jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jack Rosenberger | Johannes Tlatlik | Nils Rump | Sebastian Münstermann
Source: Engineering Failure Analysis (article, 2025-03-22)
Field: Materials Science — Materials Chemistry

Prediction of statistical force–displacement curves of Charpy-V impact tests based on unsupervised fracture surface machine learning • Unsupervised machine learning fractography analysis corresponding to lower shelf, transition, and upper shelf regimes. • Extracted clusters strongly correlate with key parameters, enabling accurate classification of fracture behavior. • Explainable AI using a Grad-CAM analysis. • Statistical material-sensitive F-s curve inference with ± 1σ corridors. While conventional pendulum impact tests only measure a material’s integral energy absorption, the instrumented version of the test provides valuable additional insights by extracting force–displacement behaviour of the loaded specimen. The latter, however, requires auxiliary testing equipment, calibration procedures and evaluation methods. Therefore, this study aims to predict force–displacement behaviour of instrumented Charpy impact tests solely on the basis of analyzing images of specimen fracture surfaces postmortem. This is explored and achieved by using unsupervised machine learning techniques for computer vision. By using unsupervised computer vision on fracture images from 4 steels, we assess the feasibility of classifying fracture surfaces and deriving statistical force–displacement curves and provide crucial interpretability of the model’s decision making. The results indicate the model’s ability to learn the necessary representations without the need of supervision. The unsupervised model can extract significant insights from fracture images alone, supporting efficient, accurate, and interpretable materials testing, where confidence intervals of 97 % can already be met for the upper shelf. This allows detailed information about the mechanical behaviour of the material to be obtained from non-instrumented tests. Charpy impact test ; Displacement (psychology) ; Fracture (geology) ; Surface (topology) ; Materials science ; Structural engineering ; Composite material ; Engineering ; Mathematics ; Geometry ; Psychology ; Ultimate tensile strength High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Non-Destructive Testing Techniques
Authors: Omar A. M. Abdelraouf
Source: Optics Communications (article, 2025-10-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

NanoPhotoNet-NL: AI-assisted design of nonlinear multilayer metasurfaces for broadband tunable deep ultraviolet emission Broadband ; Nonlinear system ; Parametric statistics ; Nonlinear optics ; Artificial neural network ; Wavelength ; Convolutional neural network ; Field (mathematics) Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Optical Wireless Communication Technologies
Authors: Heqing Tian | Tianyu Liu | Wenguang Zhang
Source: Materials Today Physics (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

High precision prediction of structure and thermal properties of ternary eutectic carbonates by machine learning potential for solar energy application Materials science ; Eutectic system ; Ternary operation ; Thermal ; Energy (signal processing) ; Thermodynamics ; Engineering physics ; Metallurgy ; Microstructure ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Iron and Steelmaking Processes
Authors: Debendra Meher | Nikhil V. S. Avula | Sundaram Balasubramanian
Source: The Journal of Chemical Physics (article, 2025-01-24)
Field: Materials Science — Ceramics and Composites

Slowly quenched, high pressure glassy B2O3 at DFT accuracy Modeling inorganic glasses requires an accurate representation of interatomic interactions, large system sizes to allow for intermediate-range structural order, and slow quenching rates to eliminate kinetically trapped structural motifs. Neither first principles-based nor force field-based molecular dynamics (MD) simulations satisfy these three criteria unequivocally. Herein, we report the development of a machine learning potential (MLP) for a classic glass, B2O3, which meets these goals well. The MLP is trained on condensed phase configurations whose energies and forces on the atoms are obtained using periodic quantum density functional theory. Deep potential MD simulations based on this MLP accurately predict the equation of state and the densification of the glass with slower quenching from the melt. At ambient conditions, quenching rates larger than 1011 K/s are shown to lead to artifacts in the structure. Pressure-dependent x-ray and neutron structure factors from the simulations compare excellently with experimental data. High-pressure simulations of the glass show varied coordination geometries of boron and oxygen, which concur with experimental observations. Quenching (fluorescence) ; Molecular dynamics ; Materials science ; Density functional theory ; Boron ; Representation (politics) ; Chemical physics ; Statistical physics ; Phase (matter) ; Range (aeronautics) ; Force field (fiction) ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry ; Fluorescence ; Quantum mechanics Glass properties and applications ; Material Dynamics and Properties ; Phase-change materials and chalcogenides
Authors: M. Vignesh | K Vasugi
Source: Materials Today Communications (article, 2025-03-25)
Field: Materials Science — Biomaterials

Machine learning optimization of KOH treatment parameters for enhanced tensile strength and cellulose content in sisal fibers: A synergistic approach Materials science ; Ultimate tensile strength ; SISAL ; Cellulose ; Composite material ; Cellulose fiber ; Fiber ; Chemical engineering Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications
Authors: Azat O. Tipeev | Edgar Dutra Zanotto
Source: Acta Materialia (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Crystal nucleation and growth dynamics of aluminum via quantum-accurate MD simulations Nucleation ; Materials science ; Molecular dynamics ; Aluminium ; Crystal growth ; Crystal (programming language) ; Chemical physics ; Quantum ; Crystallography ; Computational chemistry ; Thermodynamics ; Metallurgy ; Physics ; Quantum mechanics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Microstructure and mechanical properties
Authors: Sachin Shinde | Preeti Khulbe
Source: South Eastern European Journal of Public Health (review, 2025-05-25)
Field: Chemistry — Analytical Chemistry

Analytical Method Validation: A Comprehensive Review of Current Practices Analytical method validation is a critical process in industries such as pharmaceuticals, food safety, biotechnology, and environmental monitoring. It guarantees reliability, reproducibility, and compliance with regulatory obligations. This review covers current practices including adherence to guidelines from ICH Q2(R1), USP, and EMA. Method suitability is analyzed for its key validation parameters, including accuracy, precision, specificity, detection limits, and robustness. Validation approaches are changing with the adoption of automation, artificial intelligence (AI), machine learning, and Analytical Quality by Design (AQbD) emerging trends. The review also addresses the matrix effects, sensitivity- specificity tradeoffs, and method transfers between laboratories. Valuable use of validated methods is demonstrated in pharmaceuticals, biologics, food safety, and environmental monitoring. Chromatographic methods and lifecycle management strategies are shown to be successful in case studies. The review concludes by highlighting gaps, (such as global harmonization) and opportunities for innovation in green analytical chemistry and real-time methods. Current (fluid) ; Computer science ; Management science ; Engineering ; Electrical engineering Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses
Authors: Chelsea E. R. Edwards | Hongyi Zhang | Ginny Wang | Matthew E. Helgeson
Source: Langmuir (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Spontaneous Formation of Core–Shell Microdroplets during Conventional Coacervate Phase Separation We report the single-step formation and stability of protocell-like, core-shell coacervate droplets comprising a polyelectrolyte-rich shell and a solvent-rich vacuole core from the poly(allylamine hydrochloride) (PAH) and poly(acrylic acid) (PAA) system. These double emulsion (DE) coacervate droplets coexist with single emulsion (SE) droplets, suggesting a kinetic mechanism of formation. We use high-throughput microscopy and machine learning to classify droplet morphologies across various final compositions (polyelectrolyte ratios and salt concentrations) and processing routes (mixing rate and thermodynamic path). We find that DE droplets form preferentially over SE droplets at a wide range of compositions using a slow injection mixing rate. DE droplet formation is enhanced at lower salt (NaCl) levels and near 1:1 charge stoichiometry, showing a preference for polycation excess. DE droplets are stable to the micron scale and retain their core-shell structure even after coalescence. Nevertheless, they are metastable; direct observations of various coarsening phenomena suggest that they are primarily stabilized by the viscoelasticity and high viscosity of the polymer-rich shell. Overall, the scalable, simple mixing process used herein offers a novel mechanism to produce multiphase coacervate droplets that is orthogonal to existing routes, which require either dropwise synthesis or thermodynamic tuning. Coacervate ; Chemistry ; Polyelectrolyte ; Chemical engineering ; Coalescence (physics) ; Mixing (physics) ; Phase (matter) ; Polymer ; Chemical physics ; Chromatography ; Organic chemistry Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Polymer Surface Interaction Studies
Authors: Sujeong Lee | Juhwan Noh | Ho Jin Ryu
Source: Journal of Hazardous Materials (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Discovery of multi-metal-layered double hydroxides for decontamination of iodate by machine learning-assisted experiments Layered double hydroxides ; Iodate ; Adsorption ; Ternary operation ; Human decontamination ; Chemistry ; Materials science ; Inorganic chemistry ; Computer science ; Waste management ; Iodide ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Hendrik Heimes | Ethan P. Turner | Alan Salek | Jessica Wierbik | Xingshuo Huang | William R. Dunstan | Nigel A. Marks | Dougal G. McCulloch | J. E. Bradby
Source: Diamond and Related Materials (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Mechanisms of nanodiamond and amorphous diamond-like carbon formation following room temperature compression of C60 The transformation of carbon into sp 3 -rich phases is of broad interest for developing superhard materials, yet the role of non-hydrostatic pressures during such transformations remains poorly understood. While buckminsterfullerene (C 60 ) is known to collapse under high pressures and temperatures, the structural evolution pathways under non-hydrostatic conditions have not been fully established. Here, we report the formation of transparent, amorphous diamond-like carbon ( a-D ) from C 60 compressed to 49 GPa at room temperature under non-hydrostatic stress. Transmission electron microscopy of the recovered material reveals a predominantly sp 3 -bonded network with (85 ± 5)% sp 3 bonds and a density of (3.05 ± 0.10) g cm −3 , interlaced with residual C 60 molecules and narrow bands of nanocrystalline diamond. To understand the transformation mechanism, we performed molecular dynamics simulations using an atomic cluster expansion potential. The simulations show that under uniaxial stress, fullerene cages collapse at lower pressures (29–32 GPa) than under hydrostatic conditions (42–45 GPa), forming a structure consistent with amorphous diamond-like carbon. Annealing simulations on the amorphous structure at 50 GPa result in nanocrystalline diamond formation, suggesting that localised heat spikes generated by an adiabatic shear band process drive the formation of observed nanodiamond regions. • Formation of recoverable sp 3 -bonded carbon at room temperature. • Formation of nanodiamonds in shear bands. • Non-hydrostatic stresses promote C 60 cage collapse. • Machine learning potentials are powerful tools to model high pressure responses in carbon. Nanodiamond ; Diamond ; Carbon fibers ; Materials science ; Amorphous solid ; Amorphous carbon ; Nanotechnology ; Compression (physics) ; Diamond-like carbon ; Fullerene ; Chemical engineering ; Composite material ; Chemistry ; Composite number ; Crystallography ; Thin film ; Organic chemistry ; Engineering Diamond and Carbon-based Materials Research ; Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites
Authors: Obiekezie C Obijiofor | Alexander S. Novikov
Source: Journal of Molecular Modeling (review, 2025-06-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the role of density functional theory in the design of gold nanoparticles for targeted drug delivery: a systematic review Drug delivery ; Nanotechnology ; Density functional theory ; Drug ; Colloidal gold ; Nanoparticle ; Computer science ; Medicine ; Pharmacology ; Chemistry ; Materials science ; Computational chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions ; Nanoparticle-Based Drug Delivery
Authors: Ewelina Bojarska | Wojciech Zajączkowski | Elwira Furtak | Maksymilian Stela | Leslaw Gorniak | Marcin Podogrocki | Michał Bijak
Source: Molecules (review, 2025-08-31)
Field: Chemistry — Spectroscopy

Modern Methods for Detection of Fentanyl and Its Analogues: A Comprehensive Review of Technologies and Applications Fentanyl and its analogues represent a severe threat due to their extreme potency and increasing prevalence in illicit drug supplies. Even trace amounts (on the order of a couple of milligrams) can be lethal, contributing to a surge in opioid overdose deaths worldwide. Beyond the public health crisis, fentanyl has emerged as a security concern, with the potential for deliberate use as a chemical agent in CBRN scenarios. This underscores the critical need for rapid and accurate detection methods that can be deployed by security forces and first responders. Modern technology offers a range of solutions-from portable mass spectrometers and spectroscopic devices to electrochemical sensors and immunoassay kits-that enable on-site identification of fentanyl and its analogues. This review provides a comprehensive overview of detection techniques, examining their capabilities and applications in law enforcement, border control, and CBRN incident response. We highlight how integration of advanced sensors with machine learning is enhancing detection accuracy in complex field environments. Challenges such as operational constraints and the ever-evolving variety of fentanyl analogues are discussed, and future directions are recommended to improve field-deployable detection tools for safety and security applications. Fentanyl ; Nanotechnology ; Biochemical engineering ; Computational biology ; Computer science ; Medicine ; Data science ; Pharmacology ; Engineering ; Materials science ; Biology Mass Spectrometry Techniques and Applications ; Forensic Toxicology and Drug Analysis ; Pharmacological Effects and Assays
Authors: Ajaz Hussain | Abdulraheem S. A. Almalki | Abdul Sami | Muhammad Tariq | Farhan Zafar | Muhammad Ali Khan | Naeem Akhtar | Sagheer Ahmad | Muhammad Usman Ur Rehman | Gaber A. M. Mersal | Mohamed M. Ibrahim
Source: Materials Chemistry and Physics (article, 2025-08-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-guided fabrication of NiO/CoO and CuO/CoO based nanocomposites for high-performance supercapacitors Non-blocking I/O ; Supercapacitor ; Fabrication ; Nanocomposite ; Materials science ; Nanotechnology ; Chemical engineering ; Electrochemistry ; Chemistry ; Electrode ; Engineering ; Medicine ; Physical chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Transition Metal Oxide Nanomaterials
Authors: Lukas M. Sigmund | Tina Seifert | Riya Halder | Giulia Bergonzini | Magnus J. Johansson | Per‐Ola Norrby | Kjell Jorner | Mikhail Kabeshov
Source: Angewandte Chemie International Edition (article, 2025-09-14)
Field: Chemistry — Organic Chemistry

Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach The direct thianthrenation of aromatic C─H bonds is a valuable late-stage functionalization strategy that can assist, for example, the development of new drugs. We herein present a predictive computational model for this reaction, denoted PATTCH, which is based on semiempirical quantum mechanics and machine learning. It classifies each C<sub>aromatic</sub>-H unit either as reactive or not with an accuracy of above 90%. It can address both the site-selectivity and reaction feasibility question associated with the thianthrenation protocol. First, this was achieved by selecting carefully engineered features, which take into account the electronic and steric influence on the site-selectivity. Second, parallel experimentation was used to supplement the available literature data with 54 new negative reactions (unsuccessful thianthrenation), which we show was instrumental for developing the PATTCH tool. Ultimately, we successfully applied the model to a challenging test set encompassing the differentiation between carbocycle versus heterocycle functionalization, the identification of substrates that were reported to result in a mixture of isomeric products, and to molecules that could not be thianthrenated. The computational predictions were experimentally validated. The PATTCH tool can be obtained free of charge from https://github.com/MolecularAI/thianthrenation_prediction. Steric effects ; Selectivity ; Identification (biology) ; Molecule ; Charge (physics) ; Set (abstract data type) ; Surface modification ; Reaction conditions ; Test set Sulfur-Based Synthesis Techniques ; Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions
Authors: Jacob Toney | R. Michel | Aaron Garrison | Ilia Kevlishvili | Heather J. Kulik
Source: Journal of the American Chemical Society (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Identifying Dynamic Metal–Ligand Coordination Modes with Ensemble Learning Knowledge of how a ligand coordinates a metal is essential for mechanistic and data-driven studies of transition metal complexes (TMCs), but most analyses assume a single binding interaction for a given metal-ligand pair. In catalysis, many ligands engage in dynamic, hemilabile coordination modes, challenging this assumption. In this work, we curate data sets of hemilabile and nonhemilabile ligands from experimentally characterized structures in the Cambridge Structural Database, analyze trends in observed coordination modes, and introduce four exhaustive and mutually exclusive types of hemilability. Using these labeled data sets, we train graph neural networks to carry out classification of hemilabile ligands with high accuracy, precision, and recall and develop an ensemble algorithm that predicts primary and alternative chemically plausible coordination modes from SMILES strings in an end-to-end fashion. We demonstrate the utility of our algorithm by generating novel TMCs in predicted coordination modes and calculating the corresponding energy difference due to changes in coordination (i.e., Δ<i>E</i><sub>c</sub>) with density functional theory. Comparing our novel TMCs in multiple poses against an energetic criterion from experimentally observed TMCs confirms the plausibility of our alternative poses. We anticipate that our open-source workflows will accelerate organometallic discovery in experimental and virtual screening campaigns by proposing realistic metal-ligand coordination. Workflow ; Graph ; Ensemble learning ; Virtual screening ; Artificial neural network ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Prakriti P. Joshi | Davis Unruh | Nasim Mirzajani | Thomas E. Gage | Luqing Wang | Ruiyu Li | Haihua Liu | Liangbo Liang | Ilke Arslan | Maria K. Y. Chan | Sarah B. King
Source: ACS Nano (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Lattice Anisotropy-Driven Reduction of Phonon Velocities in Black Phosphorus Phonon dynamics and transport determine how heat is utilized and dissipated in materials. In 2D systems for optoelectronics and thermoelectrics, the impact of nanoscale material structure on phonon propagation is central to controlling thermal conduction. Here, we directly observe in-plane coherent acoustic phonon propagation in black phosphorus (BP) using ultrafast electron microscopy. We identify a significant reduction of the group velocities in directions intermediate to the armchair and zigzag lattice directions. Using a machine learning-based model with an >8000 atom supercell, we find that this slowing results from the mixing of in-plane transverse and longitudinal acoustic phonons and is independent of broken symmetries of edge reconstructions. This work demonstrates how coherent phonon transport is sensitive to propagation direction in the lattice plane. Phonon ; Anisotropy ; Black phosphorus ; Condensed matter physics ; Lattice (music) ; Materials science ; Physics ; Quantum mechanics ; Optoelectronics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Tianli Wang | Yi Zhang | Fei Li | Ning Cao
Source: Computers and Electronics in Agriculture (article, 2025-03-06)
Field: Chemistry — Analytical Chemistry

Preprocessing and regression approaches alter the spectral estimation accuracy of plant phosphorus content—A three-level meta-analysis Regression analysis ; Regression ; Estimation ; Preprocessor ; Content (measure theory) ; Phosphorus ; Statistics ; Meta-analysis ; Meta-regression ; Spectral analysis ; Computer science ; Mathematics ; Environmental science ; Artificial intelligence ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Isotope Analysis in Ecology