jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Neda Kalantari | Ali Farzi | Faez Hamooni | Nagihan Çaylak Delibaş | Ali Tarjomannejad | Aligholi Niaei | Dariush Salari
Source: Journal of Sol-Gel Science and Technology (article, 2024-06-21)
Field: Chemistry — Inorganic Chemistry

Simultaneous study of different combinations of ZSM-5 templates and operating conditions in the MTP process; designing, modeling, and optimization by RSM-ANN-GA Response surface methodology ; Ammonium bromide ; Methanol ; Materials science ; Selectivity ; Artificial neural network ; Bromide ; Central composite design ; Catalysis ; Biological system ; Optimal design ; Computer science ; Chemical engineering ; Chemistry ; Machine learning ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: R. Dineshkumar | V Sunil Kumar | R Archana Reddy | Ammar Hameed Shnain | Sachinkumar
Source:  (article, 2024-05-17)
Field: Chemistry — Spectroscopy

Multi-objective de novo Drug Design using Bidirectional Recurrent Neural Networks and Nondominated Sorting Drug Design (DD) is a significant field for pharmaceutical businesses study. Deep Learning (DL) approaches are evolving as an auspicious solution to employ de novo drug design, such as to develop drug-like molecules personalized to particular requirements. In DD, the design as well as production of safe and effective compounds is a long-term, complex, as well as difficult process. Hence, this research proposes the Bidirectional Recurrent Neural Network (BiRNN) for the design of multi-objective de novo drug. Initially, the CheMBL and KIBA datasets are utilized to estimate the effectiveness of the proposed method. Then, the data are proprocessed by utilizing Fragment similarity calculations to design various ligands sets and particular target collections. The proposed BiRNN attains the accuracy of 0.9742, and 0.0632 on CheMBL dataset, as well as Mean Square Error (MSE) of 0.213 and 0.024 on KIBA dataset respectively. These obtained results are better and effective when compared to the existing methods like Transformer-Decoder Generator, A2C Reinforcement Learning (RL) and Recurrent Neural Network (RNN). Sorting ; Computer science ; Artificial neural network ; Recurrent neural network ; Artificial intelligence ; Drug ; Machine learning ; Algorithm ; Biology ; Pharmacology Analytical Chemistry and Chromatography ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Li Zhao | Shulai Lu | Ming Chen | Yuchao Wang | Shicheng Zhao
Source: Journal of Polymer Science (article, 2024-03-15)
Field: Chemistry — Spectroscopy

Artificial neural network prediction of particle size of agglomerated polybutadiene latex Abstract Polymer agglomeration is a useful way to enlarge the particle size of polybutadiene latex (PBL). However, the agglomeration process is very complex and is affected by many factors. Therefore, it is difficult to predict the mean particle size and distribution of agglomerated PBL. This study proposed a novel simulation method based on the artificial neural network (ANN) model. Firstly, the influence of agglomeration conditions on the particle size of agglomerated PBL were investigated, including the particle size of pre‐agglomerate PBL, the amount, the α‐methacrylic acid (α‐MAA) content, the solid content and the particle size of agglomerating agent. It is found that these factors have a close nonlinear relationship with the mean particle size and polydispersion index (PDI) of agglomerated PBL. Then, a model containing 236 data points was built using ANN including these properties as input with mean particle size and PDI of agglomerated PBL as output. The coefficient of determination ( R 2 ) values predicted by ANN for training, validation, test and total data are 0.9879, 0.9964, 0.9930, and 0.9902, respectively. It indicates that there is a good agreement between experimental data and predicted results by ANN. This study demonstrates that a simple machine learning method has good performance in predicting the particle size of agglomerated PBL, which further improves the development efficiency of PBL polymer agglomeration. Polybutadiene ; Artificial neural network ; Particle size ; Polymer science ; Materials science ; Particle (ecology) ; Chemical engineering ; Composite material ; Artificial intelligence ; Computer science ; Engineering ; Polymer ; Geology ; Copolymer Adsorption, diffusion, and thermodynamic properties of materials ; Asphalt Pavement Performance Evaluation ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Lirong Zheng | Banghao Wu | Bozitao Zhong | Runye Huang | Shudong Jiang | Mingchen Li | Pan Tan | Liang Hong
Source: Research Square (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

Harnessing Protein Language Model for Structure-Based Discovery of Highly Efficient and Robust PET Hydrolases Computer science ; Computational biology ; Chemistry ; Biology Enzyme Structure and Function ; Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics
Authors: David M. Vahey | Manting Mu | Shannon A. Bonke | Timo Sommer | Prithvi Vangal | Carl J. Mallia | Max García‐Melchor | Erwin Reisner
Source: ChemRxiv (preprint, 2024-11-12)
Field: Chemistry — Organic Chemistry

Anti-Friedel-Crafts alkylation via electron donor-acceptor photoinitiated autocatalysis The ubiquity of C–H bonds in organic molecules makes C–H functionalisation one of the most valuable synthetic transformations in organic chemistry. It provides a sustainable synthetic strategy, optimising atom and step economy by eliminating the need for functional handles, instead relying on reagent control or the substrate’s innate selectivity. C–H alkylation of electron-poor aromatic species remains a particular challenge, typically relying on functional handles to direct selectivity with either traditional palladium cross-coupling or nickel photoredox methodologies. Here we introduce a highly selective, scalable, and cost effective synthetic strategy for C–H alkylation of electron-poor aromatics, exhibiting high functional group tolerance applicable to pharmaceutical compounds. This reaction leverages an autocatalytic, electron donor-acceptor triggered, radical reaction, as confirmed by both experimental and computational analyses. Notably, it demonstrates ‘anti-Friedel-Crafts’ selectivity, consistent with theoretical predictions from Fukui indices and machine learning models on previously ‘unseen’ substrates. Friedel–Crafts reaction ; Autocatalysis ; Alkylation ; Electron acceptor ; Friedel oscillations ; Chemistry ; Electron ; Acceptor ; Photochemistry ; Organic chemistry ; Catalysis ; Physics ; Impurity ; Condensed matter physics ; Quantum mechanics Radical Photochemical Reactions ; Oxidative Organic Chemistry Reactions ; Chemical Synthesis and Reactions
Authors: Kwang Seon Shin | A. Drobyshev | N. Redko | Kantemir A. Kunizhev | A. A. Komissarov | В. Е. Баженов | Zaira Gurganchova | Andrey Miterev | Yaroslav Skakunov | Д. А. Лежнев | Oleg O. Yanushevich
Source: Metals (article, 2024-12-10)
Field: Materials Science — Biomaterials

A New Method for Assessing the Rate of Biodegradation of Magnesium Alloy-Based Products in an In Vivo Experiment Magnesium alloys have emerged as a promising materials for temporary implants in medical applications due to their favorable biomechanical properties and biocompatibility. Their natural degradation within the body minimizes the need for surgical removal, making them particularly attractive for use in orthopedics, dentistry, and veterinary medicine. However, the formation of gas cavities during degradation poses significant challenges, adversely affecting mechanical stability and healing outcomes. This study aims to develop a quantitative method for assessing gas cavity volumes around magnesium alloy screws, comparing it with existing imaging techniques, including computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound diagnostics. A systematic review of research articles identified CT as the most effective method, offering high accuracy and detailed three-dimensional imaging capabilities, though it has limitations such as cost and radiation exposure. Our experimental work involved implanting magnesium screws in a rat model to analyze gas cavity formation. Our results indicated that Mg-2 wt.%Zn-2 wt.%Ga screws resulted in the moderate formation of a gas cavity. The findings underscore the importance of selecting appropriate measurement techniques for evaluating gas cavity dynamics, which are critical for optimizing clinical outcomes and improving patient care. Future research should explore integrating advanced technologies such as machine learning to enhance image analysis and visualization efficiency in clinical settings. Biodegradation ; In vivo ; Magnesium ; Alloy ; Materials science ; Metallurgy ; Chemistry ; Biotechnology ; Biology ; Organic chemistry Magnesium Alloys: Properties and Applications ; Magnesium Oxide Properties and Applications ; Phosphorus and nutrient management
Authors: Kevin M. Roccapriore | Maxim Ziatdinov | Andrew R. Lupini | Abhay Pratap Singh | Usha Philipose | Sergei V. Kalinin
Source: Journal of Applied Physics (article, 2024-03-21)
Field: Materials Science — Surfaces, Coatings and Films

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader. Electron energy loss spectroscopy ; Mesoscopic physics ; Plasmon ; Visualization ; Invariant (physics) ; Spectroscopy ; Nanowire ; Computer science ; Scanning transmission electron microscopy ; Computational physics ; Materials science ; Physics ; Artificial intelligence ; Optics ; Transmission electron microscopy ; Nanotechnology ; Condensed matter physics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: E. V. Lebedeva
Source: Priroda (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

The winners of the 2024 Nobel Prize in Chemistry – David Baker, Demis Hassabis and John Jumper The Nobel Prize in Chemistry 2024 was awarded to David Baker, Demis Hassabis and John Jumper for their significant contributions to the development of computational protein design and structure prediction based on amino acid sequence. One of David Baker’s major achievements is the development of new designer proteins using the Rosetta program. The AlphaFold neural network, created by Hassabis and Jumper, allows for highly accurate prediction of the three-dimensional structure of proteins based on their amino acid sequences. The potential of AlphaFold’s artificial intelligence and the possibilities discovered by the Nobel laureates are inexhaustible. AI will accelerate discoveries in the biological and medical sciences and help develop new treatments. Jumper ; Polymer science ; Chemistry ; Art history ; Engineering physics ; Classics ; Philosophy ; Art ; Engineering ; Electrical engineering History and advancements in chemistry
Authors: Edoardo Di Napoli | Xinzhe Wu | Thomas Bornhake | Piotr Kowalski
Source: Frontiers in Applied Mathematics and Statistics (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Computing formation enthalpies through an explainable machine learning method: the case of lanthanide orthophosphates solid solutions In the last decade, the use of AI in Condensed Matter physics has seen a steep increase in the number of problems tackled and methods employed. A number of distinct Machine Learning approaches have been employed in many different topics; from prediction of material properties to computation of Density Functional Theory potentials and inter-atomic force fields. In many cases, the result is a surrogate model which returns promising predictions but is opaque on the inner mechanisms of its success. On the other hand, the typical practitioner looks for answers that are explainable and provide a clear insight into the mechanisms governing a physical phenomena. In this study, we describe a proposal to use a sophisticated combination of traditional Machine Learning methods to obtain an explainable model that outputs an explicit functional formulation for the material property of interest. We demonstrate the effectiveness of our methodology in deriving a new highly accurate expression for the enthalpy of formation of solid solutions of lanthanide orthophosphates. Lanthanide ; Computer science ; Chemistry ; Process engineering ; Engineering ; Organic chemistry ; Ion Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Wataru Takahara | Ryuto Baba | Yosuke Harashima | Tomoaki Takayama | Shogo Takasuka | Yuichi Yamaguchi | Akihiko Kudo | Mikiya Fujii
Source: ChemRxiv (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Proposal of an Approach to Explore Photocatalysts by Two-stage Machine Learning Model In the field of data-driven material development, bias in a dataset often causes difficulties in building a regression model when machine learning methods are applied. One of inorganic functional materials facing such a difficulty is photocatalysts. In this study, we propose a two-stage machine learning model to predict the activity for hydrogen evolution (H2/µmol h-1) from an aqueous solution containing sacrificial reagents over metal-sulfide photocatalysts under visible light irradiation. This two-stage machine learning model consists of the following two parts: a first regression model that predicts the activity for sacrificial hydrogen evolution and a second classification model that determines the reliability of the values predicted by the first regression model. We also propose a search scheme for variables related to the experimental conditions based on the proposed two-stage machine learning model. The proposed two-stage machine learning model improves the prediction accuracy of the activity compared with the first regression model. Artificial intelligence ; Machine learning ; Computer science ; Regression ; Regression analysis ; Stage (stratigraphy) ; Reliability (semiconductor) ; Field (mathematics) ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Tomoyuki Okuda | Toshinori Yamauchi | San Kang | Y.O. Park | Kiwoong Lee | Ilyong Lee | Hyewon Park | Gwangseob Lim | Youjoung Jun | Yoonyoung Jun | Gyeongyun Shin
Source:  (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

High throughput CD-SEM metrology using image denoising based on deep learning Accompanying the microfabrication and the complexity of the semiconductor manufacturing process, measurement and inspection using a scanning electron microscope (SEM) have become increasingly important for semiconductor manufacturing. Therefore, we have introduced an image denoising algorithm based on supervised deep learning with measurements for model training that transform a low signal-to-noise ratio (S/N) SEM image into a high S/N one, thereby improving the measurement success rate and maintaining measurement precision. Our experimental results demonstrated its effectiveness by an algorithm for enhancing throughput. However, performance may degrade when dealing with images containing features not included in the training dataset because deep learning models generally rely on trained features. Therefore, we propose high throughput CD-SEM metrology using image denoising based on deep learning that include a technique to statistically monitor deviations from the training images during model operation. In this study, we mainly discuss about monitoring module. To verify effectiveness of our proposed monitoring module, we first acquired sets of normal images used for training a deep learning model and sets of deviated images in which the SEM imaging recipe was partially changed. Then, the distribution of statistical values for noise and brightness features in the normal image set was used as a reference to compare the deviated image sets by the proposed method. As a result, the detection rate of the deviated images achieves 100%, and the false detection rate achieve 0% by combining of multiple statistical value distributions. By detecting deviated images that may degrade measurement performance, it is possible to maintain measurement precision and operate high-throughput measurement by using a denoising model based on deep learning. Artificial intelligence ; Computer science ; Semiconductor device fabrication ; Deep learning ; Metrology ; Noise (video) ; Noise reduction ; Throughput ; Computer vision ; Pattern recognition (psychology) ; Brightness ; Image (mathematics) ; Materials science ; Mathematics ; Optics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection
Authors: Mehdi Maleki | Seyed Hamid Zahiri
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Chemistry — Spectroscopy

OWPCP: A Deep Learning Model to Predict Octanol-Water Partition Coefficient The physicochemical properties of chemical compounds have great importance in several areas, including pharmaceuticals, environmental and separation science. Among these are physicochemical properties such as the octanol-water partition coefficient, which has been considered an important index pointing out lipophilicity and hydrophilicity. It affects drug absorption and membrane permeability. Following Lipinski's rule of five, logP was identified as one of the key determinants of the stability of chemical entities and, as such, needed state-of-the-art methods for measuring lipophilicity. This paper presents a deep-learning model, OWPCP, developed to compute logP using Morgan fingerprints and MACCS keys as input features. It uses the interconnection of such molecular representations with logP values extracted from 26,254 compounds. The dataset was prepared to contain a wide range of chemical structures with differing molecular weights and polar surface area. Hyperparameter optimization was conducted using the Keras Tuner alongside the Hyperband algorithm to enhance the performance. OWPCP demonstrated outstanding performance compared to current computational methods, achieving an MAE=0.247 on the test set and outperforming all previous DL models. Remarkably, while one of the most accurate recent models is based on experimental data on retention time to make predictions, OWPCP manages computing logP efficiently without depending on these factors, being, therefore, very useful during early-stage drug discovery. Our model outperforms the best model, which leverages Retention Time, and our model does not require any experimental data. Further validation of the model performance was done across different functional groups, and it showed very high accuracy, especially for compounds that contain aliphatic OH groups. The results have indicated that OWPCP provides a reliable prediction of logP. Partition coefficient ; Partition (number theory) ; Octanol ; Artificial intelligence ; Statistics ; Mathematics ; Computer science ; Chemistry ; Chromatography Analytical Chemistry and Chromatography
Authors: Travis Allen | Jake Graser | Ramsey Issa | Taylor D. Sparks
Source: Advances in Applied Ceramics Structural Functional and Bioceramics (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

Thermoelectric properties of TaVO<sub>5</sub> and GdTaO<sub>4</sub>: An experimental verification of machine learning prediction Advancements in materials discovery tend to rely disproportionately on happenstance and luck rather than employing a systematic approach. Recently, advances in computational power have allowed researchers to build computer models to predict the material properties of any chemical formula. From energy minimization techniques to machine learning-based models, these algorithms have unique strengths and weaknesses. However, a computational model is only as good as its accuracy when compared to real-world measurements. In this work, we take two recommendations from a thermoelectric machine learning model, TaVO 5 and GdTaO 4 , and measure their thermoelectric properties of Seebeck coefficient, thermal conductivity, and electrical conductivity. We see that the predictions are mixed; thermal conductivities are correctly predicted, while electrical conductivities and Seebeck coefficients are not. Furthermore, we explore TaVO 5 ’s unusually low thermal conductivity of 1.2 Wm −1 K −1 , and we discover a possible new avenue of research of a low thermal conductivity oxide family. Materials science ; Thermoelectric effect ; Engineering physics ; Machine learning ; Thermodynamics ; Computer science ; Engineering ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Gas Sensing Nanomaterials and Sensors
Authors: Chunyu Yang | Kai Zhu | Bing Yan
Source: ACS Applied Materials & Interfaces (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Hydrogen-Bonded Organic Frameworks for Antibiotic Fluorescent Sensing Artificial Intelligence-Enhanced Anticounterfeiting The paramount importance of anticounterfeiting measures in safeguarding consumers from counterfeit products lies in their ability to ensure product safety and reliability. Advanced luminescent anticounterfeiting materials, particularly those responsive to multiple stimuli, afford a dynamic and multilayered security assurance. This study presents the synthesis of a novel material, Eu/Tb@GC-3, via postsynthetic modification, which exhibits notable photoluminescent properties with emission at 544 and 614 nm. The material demonstrates high selectivity and sensitivity in detecting Nitrofural and Enrofloxacin, with limits of detection at 0.0122 and 0.0280 μM, respectively. Furthermore, multistimulus responsive luminescent fibers and inks were developed, facilitating intelligent anticounterfeiting labels. The integration of these labels with back-propagation neural networks (BPNNs) significantly enhances pattern recognition and authentication capabilities, providing an efficacious strategy to combat counterfeit products and ensure consumer safety. Materials science ; Fluorescence ; Nanotechnology Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Metal-Organic Frameworks: Synthesis and Applications