jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alessandro Cossard | Chiara Sabena | Gianluca Bianchini | Emanuele Priola | Roberto Gobetto | Andrea Aramini | Michele R. Chierotti
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

Advanced feature analysis for enhancing cocrystal prediction The design of novel pharmaceutical crystal forms, including molecular salts and cocrystals, has gained significant attention from pharmaceutical companies due to their ability to modulate key physicochemical and biopharmaceutical properties. The selection of appropriate coformers for cocrystallization, however, remains a challenge, typically relying on labor-intensive trial-and-error methods. This study introduces FeatureMaster , a tool designed to evaluate the representativeness of training sets relative to test sets, thereby enhancing the reliability of machine learning models in predicting cocrystallization outcomes. We employed four key algorithms — feature overlap, quartiles, Cohen’s D, and p-value analysis — to a priori assess the predictive accuracy. The efficacy of these methods was evaluated on two systems: piracetam (PRC) and pyridoxine (PN). The test set data were collected from in-house experiments: the PRC and PN test sets were experimentally created with a series of coformers (20 for PRC and 14 for PN) using different synthetic techniques. The experimental tests lead to the formation of 3 new cocrystals for PRC (with quercetin, 2-ketoglutaric acid, and malic acid) and 7 new molecular salts for PN (with 2-ketoglutaric acid, pimelic acid, cinnamic acid, gallic acid, N-acetylcysteine, and caffeic acid). Training sets were collected from literature and features calculated using Hansen Solubility Parameters (HSP), Hydrogen Bond Energy (HBE), Molecular Complementarity (MC), and Quantitative Structure-Activity Relationship (QSAR) methods. Models were developed using the Random Forest algorithm, known for its robustness in handling complex datasets. Our results demonstrate that statistical analyses using overlap, Cohen’s D and p-values are fundamental for improving the prediction and for providing a priori insights into the model's reliability. This approach reduces the experimental tests and resource consumption in the cocrystal screening process, offering a promising strategy for future pharmaceutical development. • Development of FeatureMaster, a tool to improve models for predicting cocrystals. • Use of feature overlap, Cohen’s D, and p-value to assess prediction accuracy. • Machine learning to reduce experimental testing and enhance drug development. • Achievement of new cocrystals and molecular salts for piracetam and pyridoxine. • Evaluation of the representativeness of training sets relative to test sets. Cocrystal ; Feature (linguistics) ; Computer science ; Pattern recognition (psychology) ; Data mining ; Artificial intelligence ; Chemistry ; Molecule Crystallization and Solubility Studies ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Andrea N. Giordano | Samuel Franqui-Rios | Steven M. Quarin | Der Vang | Drake Austin | Abigail G. Doyle | Luke A. Baldwin | Pietro Strobbia | Rahul Rao
Source: ACS Applied Nano Materials (article, 2025-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication of SERS Substrates Using Silver-Coated Gold Nanostars for Chemical Sensing: A Multiobjective Bayesian Optimization Approach Field detection of trace analytes remains a challenge in many sectors, including national security, public health, and environmental monitoring. This challenge arises from the scarcity of field-deployable technologies capable of identifying trace analytes in the complex matrices of real-world samples. Surface-enhanced Raman scattering (SERS) is a promising technique for chemical sensing because it offers sensitive and specific analyte detection with commercial portable Raman systems. Despite the immense potential of SERS, commercially available SERS technologies that are accurate, reliable, cost-effective, and compatible with portable spectrometers remain scarce. This scarcity is due to the challenges associated with the complex design and resource-intensive optimization of the manufactured substrates that can achieve both high SERS intensity and signal uniformity. Machine learning (ML) tools are ideal for addressing complex optimization problems, but their application in SERS substrate fabrication remains largely unexplored. We present a pioneering example of applying ML to optimize SERS substrate fabrication using silver-coated gold nanostars for chemical sensing. In this study, we used multiobjective Bayesian optimization to achieve both high SERS intensity and signal uniformity across the SERS substrate. Optimal fabrication parameters were identified for spin and flow coating of silver-coated gold nanostars. The optimized spin coated substrate achieved an experimental limit of detection of 100 nM with an application relevant compound (ammonium nitrate), suggesting its potential for trace-level sensing. This work underscores the potential for ML to accelerate the development of field-deployable SERS technologies for trace analyte detection. Furthermore, we provide a general framework for optimized fabrication of nanoparticles on surfaces for other applications such as catalysis, electronics, and medicine. Fabrication ; Bayesian optimization ; Nanotechnology ; Materials science ; Computer science ; Bayesian probability ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Vikas Tiwari | Tarak Karmakar
Source: Nano Letters (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Atomistic Details of Nanocluster Formation from Machine-Learned-Potential-Based Simulations Understanding the mechanism for the formation of metal nanoclusters is an open challenge in nanoscience. Computational modeling can provide molecular details of nanocluster formation that are otherwise inaccessible. However, simulating nanocluster nucleation in solution presents significant challenges, including inaccurate energy predictions and limitations on the system size and time scale. This work addresses these challenges by combining deep neural networks (DNNs) with well-tempered metadynamics (WT-MetaD) to model the nucleation of a prototypical nanocluster, Ag<sub>6</sub>(SCNH<sub>2</sub>)<sub>6</sub> in methanol. A neural-network-potential-based unbiased molecular dynamics simulation captured the cluster's dynamic behavior, while WT-MetaD simulations revealed an almost barrierless transition from dispersed precursors to a nucleated state. The method's robustness was further demonstrated by scaling up to 30 randomly distributed precursors, which resulted in spontaneous nucleation. This study presents the first successful DNN model of nanocluster formation in solution with density-functional-theory-level accuracy, paving the way for advancements in the field. Nanotechnology ; Nanoclusters ; Materials science ; Computer science Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science
Authors: Khalid Ferji
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

DetectNano: deep learning detection in TEM images for high-throughput nanostructure characterization The rapid and unbiased characterization of self-assembled polymeric vesicles in transmission electron microscopy (TEM) images remains a challenge in polymer science. Here, we present a deep learning-powered detection framework based on YOLOv8, enhanced with Weighted Box Fusion, to automate the identification and size estimation of polymer nanostructures. By incorporating multiple morphologies in the training dataset, we achieve robust detection across unseen TEM images. Our results demonstrate that the model provides accurate vesicle detection within 2 seconds-an efficiency unattainable using traditional image analysis software. The proposed framework enables reproducible and scalable nano-object characterization, paving the way for a general AI-driven automation in polymer self-assembly research. Nanostructure ; Characterization (materials science) ; Throughput ; Materials science ; Nanotechnology ; Deep learning ; Polymer ; Artificial intelligence ; Computer science ; Composite material ; Telecommunications Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Paul Fuchs | Michał Sanocki | Julija Zavadlav
Source: npj Computational Materials (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Learning non-local molecular interactions via equivariant local representations and charge equilibration Graph Neural Network (GNN) potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs. Message-passing GNNs model interactions beyond their immediate neighborhood by propagating local information between neighboring particles while remaining effectively local. However, locality precludes modeling long-range effects critical to many real-world systems, such as charge transfer, electrostatic interactions, and dispersion effects. In this work, we propose the Charge Equilibration Layer for Long-range Interactions (CELLI) to address the challenge of efficiently modeling non-local interactions. This novel architecture generalizes the classical charge equilibration (Qeq) method to a model-agnostic building block for modern equivariant GNN potentials. Therefore, CELLI extends the capability of GNNs to model long-range interactions while providing high interpretability through explicitly modeled charges. On benchmark systems, CELLI achieves state-of-the-art results for strictly local models. CELLI generalizes to diverse datasets and large structures while providing high computational efficiency and robust predictions. Interpretability ; Locality ; Charge (physics) ; Equivariant map ; Block (permutation group theory) ; Representation (politics) ; Graph ; Benchmark (surveying) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Xiaoyu Huang | Sebastian Volz | Masahiro Nomura | Yuxiang Ni
Source: Physical review. B./Physical review. B (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Machine learning-guided broadband phonon blockade via Anderson localization in engineered Si/Ge nanowires The localization of thermal phonons is a well-represented phenomenon in disordered atomic systems. Traditional disorder entities, such as random defects and aperiodicity, impede mid- and high-frequency phonons more than low-frequency modes, thus increasing the relative contributions of low-frequency phonon to thermal conductivity. Recognizing the constraints that this drawback imposes on the design of thermal functional materials, our research proposes a disordered structure for Si/Ge nanowires utilizing a checkerboard pattern. By combining molecular dynamics simulations with machine learning techniques, we identified an optimal configuration that minimizes thermal conductivity. This structure incorporates a distinctive design that achieves both strong phonon Anderson localization and enhanced interface scattering. This dual effect significantly hinders phonon transport across the broadband frequency spectrum, reducing the contribution of low-frequency phonons to thermal conductivity to a mere 9%. Our study deepens insights into phonon behavior in disordered structures and introduces a practical method for precise thermal conductivity control. Nanowire ; Broadband ; Phonon ; Blockade ; Materials science ; Optoelectronics ; Computer science ; Condensed matter physics ; Physics ; Medicine ; Telecommunications ; Internal medicine ; Receptor Thermal properties of materials ; Force Microscopy Techniques and Applications ; Advancements in Semiconductor Devices and Circuit Design
Authors: Ubonrat Siripatrawan | Yoshio Makino
Source: Food Research International (article, 2025-04-18)
Field: Chemistry — Analytical Chemistry

Rapid and chemical-free technique based on hyperspectral imaging combined with artificial intelligence for monitoring quality and shelf life of dried shrimp Hyperspectral imaging ; Shelf life ; Shrimp ; Fishery ; Environmental science ; Artificial intelligence ; Food science ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: D. Singh
Source: Journal of Food Protection (review, 2025-09-16)
Field: Chemistry — Analytical Chemistry

Harnessing Artificial Intelligence to Safeguard Food Quality and Safety Artificial Intelligence (AI) is reforming the food industry, particularly in food safety and quality control, by enhancing detection, predicting shelf life, and optimizing production processes. This review explores the innovative role of AI, focusing on the integration of machine learning (ML), computer vision, and natural language processing (NLP) in food safety systems. AI is transforming food safety by enabling real-time monitoring, predictive analytics, rapid contaminant detection, and automation throughout the food supply chain. These technologies reduce human error and allow quicker responses to safety threats, ultimately preventing foodborne illnesses and improving product quality. AI also helps to predict and manage climate-induced risks, such as chemical and microbiological hazards linked to extreme weather and temperature shifts. The review outlines the integration of digital tools such as biosensors and Internet of Things (IoT) devices and examines AI's convergence with blockchain and process analytical technologies to enhance traceability and strengthen food safety management systems. Despite its potential, the widespread adoption of AI is hindered by challenges such as data privacy concerns, workforce adaptation, and regulatory barriers, while critical gaps in digital infrastructure, data standardization, and policy support also need to be addressed to enable effective implementation. The review highlights the importance of ethical frameworks and interdisciplinary collaboration to guide responsible AI deployment. Emerging tools like neural networks and behavior-based safety assessments can boost food system resilience. The review concludes by calling for enhanced regulatory cooperation and technological investment to realize AI's full potential in creating safer, more sustainable, and efficient food systems. Food safety ; Traceability ; Process safety ; Food processing ; Quality (philosophy) ; Automation ; Process (computing) ; Product (mathematics) ; Safeguard Spectroscopy and Chemometric Analyses
Authors: Florian Pabst | Stefano Baroni
Source: Physical review. E (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Glassy dynamics in a glass-forming liquid: A first-principles study of toluene The microscopic understanding of the dramatic increase in viscosity of liquids when cooled toward the glass transition is a major unresolved issue in condensed matter physics. Here, we use machine learning methods to accelerate molecular dynamics simulations with first-principles accuracy for the glass-former toluene. We show that the increase in viscosity is intimately linked to the increasing number of dynamically correlated molecules N^{*}. While certain hallmark features of glassy dynamics, like physical aging, are linked to N^{*} as well, others, like relaxation stretching, are not. Toluene ; Dynamics (music) ; Materials science ; Chemistry ; Organic chemistry ; Psychology Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Chemical Thermodynamics and Molecular Structure
Authors: Jian Liu | Peifeng Xiong | Changjiao Li | Hua Hao | Hanxing Liu
Source: ACS Sustainable Chemistry & Engineering (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Accelerated Research of Energy Storage Properties of BaTiO<sub>3</sub>–BiMeO<sub>3</sub> Ceramics The exploration of dielectric materials with excellent energy storage properties has always been a research focus in the field of materials science. The development of a technical method that can accurately predict the energy storage characteristics of ceramics will significantly accelerate the pace of research into energy storage materials. In this research, a machine learning method was utilized with the aim of accurately predicting the energy storage density (Wrec) and energy storage efficiency (η) of BaTiO3–BiMeO3 (BT-BMO) ferroelectric ceramics. Initially, a data set was established on the energy storage properties of BT-BMO bulk ceramics by consulting relevant published literature. Three distinct feature vector spaces were constructed based on the physicochemical characteristics of constituent elements, related property information, and sintering process parameters. After that, multiple machine learning algorithm models were built to train and predict Wrec and η. For the prediction of energy storage density, the 10-fold cross-validation coefficient of determination (r2) and root-mean-squared error (RMSE) of the GBR model are 0.974 and 0.142, respectively. For the prediction of energy storage efficiency, the 10-fold cross-validation r2 and RMSE of the LGBM model are 0.894 and 0.068, respectively. To enhance the interpretability of the models, the Shapley additive explanation method was introduced, revealing and briefly analyzing the important features that influence the target performance. Finally, the material system of xBaTiO3-(1 – x)Bi(Zn2/3Ta1/3)O3, which was not included in the data set, was synthesized experimentally and tested. The experimental results were found to be close to the model’s predictions, thereby validating the effectiveness of the method. This study provides a new approach that could accelerate the development of dielectric materials with excellent energy storage properties. Ceramic ; Materials science ; Energy storage ; Energy (signal processing) ; Nanotechnology ; Chemical engineering ; Engineering physics ; Process engineering ; Mineralogy ; Chemistry ; Composite material ; Physics ; Engineering ; Thermodynamics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Yuchen Wang | D. Sulalith N. D. Samarasinghe | Hao Deng | Bin Liu | Christine M. Aikens
Source: Journal of Chemical Information and Modeling (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Gaussian Process Approach to Constructing Transferable Force Fields for Thiolate-Protected Gold Nanoclusters Gold nanoparticles can exhibit unique physical and chemical properties, such as plasmon resonances or photoluminescence. These nanoparticles have many atoms, which leads to high computational costs for density functional theory (DFT) calculations. In this work, we used the FLARE++ (fast learning of atomistic rare events) code and incorporated an active learning algorithm to construct force fields for gold thiolate-protected nanoclusters. We started training the force field using Au<sub>20</sub>(SCH<sub>3</sub>)<sub>16</sub> as the initial structure and then applied the trained force field to perform molecular dynamics (MD) simulations. We then validated the machine learning force field using different types of gold nanoclusters as testing models. The test results were integrated into the existing database and retrained again. The final force fields show success in predicting energies for nanoclusters not only in the training database but also outside the database. These tests revealed that the force field has achieved quantum mechanical level accuracy in some key performance metrics. Nanoclusters ; Gaussian ; Force field (fiction) ; Gaussian process ; Process (computing) ; Nanotechnology ; Chemical physics ; Statistical physics ; Materials science ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Mathematical Biology Tumor Growth
Authors: Qinglang Zhou | Xiangyu Ma | Peng Lin | Xiaodong Xing | Shuyong Jiang | Xinxin Bai | Zhongyu Zhou | Yanqiu Zhang | Yanqing Zhang
Source: Journal of Alloys and Compounds (article, 2025-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction for magnetic properties of sintered NdFeB magnets based on machine learning Neodymium magnet ; Magnet ; Materials science ; Metallurgy ; Mechanical engineering ; Engineering Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Powder Metallurgy Techniques and Materials
Authors: Jussara V. Roque | Wilson Cardoso | Deborah V. A. de Aguiar | Gabriel Franco dos Santos | Alexandre de O. Gomes | Íris Medeiros | Gesiane da Silva Lima | Boniek G. Vaz
Source: Analytical Chemistry (article, 2025-02-26)
Field: Chemistry — Analytical Chemistry

Integrating FT-ICR MS and Machine Learning to Forecast Acid Content Across Boiling Cuts In this study, we introduce a pioneering approach that leverages advanced machine learning and ultrahigh-resolution Fourier transform ion cyclotron mass spectrometry (FT-ICR MS) data to predict the distribution of the total acid number (TAN) in true boiling point (TBP) distillation cuts from crude oil. By employing partial least-squares (PLS) regression and ordered predictor selection (OPS), we achieved robust predictive models with high accuracy, evidenced by low root-mean-square error of calibration (RMSEC) and strong correlation coefficients (Rc). Our analysis of 36 diverse crude oil samples revealed significant variations in chemical composition, with nitrogen- and oxygen-containing compounds playing key roles in influencing TAN values. Through the use of volcano plots, we identified critical molecular classes that drive changes in TAN. The predictive models demonstrated remarkable consistency between predicted and actual TAN values, particularly in samples with a higher TAN, further validating their reliability. Significantly, our method overcomes the limitations of traditional ASTM testing by requiring smaller sample volumes while still providing accurate TAN predictions. This novel approach offers a powerful new tool for the molecular characterization and behavioral forecasting of complex mixtures, enabling a more efficient pathway for sample analysis when resources are limited. Chemistry ; Partial least squares regression ; Calibration ; Boiling point ; Distillation ; Analytical Chemistry (journal) ; Mean squared error ; Mass spectrometry ; Sample (material) ; Biological system ; Statistics ; Chromatography ; Mathematics Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Hydrocarbon exploration and reservoir analysis
Authors: Daisuke Kimura | Naoko Tajima | Toshiya Okazaki | Sho Muroga
Source: Carbon (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Explainable Multimodal Machine Learning for revealing structure-property relationships in carbon nanotube fibers Property (philosophy) ; Materials science ; Carbon nanotube ; Composite material ; Nanotube ; Nanotechnology ; Computer science ; Philosophy Carbon Nanotubes in Composites ; Machine Learning in Materials Science