jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tianliang Li | Bin Cao | Tianhao Su | Lixing Lin | Dong Wang | Xinting Liu | Haoyu Wan | Haiwei Ji | Zixuan He | Yingying Chen | Lingyan Feng | Tong‐Yi Zhang
Source: Small (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Machine Learning‐Engineered Nanozyme System for Synergistic Anti‐Tumor Ferroptosis/Apoptosis Therapy Nanozymes with multienzyme-like activity have sparked significant interest in anti-tumor therapy via responding to the tumor microenvironment (TME). However, the consequent induction of protective autophagy substantially compromises the therapeutic efficacy. Here, a targeted nanozyme system (Fe-Arg-CDs@ZIF-8/HAD, FZH) is shown, which enhances synergistic anti-tumor ferroptosis/apoptosis therapy by leveraging machine learning (ML). A novel ML model, termed the sequential backward Tree-Classifier for Gaussian Process Regression (TCGPR), is proposed to improve data pattern recognition following the divide-and-conquer principle. Based on this, a Bayesian optimization algorithm is employed to select candidates from the extensive search space. Leveraging this fresh material discovery framework, a novel strategy for enhancing nanozyme-based tumor therapy, has been developed. The results reveal that FZH effectively exerts anti-tumor effects by sequentially responding to the TME, having a cascade reaction to induce ferroptosis. Moreover, the endogenous elevation of high concentration nitric oxide (NO) serves as a direct mechanism for killing tumor cells while concurrently suppressing the protective autophagy induced by oxidative stress (OS), enhancing synergistic ferroptosis/apoptosis therapy. Overall, a novel strategy for improving nanozyme-based tumor therapy has been proposed, underlying the integration of ML, experiments, and biological applications. Apoptosis ; Autophagy ; Tumor microenvironment ; Cancer research ; Combination therapy ; Programmed cell death ; Computer science ; Chemistry ; Machine learning ; Tumor cells ; Biology ; Bioinformatics ; Biochemistry Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Wise Herowati | Wahyu Aji Eko Prabowo | Muhamad Akrom | Noor Ageng Setiyanto | Achmad Wahid Kurniawan | Novianto Nur Hidayat | T. Sutojo | Supriadi Rustad
Source: Theoretical Chemistry Accounts (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Machine learning for pyrimidine corrosion inhibitor small dataset Random forest ; Boosting (machine learning) ; Machine learning ; Context (archaeology) ; Pyrimidine ; Computer science ; Artificial intelligence ; Virtual screening ; Predictive value ; Support vector machine ; Nonlinear system ; Data mining ; Chemistry ; Drug discovery ; Physics ; Medicine Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Hyeongjun Lim | Sojung Kim
Source: Energies (article, 2024-10-18)
Field: Chemistry — Analytical Chemistry

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks. Raw material ; Yield (engineering) ; Production (economics) ; Ethanol fuel ; Estimation ; Process engineering ; Computer science ; Biochemical engineering ; Engineering ; Biofuel ; Chemistry ; Waste management ; Economics ; Materials science ; Systems engineering Spectroscopy and Chemometric Analyses ; Atmospheric and Environmental Gas Dynamics ; Hydrocarbon exploration and reservoir analysis
Authors: Shiv R. Desai | Rinkal B. Bhoraniya | Mahesh Koladiya | Vidhi V. Bhopekar | Chintan Patel | Tadashi Mori | Sachin G. Modha
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2024-05-08)
Field: Chemistry — Spectroscopy

Solvatochromism and halochromism in nitro lophine derivatives: Photophysical study, computational calculations and applications as pH sensing material Solvatochromism ; Hypsochromic shift ; Chemistry ; Bathochromic shift ; Absorbance ; Nitro ; Naked eye ; Methanol ; Dissociation (chemistry) ; Photochemistry ; Organic chemistry ; Fluorescence ; Molecule ; Chromatography ; Detection limit Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Photochromic and Fluorescence Chemistry
Authors: Yesudhasan Thooyavan | L. A. Kumaraswamidhas | R. Edwin Raj | Joseph Selvi Binoj | Bright Brailson Mansingh | A. Sagai Francis Britto | Alamry Ali
Source: Journal of Bionic Engineering (article, 2024-02-27)
Field: Materials Science — Polymers and Plastics

Modelling and Characterization of Basalt/Vinyl Ester/SiC Micro- and Nano-hybrid Biocomposites Properties Using Novel ANN–GA Approach Materials science ; Composite material ; Filler (materials) ; Basalt fiber ; Composite number ; Silicon carbide ; Ultimate tensile strength ; Flexural strength ; Tribology ; Vinyl ester ; Nano- ; Fiber ; Resilience (materials science) ; Polymer Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Electrospun Nanofibers in Biomedical Applications
Authors: Fan Zhang | Ján Ilavský
Source: IUCrJ (review, 2024-08-01)
Field: Materials Science — Materials Chemistry

Bridging length scales in hard materials with ultra-small angle X-ray scattering – a critical review Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments. Characterization (materials science) ; Nanotechnology ; Bridging (networking) ; Materials science ; Small-angle X-ray scattering ; Computer science ; Engineering physics ; Scattering ; Systems engineering ; Engineering ; Physics X-ray Diffraction in Crystallography ; High-pressure geophysics and materials ; Advanced materials and composites
Authors: Setareh Medghalchi | Joscha Kortmann | Sang-Hyeok Lee | Ehsan Karimi | U. Kerzel | Sandra Korte‐Kerzel
Source: Materials & Design (article, 2024-05-18)
Field: Materials Science — Metals and Alloys

Automated segmentation of large image datasets using artificial intelligence for microstructure characterisation and damage analysis Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using scanning electron microscopy images collected from large areas (∼1 mm2) of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure. Microstructure ; Materials science ; Artificial intelligence ; Segmentation ; Artificial neural network ; Thresholding ; Martensite ; Pattern recognition (psychology) ; Computer science ; Image (mathematics) ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques
Authors: Yuelin Wang | Chengquan Zhong | Jingzi Zhang | Honghao Yao | Junjie Chen | Xi Lin
Source: Materials & Design (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction • We update the benchmark dataset by extracting more than 230 new TE materials from the latest literature. • Stacking model achieves a state-of-the-art performance with a R 2 value of 0.970, demonstrating high sensitivity to the trend in zT value variations in doping levels. • 43 potential high-zT materials are discovered, e.g. Pb 0.97 K 0.03 Te 0.65 S 0.25 Se 0.1 exhibited a zT value of 1.9. Thermoelectric materials, which convert thermal energy directly into electricity, hold promise for sustainable energy applications. However, accurately predicting their efficiency, quantified by the figure of merit ( zT ), remains challenging, especially for doped materials. Here we present a machine learning (ML) approach, the stacking model, that significantly improves zT prediction accuracy for doped thermoelectric. By combining five regression models through stacking ensemble learning and introducing 100 coordination number features alongside conventional features, our model achieves a coefficient of determination ( R 2 ) value of 0.970. This high performance demonstrates unprecedented sensitivity to zT variations due to doping. We validate our model using an expanded dataset of over 230 new materials from recent literature. The model identifies 43 potential high- zT materials, including Pb 0.97 K 0.03 Te 0.65 S 0.25 Se 0.1 with a predicted zT of 1.9. Density functional theory calculations confirm the superior electrical properties of this compound. Our approach offers an efficient strategy for large-scale screening of high-performance thermoelectric materials, potentially accelerating their discovery and development for energy applications. Materials science ; Stacking ; Figure of merit ; Thermoelectric effect ; Ensemble learning ; Engineering physics ; Machine learning ; Artificial intelligence ; Optoelectronics ; Computer science ; Thermodynamics ; Engineering ; Nuclear magnetic resonance Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Lewis Y. Geer | Stephen E. Stein | W. Gary Mallard | Douglas J. Slotta
Source: Journal of Chemical Information and Modeling (article, 2024-01-17)
Field: Chemistry — Spectroscopy

AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence The Kováts retention index (RI) is a quantity measured using gas chromatography and is commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network for predicting RI values from structure for standard semipolar columns. This network generated predictions with a mean absolute error of 15.1 and, in a quantification of the tail of the error distribution, a 95th percentile absolute error of 46.5. Because of the Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was used to predict RI values for the NIST EI-MS spectral libraries. These RI values are used to improve chemical identification methods and the quality of the library. Estimating uncertainty is an important practical need when using prediction models. To quantify the uncertainty of our network for each individual prediction, we used the outputs of an ensemble of 8 networks to calculate a predicted standard deviation for each RI value prediction. This predicted standard deviation was corrected to follow the error between the observed and predicted RI values. The <i>Z</i> scores using these predicted standard deviations had a standard deviation of 1.52 and a 95th percentile absolute <i>Z</i> score corresponding to a mean RI value of 42.6. Standard deviation ; Percentile ; Statistics ; NIST ; Artificial neural network ; Absolute deviation ; Mean absolute error ; Standard error ; Mathematics ; Mean squared error ; Artificial intelligence ; Computer science ; Speech recognition Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Cesare Malosso | Natalia Manko | Maria Grazia Izzo | Stefano Baroni | Ali Hassanali
Source: Proceedings of the National Academy of Sciences (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Evidence of ferroelectric features in low-density supercooled water from ab initio deep neural-network simulations Over the last decade, an increasing body of evidence has emerged, supporting the existence of a metastable liquid-liquid critical point in supercooled water whereby two distinct liquid phases of different densities coexist. Analyzing long molecular dynamics simulations performed using deep neural-network force fields trained to accurate quantum mechanical data, we demonstrate that the low-density liquid phase displays a strong propensity toward spontaneous polarization, as witnessed by large and long-lived collective dipole fluctuations. Our findings suggest that the dynamical stability of the low-density phase, and hence the transition from high-density to low-density liquid, is triggered by a collective process involving an accumulation of rotational angular jumps, which could ignite large dipole fluctuations. This dynamical transition involves subtle changes in the electronic polarizability of water molecules which affects their rotational mobility within the two phases. These findings hold the potential for catalyzing activity in the search for dielectric-based probes of the putative second critical point. Supercooling ; Polarizability ; Metastability ; Dipole ; Condensed matter physics ; Phase transition ; Critical point (mathematics) ; Dielectric ; Chemical physics ; Ab initio ; Materials science ; Physics ; Molecule ; Thermodynamics ; Quantum mechanics Material Dynamics and Properties ; Theoretical and Computational Physics ; Spectroscopy and Quantum Chemical Studies
Authors: Haoqi Zhu | Zhongyi Wang | Luofei Li | Liang Dong | Ying Li | Bin Xue | Yi Cao
Source: Supramolecular Materials (article, 2024-12-21)
Field: Materials Science — Materials Chemistry

Revolutionizing supramolecular materials design with artificial intelligence The design and development of supramolecular materials are hindered by complex non-covalent interactions and a lack of comprehensive rational design theories. Traditional "trial-and-error" methods are inefficient and labor-intensive, slowing progress in creating materials with precise tunability, robust stability, multifunctionality, and dynamic behavior. This perspective highlights major difficulties in supramolecular materials research and the transformative potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing the field. Key challenges in applying AI include limited data availability, data quality issues, and the path-dependent nature of assembly processes. To overcome data scarcity, we discuss strategies such as transfer learning, data augmentation, and federated learning to enhance model performance with small datasets. We propose developing Intelligent Data Manufacturing Platforms—advanced laboratory automation systems designed to generate large volumes of high-quality data. By integrating AI algorithms with robotics in a closed-loop experimental system, these platforms enable high-throughput experimentation, autonomous decision-making, and iterative refinement of AI models through continuous data acquisition. This accelerates the design-build-test-learn cycle, fostering innovation and facilitating the development of next-generation supramolecular materials. By establishing standardized data repositories and encouraging global collaboration, this framework propels the field toward a data-intensive paradigm. Computer science ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Calcium Carbonate Crystallization and Inhibition
Authors: Zhiyun Ye | Ke Li | Guoliang Chen | Yongchun Zou | Shuqi Wang | Zhao Wang | Shuang Yu | Yaming Wang | Dechang Jia | Yu Zhou
Source: Corrosion Science (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

Accelerated design and fabrication of thermal protection coating via high-throughput experiments and machine learning Corrosion ; Fabrication ; Throughput ; Coating ; Materials science ; Thermal ; Nanotechnology ; Computer science ; Composite material ; Operating system ; Physics Machine Learning in Materials Science ; Semiconductor materials and devices ; Thermal properties of materials
Authors: Zhenlin Zhang | Xilin Yin | Wei Hu | Jinlong Yang
Source: Journal of Chemical Theory and Computation (article, 2024-02-16)
Field: Chemistry — Spectroscopy

Machine Learning K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Accurate and Efficient Cubic-Scaling Exact Exchange Plus Random Phase Approximation within Plane Waves The exact-exchange plus random-phase approximation (EXX+RPA) method has emerged as a crucial tool for precisely characterizing electronic structures in molecular and solid systems. We present an accurate and efficient implementation of EXX+RPA calculations that scale cubically and are conducted within plane waves. Our approach incorporates the interpolative separable density fitting (ISDF) algorithm, effectively mitigating the computational challenges associated with the plane wave basis set. To overcome the constraints of the conventional ISDF algorithm, characterized by the exceptionally high prefactor in QR factorization for interpolation point selection, we introduce an enhanced machine learning K-means method. This method incorporates a novel empirical weight function called "SSM+" for more precise interpolation point selection, capturing physical information more accurately across diverse systems. Our machine learning approach offers a quasiquadratic scaling alternative, effectively replacing the computationally demanding cubic-scaling QRCP algorithm in plane-wave-based EXX+RPA calculations. Furthermore, we enhance the method's capabilities by optimizing GPU acceleration using MATLAB's integrated GPU toolkit. In particular, our approach reduces the computational scaling of χ<sup>0</sup> from 3.80 to 2.13 and the overall computational scaling of EXX from 2.74 to 2.10. We achieve a remarkable GPU acceleration speedup of up to 35×. Regarding CPU computation time, the standard quartic-scaling method requires 22 h to compute Si<sub>128</sub>, while QRCP completes the calculation in only around 1 h, achieving a speedup up to 20×. However, the utilization of the K-means algorithm reduces the time to 800 s, a substantial improvement of 100× compared to the standard algorithm. By employing the K-means algorithm, the computational time for interpolative point calculation using QRCP decreases from 1 h to 1 min, resulting in a 55× speed increase. With this improved algorithm, we successfully computed the dissociation curve of H<sub>2</sub> and the equilibrium polyynic geometry of C<sub>18</sub> molecules. Scaling ; Cluster analysis ; Phase (matter) ; Separable space ; Plane (geometry) ; Plane wave ; Algorithm ; k-means clustering ; Computer science ; Statistical physics ; Mathematics ; Physics ; Machine learning ; Optics ; Mathematical analysis ; Geometry ; Quantum mechanics Spectroscopy and Laser Applications ; Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials
Authors: Mehrnoush Alizade | Rushabh Kheni | Stephen Price | Bryer C. Sousa | Danielle L. Cote | Rodica Neamtu
Source: Integrating materials and manufacturing innovation (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

A Comparative Study of Clustering Methods for Nanoindentation Mapping Data Nanoindentation ; Cluster analysis ; Computer science ; Data mining ; Materials science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Mineral Processing and Grinding ; Analytical chemistry methods development