jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Azamat Salamatov | Jun Bai | Gowtham Atluri | Chaowen Guan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-22)
Field: Materials Science — Materials Chemistry

Quantum and Classical Graph Convolutional Neural Networks for Protein Ligand Dissociation Constant Prediction Abstract How long a drug stays bound to its target - the residence time - is now recognized as a stronger in vivo efficacy driver than binding affinity alone. Yet current machine learning (ML) models for dissociation kinetics (koff) ignore two critical sources of structure information: (1) the change in protein–ligand geometry across time, and (2) the ability to represent complex spatial interactions with fewer parameters. We extend a state-of -the -art spatial GNN for protein–ligand complexes with two innovations. First, we introduce a 2–timestep GCN+GRU model that learns structure changes before and after short molecular dynamics simulations. Second, we compress the model head using variational quantum circuits, preserving expressivity while removing 66% of parameters. On the full PDBbind-koff-2020 benchmark, temporal integration improves predictive accuracy, and quantum compression matches the full classical model’s accuracy at a fraction of its size. Our results show that time and quantum structure are underexplored, high-leverage axes for advancing kinetic ML models used in drug design. Quantum ; Dissociation (chemistry) ; Minification ; Graph ; Artificial neural network ; Convolutional neural network ; Kinetic energy Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Protein Structure and Dynamics
Authors: Ertuğrul Adıgüzel | Nadir Subaşı | Aysel ERSOY
Source: Turkish Journal of Mathematics and Computer Science (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Reliability Analysis of Surface Tracking Method Using Classification Methods (svm, random forest, XGBoost) with Whale Optimization Algorithm This study presents a comprehensive reliability analysis of a surface tracking methodology integrating machine learning classifiers—Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—optimized via the Whale Optimization Algorithm (WOA). The framework addresses surface degradation detection by leveraging experimental data from epoxy/MgO nanocomposite materials subjected to hydro-aging and electrical tracking conditions. Features representing physical and electrical behaviors were engineered and subjected to advanced preprocessing and classification workflows. WOA was employed for hyperparameter tuning and feature importance calibration, aiming to improve model robustness and accuracy. Results indicate that XGBoost, when optimized with WOA, outperforms SVM and RF in all metrics, achieving 92.3% accuracy and a macro F1-score of 0.94, with enhanced generalizability and resilience to class imbalance. The dataset consists of 180 samples, categorized into three reliability classes (‘low’, ‘medium’, and ‘high’) based on the quartile thresholds of reliability scores. The study underlines the critical importance of nature-inspired optimization in classification pipelines and provides a robust blueprint for predictive modeling in materials reliability applications. Robustness (evolution) ; Random forest ; Support vector machine ; Reliability (semiconductor) ; Preprocessor ; Extreme learning machine ; Hyperparameter ; Generalizability theory ; Feature extraction Corrosion Behavior and Inhibition ; High voltage insulation and dielectric phenomena ; Smart Materials for Construction
Authors: Doeke R. Hekstra | Harrison Wang | Margaret A. Klureza | Jack B. Greisman | Kevin Dalton
Source: Structural Dynamics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Sensitive Detection of Structural Differences within a Statistical Framework for Comparative Crystallography Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that observations must be placed on the same scale by correcting for experimental artifacts. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning and statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments. Computer science ; Statistical analysis ; X-ray crystallography ; Data mining ; Algorithm ; Mathematics ; Diffraction ; Statistics ; Physics Crystallization and Solubility Studies
Authors: Muyun Li (17271035) | Qingyue Cai (18596853) | Huayi Lai (18596850) | Honglong Ning (3717409) | Sifan Kong (17271038) | Chenxiao Guo (596076) | Bocheng Jiang (21366990) | Guoping Su (2403721) | Rihui Yao (3930131) | Junbiao Peng (1521604)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Polymers and Plastics

Artificial High-Throughput Aided Design of High-PerformanceLiquid Electrochromic Devices Based on Multilayer Perceptron NeuralNetworks Electrochromic devices (ECDs), serving as thermal modulation and decorative components, are extensively employed across various domains. To meet diverse application demands in different contexts, the product parameters of ECDs continually vary, notably within the electrochromic layer. In this work, a multilayer perceptron neural network is leveraged to comprehend the transition from material parameter alterations to device performance parameter changes. Specifically, 56 different testing conditions were evaluated by varying the concentration of the electrochromic molecule and the counter species; each combination was then subject to 6 different testing voltage windows, for a total of 336 different testing conditions. Subsequently, a model comprising three hidden layers and three output neurons is developed, expanding the data set simulation to 2000 groups, yielding an <i>R</i><sup>2</sup> over 0.9 on modulation amplitude. Through this model, accurate estimations of performance parameters of numerous unmanufactured liquid-based ECDs are achieved, with projected outcomes highlighting a peak modulation amplitude of 78.38%@620 nm. It is verified that the modulation amplitude prediction error is below 5% and demonstrates stable cyclic performance over 1000 cycles (97.93% at 1.45 V), thereby underscoring the feasibility of our approach. This research serves as a pertinent instance of the synergy between multilayer perceptron neural networks and ECDs, which can effectively provide guidance for more efficiently handling the complex and diverse parameters in the field of electrochromism. Artificial neural network ; Multilayer perceptron ; Modulation (music) ; Electrochromism ; Perceptron ; Waveform ; Set (abstract data type) ; Electrochromic devices Transition Metal Oxide Nanomaterials ; Conducting polymers and applications ; Nanomaterials and Printing Technologies
Authors: SHETTAHALLI MANTAIAH VINU KUMAR | Erusagounder Sakthivelmurugan | Manikandaprabu Nallasivam
Source: Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (article, 2025-04-28)
Field: Materials Science — Polymers and Plastics

Experimental evaluation on abrasive wear properties of <i>Alstonia macrophylla</i> fiber reinforced polypropylene composite using ANN model Development of natural fiber reinforced polymer composites over synthetic based composites offers numerous benefits such as biodegradability, sustainability, light weight, higher strength to weight ratio and yet cost effectiveness. Most of the components like bearings, pulleys, gears, bushes, pulley linings, brake pads, friction plates etc., are mainly made of composite materials which requires understanding of their tribological behavior. Thus, for the first time, study is undertaken to investigate the two-body abrasion wear behavior of the Alstonia macrophylla fiber reinforced Polypropylene (A-PP) composites using pin on disk machine for the selected input variables which is in accordance with ASTM G 99 standards. A-PP composites consisting 40% of fiber volume are prepared by adopting film stacking method and cured under compression molding machine. Sliding distance, grit size of abrasive paper, and applied load are selected as an input variable and their output responses measured are coefficient of friction (COF) and specific wear rate (SWR). Experimental results revealed that, both COF and SWR are found to decrease with increase in the applied load. Moreover, finer abrasive paper (1000 grit size) found to be less effective against the A-PP specimen compared to coarse abrasive paper (320 grit size). Formation of thin polymer film and accumulation of wear debris between the grits during the increase of load are found to be more appealing reasons for decreasing COF and SWR. Same observations are exposed by the FESEM analysis. Furthermore, feedforward backpropagation artificial neural network (ANN) is implemented to predict the aforesaid output responses. Confirmatory results showed that experimental and predicted values are within the acceptable range. For COF, error in prediction within ±6%, whereas for SWR it is ±7%. Materials science ; Composite material ; Polypropylene ; Abrasive ; Tribology ; Abrasion (mechanical) ; Composite number ; Fiber Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Foaming and Composites
Authors: Giuseppe Bonifazi | Giuseppe Capobianco | Paola Cucuzza | Silvia Serranti
Source:  (article, 2025-04-11)
Field: Chemistry — Analytical Chemistry

Application of hyperspectral imaging for identifying polymer blends in polypropylene flexible packaging waste for enhanced recycling In this study, a fast and efficient analytical procedure based on hyperspectral imaging (HSI), combined with machine learning (ML), was applied to address the challenges in sensor-based sorting of flexible plastic packaging waste (FPPW). Among the challenges that recycling processes must overcome to obtain a homogeneous FPP as secondary raw material, one of the most critical is the detection and removal of contaminants, particularly when polymer blends are involved. The aim of the work was to effectively identify contaminants, i.e., polypropylene blends (Blend PP), within a homogeneous FPPW stream of polypropylene (PP). Hyperspectral images of plastic flakes, representative of a post-consumer product composed of FPP in PP contaminated by Blend PP, were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). A chemometric approach was applied, starting from the application of different preprocessing algorithms to enhance the spectral characteristics of each class (i.e., PP and Blend PP classes), followed by principal component analysis (PCA) to explore the spectral data and the development of a ML classification model capable of distinguishing PP and Blend PP classes. The flake samples were also characterized by Fourier transform-infrared (FT-IR) spectroscopy in Attenuated Total Reflection (ATR), in order to evaluate the prediction performance obtained by HSI. The results demonstrated the effectiveness of the HSI-based strategy in supporting automated sorting and contaminant detection, as verified by FT-IR spectroscopy, significantly improving the recycling of FPPW composed of PP. Hyperspectral imaging ; Polypropylene ; Materials science ; Polymer ; Waste management ; Environmental science ; Process engineering ; Composite material ; Computer science ; Engineering ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Ад Быков | О.В. НАУМЕНКО | А.П. ЩЕРБАКОВ
Source: Optika atmosfery i okeana (article, 2025-12-25)
Field: Chemistry — Spectroscopy

АВТОМАТИЧЕСКАЯ ИДЕНТИФИКАЦИЯ ЛИНИЙ В КОЛЕБАТЕЛЬНО-ВРАЩАТЕЛЬНЫХ СПЕКТРАХ: ПРОГРАММНОЕ ОБЕСПЕЧЕНИЕ, ОСНОВАННОЕ НА ОБУЧАЕМОЙ НЕЙРОННОЙ СЕТИ Представлена усовершенствованная интернет-доступная экспертная система СЛОН для анализа молекулярных спектров высокого разрешения, разработанная в лаборатории молекулярной спектроскопии Института оптики атмосферы СО РАН. Экспертная система основана на использовании модели нейронной сети, способной самостоятельно принимать решения при анализе комбинационных разностей, образованных группами переходов молекул с различных вращательных подуровней основного состояния на одно и то же возбужденное колебательно-вращательное состояние. Усовершенствован набор признаков, по которым нейронная сеть отличает правильный вариант комбинационной разности от случайных реализаций. Сняты ограничения на размер анализируемого спектра. Формат используемых баз данных теперь универсален и позволяет расширить класс исследуемых молекул. Современный многоплатформенный (multy-platform) пользовательский интерфейс позволяет собирать данную программу для систем Windowsи Linux. Описаны принципы работы, опыт эксплуатации и очерчены перспективы развития созданной экспертной системы. This article presents an improved internet-accessible expert system, SLON, for analyzing high-resolution molecular spectra. It was developed in the Laboratory of Molecular Spectroscopy at Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences. The SLON expert system is based on a neural network model capable of making independent decisions when analyzing combination differences formed by groups of molecular transitions from different rotational sublevels of the ground state to the same excited vibrational-rotational state. The set of features by which the neural network distinguishes the correct variant of a combination difference from random realizations has been improved. Restrictions on the size of the analyzed spectrum have been removed. The format of the databases used is now universal and enables expanding the class of molecules under study. A modern multi-platform user interface allows this program to be compiled for Windows and Linux systems. The operating principles, operational experience, and prospects for the development of the created expert system are described. Artificial neural network ; Expert system ; Set (abstract data type) ; Interface (matter) ; State (computer science) ; Molecular spectroscopy ; Class (philosophy) Molecular spectroscopy and chirality ; Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications
Authors: Junsu Lee | Min Kyoung Kim | Aziz Ahmed | Yurij Mozharivskyj | Tae‐Soo You
Source: Zeitschrift für anorganische und allgemeine Chemie (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Combined Machine Learning and Experimental Study for the Zintl Phase Thermoelectric Ba <sub> 1− <i>x</i> </sub> Sr <sub> <i>x</i> </sub> Zn <sub> 2− <i>y</i> </sub> Cd <sub> <i>y</i> </sub> Sb <sub>2</sub> System Two new Sr and Cd cosubstituted zintl phases of Ba 0.92(2) Sr 0.08 Zn 1.30(3) Cd 0.70 Sb 2 and Ba 0.60(2) Sr 0.40 Zn 0.32(4) Cd 1.68 Sb 2 are successfully synthesized, and these compounds are crystallized in the orthorhombic BaCu 2 S 2 ‐type and the trigonal CaAl 2 Si 2 ‐type phases, respectively. Overall, crystal structures are constructed by the assembly of 1) the structural backbone of the infinite chain and 2) the Ba/Sr mixed site, filling the space within or in‐between anionic frameworks. The structural selectivity between these two title phases can be explained by the radius ratio criterion of cationic‐to‐anionic elements ( r + / r – ). Furthermore, the r + / r – criterion determining the preferred structure is closely related to the Ba/SrSb bond distance and eventually affects the Sr substitution level in both phases. A series of DFT calculations suggest that the BaCu 2 S 2 ‐type phase exhibits a heavily doped semiconductor behavior, while the CaAl 2 Si 2 ‐type phase shows semiconducting behavior. The temperature‐dependent thermoelectric (TE) property measurements prove that Ba 0.60(2) Sr 0.40 Zn 0.32(4) Cd 1.68 Sb 2 exhibits a relatively higher ZT value than Ba 0.92(2) Sr 0.08 Zn 1.30(3) Cd 0.70 Sb 2 due to its higher electrical conductivities and Seebeck coefficients. Machine learning (ML)‐based predictions successfully capture general TE trends of the title phases and show higher ZT values for the BaCu 2 S 2 ‐type phase compared to the CaAl 2 Si 2 ‐type phase. Thermoelectric effect ; Materials science ; Phase (matter) ; Thermoelectric materials ; Mineralogy ; Metallurgy ; Chemistry ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Advanced Semiconductor Detectors and Materials ; Heusler alloys: electronic and magnetic properties
Authors: Jessica Braun (19834507) | Paul Katzberger (12118440) | Gregory A. Landrum (1450801) | Djahan Lamei (22815179) | Sereina Riniker (1450804)
Source: Figshare (article, 2025-12-10)
Field: Chemistry — Physical and Theoretical Chemistry

Unraveling Torsional Preferences: Comparative Analysisof Torsion Motif Torsional-Angle Distributions across Different Environments Understanding the conformational ensemble of molecules in different environments is at the core of many research efforts. In conformer generation and geometry optimization, the complexity of the conformer space arises from the underlying torsion-angle distributions, which, in the case of force fields and some <i>in silico</i> conformer generators like ETKDG, are derived from accumulated torsion profiles for a predefined set of torsion motifs (termed ″torsion motif torsional-angle distributions″, tmTADs). Comparative studies of conformer generation and global optimization algorithms often neglect that tmTADs are sensitive to the environment they are extracted from, leading to comparisons of conformational ensembles and minimum-energy conformations from, e.g., crystal versus vacuum environments. Here, we present a large-scale comparative study of tmTADs across different environments, namely crystal, vacuum, water, and hexane, where the ensembles in the noncrystal environments are accessed through a computational workflow using the OpenFF-2.0.0 force field in combination with the graph neural network-based implicit solvent (GNNIS) approach. Our results show that the effects in the different environments, such as solvent–solute interactions in water and hexane, and packing effects in the crystal, produce strikingly distinct torsion distributions for most of the selected torsion motifs. In addition to qualitative and quantitative comparison of the extracted tmTADs, we also provide an automated fitting procedure that allows rapid parametrization of the distributions. These newly found parameters can be employed in a solvent-specific conformer generation procedure in the future. Conformational isomerism ; Torsion (gastropod) ; Force field (fiction) ; Ab initio ; Dihedral angle ; Conformational ensembles ; Searching the conformational space for docking ; Molecular dynamics Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Jessica Braun | Paul Katzberger | Gregory A. Landrum | Djahan Lamei | Sereina Riniker
Source: Journal of Chemical Information and Modeling (article, 2025-12-09)
Field: Chemistry — Physical and Theoretical Chemistry

Unraveling Torsional Preferences: Comparative Analysis of Torsion Motif Torsional-Angle Distributions across Different Environments Understanding the conformational ensemble of molecules in different environments is at the core of many research efforts. In conformer generation and geometry optimization, the complexity of the conformer space arises from the underlying torsion-angle distributions, which, in the case of force fields and some <i>in silico</i> conformer generators like ETKDG, are derived from accumulated torsion profiles for a predefined set of torsion motifs (termed ″torsion motif torsional-angle distributions″, tmTADs). Comparative studies of conformer generation and global optimization algorithms often neglect that tmTADs are sensitive to the environment they are extracted from, leading to comparisons of conformational ensembles and minimum-energy conformations from, e.g., crystal versus vacuum environments. Here, we present a large-scale comparative study of tmTADs across different environments, namely crystal, vacuum, water, and hexane, where the ensembles in the noncrystal environments are accessed through a computational workflow using the OpenFF-2.0.0 force field in combination with the graph neural network-based implicit solvent (GNNIS) approach. Our results show that the effects in the different environments, such as solvent-solute interactions in water and hexane, and packing effects in the crystal, produce strikingly distinct torsion distributions for most of the selected torsion motifs. In addition to qualitative and quantitative comparison of the extracted tmTADs, we also provide an automated fitting procedure that allows rapid parametrization of the distributions. These newly found parameters can be employed in a solvent-specific conformer generation procedure in the future. Conformational isomerism ; Torsion (gastropod) ; Force field (fiction) ; Dihedral angle ; Ab initio ; Conformational ensembles ; Molecular dynamics ; Searching the conformational space for docking ; Molecule Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: He‐Ping Wu | Jie Zhang | Linya Huang | Luyue Jiang | Zixuan Li | Zhikang Li | Libo Zhao | Gang Zhao | Wei Ren | Gang Niu
Source: Sensors and Actuators B Chemical (article, 2025-11-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Highly uniform and ultra-sensitive graphene hybrid SERS substrates fabricated by colloidal lithography Graphene ; Fabrication ; Substrate (aquarium) ; Lithography ; Raman spectroscopy ; Polystyrene ; Colloid ; Colloidal crystal Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation