</sub>S<sub>7</sub> Glass Solid Electrolyte: Fine-Tuning Machine Learning Potentials for Enhanced Accuracy Electrolyte ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Physical chemistry ; Electrode ; Chromatography Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography
Authors: Xueying Zhou | Changqing Xie | Linglong Ye | Bin Ding
Source: Coordination Chemistry Reviews (article, 2025-11-01)
Field: Chemistry — Inorganic Chemistry

Smart porous-framework sensor arrays: Design principles, AI-driven performance and multiscenario precision detection Sensor fusion ; Key (lock) ; Wireless sensor network ; Field (mathematics) ; Artificial neural network ; Wearable computer ; Signal processing ; Intelligent sensor Metal-Organic Frameworks: Synthesis and Applications ; Advanced Chemical Sensor Technologies ; Gas Sensing Nanomaterials and Sensors
Authors: Mohammadreza Izadifar | Neven Ukrainczyk | Katrin Schönfeld | Eduardus Koenders
Source: Nanoscale Advances (article, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Activation energy of aluminate dissolution in metakaolin: MLFF-accelerated DFT study of vdW and hydration shell effects This research utilizes computational chemistry to investigate the complex mechanisms driving the dissolution of thermally activated metakaolin (MK) clay, a key supplementary cementitious material (SCM) in the manufacturing of concrete and geopolymer-based materials, thereby contributing to a reduced carbon footprint. A thorough exploration of the dissolution process is fundamental for fully understanding its pozzolanic reactivity. Expanding on our recent investigations into SiO<sub>4</sub> <sup>4-</sup> dissolution in MK, this work addresses critical data gaps in understanding the dissolution behavior of aluminate species. The findings complement essential input for microscopic forward dissolution rate computations using the atomistic kinetic Monte Carlo (kMC) upscaling approach. To this end, the study calculates the atomistic activation energy (Δ<i>E</i> <sub>a</sub>) of aluminate species at the transition state for the hydrolysis reaction using machine learning force fields (MLFF) based on density functional theory (DFT) and the improved dimer method (IDM) under far-from-equilibrium conditions, focusing on three activators: NaOH, KOH, and water. The analysis explores both the presence and absence of van der Waals (vdW) interactions, along with varying geometric configurations of hydration shells surrounding cations (Na<sup>+</sup>, K<sup>+</sup>) and the hydroxide anion (OH<sup>-</sup>). The findings indicate that KOH generally exhibits lower Δ<i>E</i> <sub>a</sub> than NaOH, especially when vdW interactions are considered. Moreover, the findings emphasize that reduced hydration shells around KOH and NaOH lead to lower Δ<i>E</i> <sub>a</sub> for the dissolution of aluminate species. Metakaolin ; Dissolution ; Shell (structure) ; Materials science ; Aluminate ; Chemical engineering ; Activation energy ; Chemistry ; Composite material ; Physical chemistry ; Cement Glass properties and applications ; Concrete and Cement Materials Research ; Thermal and Kinetic Analysis
Authors: Somnath S. Kundale | Muhammad Abubakr | Jihye Park | Fernando Ordonez Morales | I Ketut Gary Devara | Windy Ayu Lestari | R. K. Chatterjee | Sang Yong Nam | Jun Hong Park
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Chemically configurable analogue memristors, <i>via</i> the chemiresistive response of oxidized MXene The integration of gas sensing and neuromorphic computing within a single device offers a transformative architecture for artificial olfaction and edge-level intelligent perception. In this study, we demonstrate the feasibility of analogue chemiresistive memristors with an Au/oxidized MXene/Au structure for multifunctional operation. The hydrothermally oxidized MXene, rich in active sites favorable for redox reactions, enables modulation of conductance states <i>via</i> CO<sub>2</sub> and NO<sub>2</sub> gas adsorption on its surface. This dual electrical and molecular tuning yields distinctly separable conductance states, essential for emulating artificial synaptic functions. The device exhibits clear analogue resistive switching and both volatile and non-volatile memory behaviors under electrical and molecular stimuli, indicative of reliable synaptic plasticity. Moreover, gas exposure induces electrical potentiation and depression of conductance states, replicating key features of olfactory synaptic behavior. Its dynamic response to gas pulses, long-term retention, and pulse-dependent plasticity highlight its ability to store and process environmental chemical stimuli in real time. To validate its neuromorphic computing capability, an artificial neural network (ANN) was implemented using the digit-MNIST and fashion-MNIST datasets, achieving recognition accuracies of 95% and 82%, respectively. These results confirm the potential for integrated sensing and computation on a single platform. Memristor ; Materials science ; Nanotechnology ; Electronic engineering ; Engineering MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications