jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junji Zhang | Joshua Pagotto | Tim Gould | Timothy T. Duignan
Source: Machine Learning Science and Technology (article, 2025-01-28)
Field: Chemistry — Physical and Theoretical Chemistry

Scalable and accurate simulation of electrolyte solutions with quantum chemical accuracy Abstract Electrolyte solutions play critical role in a vast range of important applications, yet an accurate and scalable method of predicting their properties without fitting to experiment has remained out of reach, despite over a century of effort. Here, we combine state-of-the-art density functional theory and equivariant neural network potentials to demonstrate this capability, reproducing key structural, thermodynamic, and kinetic properties. We show that neural network potentials can be recursively trained on a subset of their own output to enable coarse-grained/continuum-solvent molecular simulations that can access much longer timescales than possible with all atom simulations. We observe the surprising formation of Li cation dimers along with identical anion-anion pairing of chloride and bromide anions. Finally, we simulate the crystal phase and infinite dilution pairing free energies despite being trained only on moderate concentration solutions. This approach should be scaled to build a greatly expanded database of electrolyte solution properties than currently exists. Quantum chemical ; Electrolyte ; Scalability ; Computer science ; Quantum ; Chemistry ; Physical chemistry ; Physics ; Quantum mechanics ; Organic chemistry ; Molecule ; Electrode ; Database Various Chemistry Research Topics
Authors: Mehrdad Khakbiz | Afshin Esteghza
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-07-18)
Field: Materials Science — Biomaterials

AI-integrated surface topography and experimental investigation of PEO-coated Mg-Zn-Si alloys for next-generation biodegradable medical implants Magnesium alloy, which can naturally degrade and is biologically compatible, is a promising candidate material for biodegradable implants. In this study, the Mg-5.5Zn-1.5Si alloy was fabricated using the cast molding technique. Plasma electrolytic oxidation (PEO) coatings on Mg-Zn-Si alloys were investigated for their ability to improve mechanical properties and corrosion resistance. The PEO-coated sample exhibited a six-fold increase in surface hardness, reaching approximately 480 HV compared to the uncoated alloy's 81 HV. Corrosion tests revealed a significant reduction in corrosion current density, with a decrease from 64.56 µA/cm² in the uncoated alloy to 0.72 µA/cm² in the PEO-coated sample. After 22 days in phosphate-buffered saline (PBS), the PEO-coated alloy exhibited a minimal weight loss of 0.66 %, compared to 7.92 % for the uncoated alloy. Additionally, excellent bioactivity was confirmed by the apatite formation in simulated body fluid (SBF), and mechanical testing showed a tensile strength of 146 MPa for the coated sample after PBS immersion, compared to 122 MPa for the uncoated alloy. Gaussian process regression (GPR) machine learning model were employed to predict surface topography parameters with high accuracy, demonstrating close alignment with experimental SPIP™ software results. The model successfully captured trends in roughness, waviness, and isotropy, validating its potential as a predictive tool for optimizing surface characteristics. This work demonstrates that PEO coatings significantly enhance the corrosion resistance and mechanical properties of magnesium alloys, making them highly promising for next-generation biodegradable medical implants. • Systematically investigated plasma electrolytic oxidation coatings across multiple voltages (150–700 V). • PEO-coated samples showed a ~99% reduction in corrosion current density and only 0.66% weight loss after 22 days in PBS. • Surface hardness increased six-fold and tensile strength improved from 122 MPa to 146 MPa with PEO coating. • Demonstrated early-stage apatite nucleation on PEO surfaces through FTIR, SEM, and XRD analysis after immersion in m-SBF. • Employed a Gaussian Process Regression (GPR) machine learning model to accurately predict surface roughness, waviness. Materials science ; Chemical engineering ; Nanotechnology ; Metallurgy ; Engineering Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Hydrogen Storage and Materials
Authors: Emery Bosten | Kai Chen | Mario Hellings | Deirdre Cabooter
Source: TrAC Trends in Analytical Chemistry (article, 2025-05-26)
Field: Chemistry — Spectroscopy

Artificial intelligence for method development in liquid chromatography Chromatography ; Chemistry ; Computer science Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Chemometric Analyses
Authors: Jie Yang | Peilin Huang | Hanwen Zhang | Hongyang Ren | Jie Zhang | Hong Chen | Xiaotian Wang | Hongkuan Yuan | Jianping Xie | Biao Wang
Source: Applied Surface Science (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

Machine-learning-driven design of TMCs heterojunctions with dual-single-atom synergy for efficient photocatalytic hydrogen evolution Heterojunction ; Photocatalysis ; Semiconductor ; Band gap ; Hydrogen ; Visible spectrum ; Anatase Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Gabriel D. Barbosa | Alberto Striolo
Source: Journal of Chemical & Engineering Data (article, 2025-07-29)
Field: Chemistry — Spectroscopy

Machine Learning Prediction of Critical Micellar Concentration Using Electrostatic and Structural Properties as Descriptors Understanding and predicting surfactants’ critical micelle concentration (CMC) remains a key challenge for the rational design of functional amphiphiles. In this work, we develop a deep learning framework to predict CMCs using quantum chemically derived descriptors, focusing on electrostatic surface potential (ESP) and structural features. We employ a comprehensive temperature-dependent data set comprising over 1300 CMC values across diverse surfactant classes. Fourteen molecular descriptors are extracted via density functional theory (DFT) calculations and used as input, alongside temperature. A fully connected neural network trained on these features yields accurate predictions, achieving performance comparable to previously published graph-based models. To support model interpretability, we explicitly assessed ESP distributions for representative surfactants. SHapley Additive exPlanations (SHAP) and partial dependence analyses reveal that molecular volume, ESP variance, and solvation free energy are the dominant predictors, aligning with established thermodynamic theories. These results demonstrate that DFT-derived electrostatic and geometric descriptors can enable robust and interpretable CMC prediction, offering a physically grounded alternative to black-box models. The methodology and insights presented here may also inform the design of nanostructured soft materials, including surfactant-assisted platforms for hydrogen storage. Chemistry ; Chromatography Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Mass Spectrometry Techniques and Applications
Authors: Nutthaporn Malahom | Nitchakan Darai | Sakda Jampasa | Tuğba Özer | Yuwanda Injongkol | Waroton Paisuwan
Source: Microchemical Journal (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Theoretical science-powered electrochemical and fluorescent systems for clinical diagnosis and environmental monitoring Electrochemistry ; Fluorescence ; Environmental chemistry ; Environmental science ; Environmental monitoring ; Nanotechnology ; Chemistry ; Materials science ; Environmental engineering ; Physics ; Electrode Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Umair Haider | Gul Rahman | Imen Kebaïli | Norah Alomayrah
Source: Computational Materials Science (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Predicting the heat of formation and energy above convex hull of 2D MXenes using machine-learning methods Convex hull ; MXenes ; Hull ; Regular polygon ; Energy (signal processing) ; Materials science ; Computer science ; Composite material ; Mathematics ; Geometry ; Nanotechnology ; Statistics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Nesrine Kherra | Ammar Mosbah | H. Baaziz | Ali Zerriouh | Hussein K. Amusa | Ahmad S. Darwish | Tarek Lemaoui | Fawzi Banat | Inas M. AlNashef
Source: Journal of environmental chemical engineering (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Machine learning and quantum-driven multiscale design of graphene-based 2D adsorbents for enhanced aqueous pesticide removal Graphene ; Adsorption ; Aqueous solution ; Quantum ; Pesticide ; Materials science ; Nanotechnology ; Computer science ; Chemical engineering ; Chemistry ; Physics ; Organic chemistry ; Engineering ; Quantum mechanics Graphene research and applications ; Adsorption and biosorption for pollutant removal ; Graphene and Nanomaterials Applications
Authors: Hyeonbin Moon | Songho Lee | Wabi Demeke | Byungki Ryu | Seunghwa Ryu
Source: npj Computational Materials (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Physics-informed neural operators for generalizable and label-free inference of temperature-dependent thermoelectric properties Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for modeling and design of thermoelectric devices. However, nonlinear temperature dependence and coupled transport behavior make forward simulation and inverse identification challenging under sparse measurements. We present a physics-informed machine learning framework combining physics-informed neural networks (PINN) and neural operators (PINO) for solving forward and inverse problems in thermoelectric systems. PINN enables field reconstruction and property inference by embedding governing equations into the loss function, while PINO generalizes across materials without retraining. Trained on simulated data for 20 p-type materials and tested on 60 unseen materials, PINO accurately infers TEPs using only sparse temperature and voltage data. This framework provides a scalable, data-efficient, and generalizable solution for thermoelectric property identification, facilitating high-throughput screening and inverse design of advanced thermoelectric materials. Thermoelectric effect ; Inference ; Artificial neural network ; Computer science ; Physics ; Artificial intelligence ; Materials science ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Nikita A. Matsokin | Roman A. Eremin | Alena Kuznetsova | Innokentiy S. Humonen | Aliaksei V. Krautsou | Vladimir D. Lazarev | Yuliya Z. Vassilyeva | A. Ya. Pak | Semen Budennyy | Alexander G. Kvashnin | Andrei A. Osiptsov
Source: npj Computational Materials (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Discovery of chemically modified higher tungsten boride by means of hybrid GNN/DFT approach High-throughput search for new crystal structures is extensively assisted by data-driven solutions. Here we address their prospects for more narrowly focused applications in a data-efficient manner. To verify and experimentally validate the proposed approach, we consider the structure of higher tungsten borides, WB4.2, and eight metals as W substituents to set a search space comprising 375k+ inequivalent crystal structures for solid solutions. Their thermodynamic properties are predicted with errors of a few meV/atom using graph neural networks fine-tuned on the DFT-derived properties of ca. 200 entries. Among the substituents considered, Ta provides the widest range of predicted stable concentrations and leads to the most considerable changes in mechanical properties. The vacuumless arc plasma method is used to perform synthesis of higher tungsten borides with different concentrations of Ta. Vickers hardness of WB5-x samples with different Ta contents is measured, showing increase in hardness. Boride ; Tungsten ; Materials science ; Metallurgy Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; MXene and MAX Phase Materials
Authors: Mayanak K Gupta
Source: Computer Physics Communications (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

A molecular dynamics postprocessing tool for analyzing the structure and dynamics of materials Phonon ; Molecular dynamics ; Fortran ; GRASP ; Fourier transform ; Software ; Dynamic structure factor ; Energy (signal processing) ; Function (biology) ; Source code Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; X-ray Diffraction in Crystallography
Authors: Yakun Zhu | Lu Zhang | Rui Yang | Yixuan Wang | Weidong Li | Lu‐Ning Wang
Source: Materials Genome Engineering Advances (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Artificial intelligence‐enabled synergistic design of strength and stress corrosion cracking resistance in light alloys Abstract High‐performance light alloys, including aluminum, titanium, magnesium alloys, etc., are utilized in aerospace, aviation, transportation and medical applications. A key challenge for these alloys is achieving both improved strength and stress corrosion cracking (SCC) resistance by optimizing the relationships between composition, processing, microstructure, and macroscopic properties. Artificial intelligence (AI)‐driven multi‐modal machine learning offers opportunities for materials design and prediction. Proposed strategies include applying machine learning‐based approaches for concurrent improvement of alloy strength and SCC resistance, conducting in situ high‐throughput experiments to investigate SCC microcrack initiation mechanisms under combined mechanical, microstructural, and corrosion conditions to support database development and developing correlative AI models for alloy microstructure evolution and macroscopic SCC failure behavior in service environments. Stress corrosion cracking ; Corrosion ; Microstructure ; Alloy ; Magnesium alloy ; Stress (linguistics) Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Annia Galano
Source: Chemical Science (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Antioxidant activity at the molecular level: exploring ways of action and computational tools to investigate them Despite their apparent simplicity, antioxidants are involved in numerous and complex processes. Several key aspects of antioxidant chemistry are covered in this review. (I) Their ways of action, which include scavenging free radicals; inhibition of ˙OH production <i>via</i> Fenton-like reactions by chelating redox metals; the repair of oxidatively damaged biomolecules; and modulation of the antioxidant/oxidant enzymatic system. (II) The main mechanisms involved in those ways of action, such as formal hydrogen atom transfer (<i>f</i>-HAT), single electron transfer (SET), sequential proton lost electron transfer (SPLET), coupled-deprotonation-chelation mechanism (CDCM), oxidant-enzyme inhibition, and antioxidant-enzyme activation. (III) Computational tools aiming to explore antioxidant activity (AOX). They are roughly grouped into four categories, depending on the used strategy (calculated properties): reactivity descriptors, thermochemistry, kinetics and ligand-receptor interactions. The approaches used to estimate them include calculations based on activity-structure relationships, quantum mechanical calculations, and molecular docking. The limitations and advantages of using these strategies and approaches are discussed, as well as some key points related to mimicking the associated chemical reactions (<i>e.g.</i> the importance of solvent polarity, pH, and diffusion). (IV) Some future research directions in the field, like the computational design of new (more efficient) antioxidants, and the emerging role of machine learning (ML) and artificial intelligence (AI) as efficient strategies to address AOX, can contribute to gaining a more complete picture about the complex chemical behavior usually involved in the health benefits offered by antioxidants. Reactivity (psychology) ; Antioxidant ; Electron transfer ; Mechanism (biology) ; Quantum chemical ; Redox ; Computational model ; Hydrogen atom ; Oxidation reduction Free Radicals and Antioxidants ; Computational Drug Discovery Methods
Authors: Xiyue Zhang | Yixiao Wang | Jing Hu | Qingyue Zhang | Xiaoting Xuan | Lufang Shi | Yong Sun
Source: International Journal of Hydrogen Energy (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

A molecular descriptor-based correlation with the composition of acid-pretreated cornstalk cultivation medium for biohydrogen production using a machine learning approach Biohydrogen ; Composition (language) ; Production (economics) ; Chemistry ; Computer science ; Process engineering ; Biochemical engineering ; Food science ; Hydrogen production ; Organic chemistry ; Hydrogen ; Engineering Machine Learning in Materials Science ; Process Optimization and Integration ; Microbial Metabolic Engineering and Bioproduction
Authors: Xiaodan Yin | Xiaorui Wang | Zhenxing Wu | Qin Li | Yu Kang | Yafeng Deng | Pei Luo | Huanxiang Liu | Guqin Shi | Yefei Wang | Xiaojun Yao | Chang‐Yu Hsieh | Tingjun Hou
Source: Journal of Cheminformatics (article, 2025-03-02)
Field: Materials Science — Materials Chemistry

Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt's robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.Scientific contribution Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt's universal workflow makes it suitable for various types of molecular optimization tasks. Template ; Computer science ; Data mining ; Combinatorial chemistry ; Chemistry ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Uzair Ahmad | Mehboob Khan | Asghar Ali | A. Zeeshan | Afifa Khanim | Sadia Nazir | Saira Imran | Abdalla Ibrahim | Ali Raza
Source: Scholars Academic Journal of Biosciences (review, 2025-01-22)
Field: Chemistry — Organic Chemistry

Designing Nanoparticle-Driven Materials for High-Performance Applications a Multidisciplinary Review Materials powered by nanoparticles have become essential for developing high-performance technologies, especially in the areas of energy storage and wastewater treatment. The design, production, and use of nanoparticles to address important issues in various fields are thoroughly examined in this study. Heavy metals, organic pollutants, and pathogens may be effectively removed from wastewater thanks to the special physicochemical characteristics of nanoparticles, which include large surface area, adjustable porosity, and improved reactivity. Similarly, nanoparticles are essential for improving energy density, charge-discharge rates, and lifespan stability in batteries because they can enhance ion transport dynamics and electrode performance. Innovations in functionalized nanoparticles, hybrid nanocomposites, and scalable synthesis techniques have been made possible by the integration of multidisciplinary approaches, such as materials science, nanotechnology, and environmental engineering. These developments have helped close the gap between laboratory research and practical applications, engaging a wide range of professionals in the process. Alongside the promise of new developments like green synthesis, issues like toxicity, environmental effect, and cost-efficiency are rigorously analyzed. It's important to consider the ethical implications of nanoparticle use, particularly in terms of their potential environmental impact and effects on human health. Another promising development is 'machine learning-driven material optimization, a process that uses machine learning algorithms to design and optimize nanoparticle-driven materials for specific applications, thereby enhancing their performance and efficiency. By providing a comprehensive picture of the current situation and prospective future paths, this study aims to inform the audience about the role of nanoparticles in high-performance technologies. Multidisciplinary approach ; Nanotechnology ; Nanoparticle ; Materials science ; Computer science ; Systems engineering ; Engineering ; Sociology Nanomaterials for catalytic reactions
Authors: Mahesh Hariharan | R.D. Eithiraj
Source: Materials Today Communications (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Machine learning model used to predict DFT-computed thermoelectric parameters of chalcogenide halides CuHgSCl and CuHgSI Materials science ; Chalcogenide ; Halide ; Thermoelectric effect ; Chalcogenide glass ; Thermodynamics ; Optoelectronics ; Inorganic chemistry Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Heusler alloys: electronic and magnetic properties