jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Francisco Javier López-Flores | Jorge Andrés Ornelas-Guillén | Alejandra Pérez-Nava | J. Betzabe González‐Campos | José María Ponce‐Ortega
Source: Industrial & Engineering Chemistry Research (article, 2024-03-27)
Field: Materials Science — Biomaterials

Data-Driven Machine Learning Approach for Modeling the Production and Predicting the Characteristics of Aligned Electrospun Nanofibers The generation of electrospun nanofibrous with controlled size, shape, and spatial orientation is crucial for the development of biomedical and electronic devices. Aligned nanofibers are advantageous over random nanofibers because control of the spatial orientation can improve electrical and optical properties and play an important role in tissue engineering applications, impacting the mechanical and biological properties of the scaffold. Therefore, different machine learning models have been developed to predict the optimal production of electrospun-aligned poly(vinyl alcohol) nanofibers. The database was obtained by multiple assays using the airgap electrospinning setup and varying the voltage, the distance between the tip and collector, and polymer concentration. Binary classification models were developed, which can predict the production or not of aligned nanofibers. In addition, regression models have been developed to predict the orientation, angle, and diameter of the nanofibers when there is a production of nanofibers. A convolutional neural network has also been developed. It was concluded that for the binary classification, the artificial neural network performs better predictions obtaining an accuracy equal to 0.94, and for the validation set, an accuracy equal to 0.90 and an F1-score equal to 0.87 were obtained. Nanofiber ; Electrospinning ; Materials science ; Biological system ; Artificial neural network ; Binary number ; Computer science ; Orientation (vector space) ; Convolutional neural network ; Artificial intelligence ; Nanotechnology ; Machine learning ; Polymer ; Composite material ; Mathematics Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Katarina Virijević | Marko Živanović | Dalibor Nikolić | Nevena Milivojević | Jelena Pavić | Ivana Morić | Lidija Šenerović | Luka Dragačević | Philipp J. Thurner | Manuel Rufin | Orestis G. Andriotis | Biljana Ljujić | Marina Miletić Kovačević | Miloš Papić | Nenad Filipović
Source: ACS Applied Materials & Interfaces (article, 2024-04-25)
Field: Materials Science — Biomaterials

AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing Here, an artificial intelligence (AI)-based approach was employed to optimize the production of electrospun scaffolds for in vivo wound healing applications. By combining polycaprolactone (PCL) and poly(ethylene glycol) (PEG) in various concentration ratios, dissolved in chloroform (CHCl<sub>3</sub>) and dimethylformamide (DMF), 125 different polymer combinations were created. From these polymer combinations, electrospun nanofiber meshes were produced and characterized structurally and mechanically via microscopic techniques, including chemical composition and fiber diameter determination. Subsequently, these data were used to train a neural network, creating an AI model to predict the optimal scaffold production solution. Guided by the predictions and experimental outcomes of the AI model, the most promising scaffold for further in vitro analyses was identified. Moreover, we enriched this selected polymer combination by incorporating antibiotics, aiming to develop electrospun nanofiber scaffolds tailored for in vivo wound healing applications. Our study underscores three noteworthy conclusions: (i) the application of AI is pivotal in the fields of material and biomedical sciences, (ii) our methodology provides an effective blueprint for the initial screening of biomedical materials, and (iii) electrospun PCL/PEG antibiotic-bearing scaffolds exhibit outstanding results in promoting neoangiogenesis and facilitating in vivo wound treatment. Polycaprolactone ; Materials science ; Ethylene glycol ; Nanofiber ; Electrospinning ; Scaffold ; In vivo ; Biomedical engineering ; Polymer ; Wound healing ; Nanotechnology ; Chemical engineering ; Composite material ; Surgery Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Advanced Sensor and Energy Harvesting Materials
Authors: Dylan M. Anstine | R.I. Zubatyuk | Olexandr Isayev
Source: ChemRxiv (preprint, 2024-04-30)
Field: Chemistry — Physical and Theoretical Chemistry

AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff. Despite this attraction, the benefits of such efficiency are only impactful when an MLIP uniquely enables insight into a target system or is broadly transferable outside of the training dataset, where models achieving the latter are seldom reported. In this work, we present the 2nd generation of our atoms-in-molecules neural network potential (AIMNet2), which is applicable to species composed of up to 14 chemical elements in both neutral and charged states, making it a valuable model for modeling the majority of non-metallic compounds. Using an exhaustive dataset of 20 million hybrid quantum chemical calculations, AIMNet2 combines ML-parameterized short-range and physics-based long-range terms to attain generalizability that reaches from simple organics to diverse molecules with “exotic” element-organic bonding. We show that AIMNet2 outperforms semi-empirical GFN-xTB and is on par with reference density functional theory for interaction energy contributions, conformer search tasks, torsion rotation profiles, and molecular-to-macromolecular geometry optimization. Overall, the demonstrated chemical coverage and computational efficiency of AIMNet2 is a significant step toward providing access to MLIPs that avoid the crucial limitation of curating additional quantum chemical data and retraining with each new application. Artificial neural network ; Business ; Chemistry ; Computer science ; Artificial intelligence Various Chemistry Research Topics
Authors: Patrick Ehi Imoisili | Mamookho Elizabeth Makhatha | Tien‐Chien Jen
Source: Journal of Science Advanced Materials and Devices (article, 2024-03-08)
Field: Materials Science — Polymers and Plastics

Artificial Intelligence prediction and optimization of the mechanical strength of modified Natural Fibre/MWCNT polymer nanocomposite Artificial Intelligence (AI), techniques like artificial neural networks (ANN), and machine learning have been used to solve a variety of engineering problems. In this study, Multiwall Carbon Nanotube (MWCNT) and Natural Fibres (NF) from plantain (Musa Paradisiaca) fiber (PF), were utilized to prepare a reinforced hybrid polymer nanocomposite for advanced composite applications. A chemical solution containing potassium permanganate (KMnO4) in acetone (C3H6O) was applied to modify the fibers to alter their surface and improve adhesion and interaction between the PF/polymer matrix. To predict and optimize the tensile strength (TS) of the prepared PF/MWCNT hybrid nanocomposite (PFMNC), the ANN model with hyper-parameter optimization in a single-layer-perceptron architecture of 3-5-1 was used with 5 neurons in the hidden layer, and Box-Behnken Design (BBD) was utilized. Scanning electron microscope (SEM) micrographs demonstrate that KMnO4 modification has impacted the TS of the hybridized nanocomposite. Mechanical Test results show that these variables impacted the TS of the PFMNC as shown by analysis of variance (ANOVA) with R2 = 0.9986. The expected findings were nearly identical to the experimental results. The model predicted an optimal tensile strength of 46.1563 Mpa. To substantiate the reliability of the empirical experimental investigation, TS analysis was performed at predicted optimal settings. TS results showed an average strength of 45.4401 Mpa. About 98.45 % of the projected tensile strength is accounted for by the model. The present study has demonstrated the effectiveness of the ANN-BBD modeling technique in achieving the appropriate mechanical property values quickly, reducing production costs, and preserving resources. Materials science ; Ultimate tensile strength ; Composite material ; Nanocomposite ; Carbon nanotube ; Polymer ; Response surface methodology ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Sanjeev Gautam | Priyal Singhal | Suvankar Chakraverty | Navdeep Goyal
Source: Materials Science in Semiconductor Processing (review, 2024-07-03)
Field: Materials Science — Materials Chemistry

Synthesis and characterization strategies of two-dimensional (2D) materials for quantum technologies: A comprehensive review Materials science ; Characterization (materials science) ; Nanotechnology ; Engineering physics ; Engineering 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Afaf Murtadha Kadhum | Azal S. Waheeb | Masar Ali Awad | Abrar U. Hassan | Sajjad Hussain Sumrra | Cihat Güleryüz | Ayesha Mohyuddin | Sadaf Noreen | Hussein Ali Kadhim Kyhoiesh | Mohammed T. Alotaibi
Source: Solar Energy (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials: A DFT and ML analysis Photovoltaic system ; Quantum dot ; Selenide ; Basis (linear algebra) ; Carbon quantum dots ; Materials science ; Electronic structure ; Carbon fibers ; Engineering physics ; Nanotechnology ; Physics ; Quantum mechanics ; Mathematics ; Electrical engineering ; Composite material Quantum Dots Synthesis And Properties ; Graphene research and applications ; 2D Materials and Applications
Authors: Teerachote Pakornchote | Natthaphon Choomphon-anomakhun | Sorrjit Arrerut | Chayanon Atthapak | Sakarn Khamkaeo | Thiparat Chotibut | Thiti Bovornratanaraks
Source: Scientific Reports (article, 2024-01-13)
Field: Materials Science — Materials Chemistry

Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symmetry. In this study, we leverage novel diffusion probabilistic (DP) models to denoise atomic coordinates rather than adopting the standard score matching approach in CDVAE. Our proposed DP-CDVAE model can reconstruct and generate crystal structures whose qualities are statistically comparable to those of the original CDVAE. Furthermore, notably, when comparing the carbon structures generated by the DP-CDVAE model with relaxed structures obtained from density functional theory calculations, we find that the DP-CDVAE generated structures are remarkably closer to their respective ground states. The energy differences between these structures and the true ground states are, on average, 68.1 meV/atom lower than those generated by the original CDVAE. This significant improvement in the energy accuracy highlights the effectiveness of the DP-CDVAE model in generating crystal structures that better represent their ground-state configurations. Autoencoder ; Leverage (statistics) ; Generative model ; Crystal structure prediction ; Crystal (programming language) ; Probabilistic logic ; Computer science ; Matching (statistics) ; Algorithm ; Artificial intelligence ; Crystal structure ; Statistical physics ; Physics ; Mathematics ; Generative grammar ; Deep learning ; Chemistry ; Crystallography ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Xiaoyu Yang | Zhichun Bi | Chenghui Yin | Hui Huang | Yongxin Li
Source: Talanta (article, 2024-02-27)
Field: Materials Science — Materials Chemistry

A novel hybrid sensor array based on the polyphenol oxidase and its nanozymes combined with the machine learning based dual output model to identify tea polyphenols and Chinese teas A novel sensor array was developed based on the enzyme/nanozyme hybridization for the identification of tea polyphenols (TPs) and Chinese teas. The enzyme/nanozyme with polyphenol oxidase activity can catalyze the reaction between TPs and 4-aminoantipyrine (4-AAP) to produce differences in color, and the sensor array was thus constructed to accurately identify TPs mixed in different species, concentrations, or ratios. In addition, a machine learning based dual output model was further used to effectively predict the classes and concentrations of unknown samples. Therefore, the qualitative and quantitative detection of TPs can be realized continuously and quickly. Furthermore, the sensor array combining the machine learning based dual output model was also utilized for the identification of Chinese teas. The method can distinguish the six teas series in China, and then precisely differentiate the more specific tea varieties. This study provides an efficient and facile strategy for the identification of teas and tea products. Chemistry ; Polyphenol oxidase ; Polyphenol ; Dual (grammatical number) ; Biological system ; Identification (biology) ; Food science ; Enzyme ; Chromatography ; Biochemistry ; Botany ; Antioxidant Advanced Nanomaterials in Catalysis ; Advanced Chemical Sensor Technologies ; Electrochemical sensors and biosensors
Authors: Francesca Cenci | Samir Diab | Paola Ferrini | Catajina Harabajiu | Massimiliano Barolo | Fabrizio Bezzo | Pierantonio Facco
Source: International Journal of Pharmaceutics (article, 2024-05-18)
Field: Materials Science — Materials Chemistry

Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent types are identified by molecular descriptors, which, in this study, are considered as UNIFAC subgroups. To overcome the potential lack of UNIFAC subgroups for the complex Active Pharmaceutical Ingredients (APIs) currently developed in the pharmaceutical industry, the API molecule is considered as a unique entity in the proposed modelling approach. Therefore, API solubility is predicted as a function of temperature, functional subgroups of the solvents and composition of the solvent mixture; in turn, regressors' correlation is handled through Partial Least-Squares (PLS) regression. The method is developed and tested with experimental data of a real API and 14 organic solvents that are industrially employed for crystallisation. Solubility predictions are accurate and precise for single solvents, binary mixtures and ternary mixtures of organic solvents at different compositions and temperatures, with a determination coefficient R<sup>2</sup> ≥ 0.90. To further test the applicability of the model, the proposed approach is applied to 9 literature organic solubility datasets of drugs and drug-like compounds and compared to benchmark solubility models in the literature. Results show that the proposed approach provides satisfactory predictions: the majority of validation and calibration data have R<sup>2</sup> = 0.95-0.99; the ratio between RMSE (root mean squared error) of the proposed method and the range of measured solubility values is from 1 to 3 orders of magnitude smaller than the RMSE ratio obtained by the benchmark models. Solubility ; UNIFAC ; Quantitative structure–activity relationship ; Chemistry ; Molecular descriptor ; Ternary operation ; Mean squared error ; Solvent ; Partial least squares regression ; Group contribution method ; Thermodynamics ; Biological system ; Activity coefficient ; Organic chemistry ; Machine learning ; Computer science ; Mathematics ; Statistics ; Aqueous solution ; Stereochemistry ; Phase equilibrium Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Zhao Li | Kaihang Shi | David Dubbeldam | Mark Dewing | Chris Knight | Álvaro Vázquez‐Mayagoitia | Randall Q. Snurr
Source: Journal of Chemical Theory and Computation (article, 2024-11-19)
Field: Chemistry — Inorganic Chemistry

Efficient Implementation of Monte Carlo Algorithms on Graphical Processing Units for Simulation of Adsorption in Porous Materials We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials, and this is illustrated with grand canonical Monte Carlo simulations of CO<sub>2</sub> adsorption in Mg-MOF-74 that show much better agreement with experiment than simulations using a traditional force field. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA. Monte Carlo method ; CUDA ; Computer science ; Computational science ; Kinetic Monte Carlo ; Parallel computing ; Algorithm ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Shuwei Tang | Ai Peng | Shulin Bai | Da Wan | Xiaodong Li | Wanrong Guo | Tuo Zheng | Hao Wang
Source: Materials Today Physics (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Weak interatomic interactions induced low lattice thermal conductivity in 2D/2D PbSe/SnSe vdW heterostructure Materials science ; Heterojunction ; Condensed matter physics ; Phonon ; Thermoelectric effect ; Anharmonicity ; Thermal conductivity ; Phonon scattering ; Scattering ; Optoelectronics ; Optics ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; 2D Materials and Applications
Authors: Christoforos Brozos | Jan G. Rittig | Sandip Bhattacharya | Elie Akanny | Christina Kohlmann | Alexander Mitsos
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2024-05-03)
Field: Chemistry — Analytical Chemistry

Graph neural networks for surfactant multi-property prediction Surfactants are of high importance in different industrial sectors such as cosmetics, detergents, oil recovery and drug delivery systems. Therefore, many quantitative structure–property relationship (QSPR) models have been developed for surfactants. Each predictive model typically focuses on one surfactant class, mostly nonionics. Graph Neural Networks (GNNs) have exhibited a great predictive performance for property prediction of ionic liquids, polymers and drugs in general. Specifically for surfactants, GNNs can successfully predict critical micelle concentration (CMC), a key surfactant property associated with micellization. A key factor in the predictive ability of QSPR and GNN models is the data available for training. Based on extensive literature search, we create the largest available CMC database with 429 molecules and the first large data collection for surface excess concentration (Γm), another surfactant property associated with foaming, with 164 molecules. Then, we develop GNN models to predict the CMC and Γm and we explore different learning approaches, i.e., single- and multi-task learning, as well as different training strategies, namely ensemble and transfer learning. We find that a multi-task GNN with ensemble learning trained on all Γm and CMC data performs best. Thus, our results show that the simultaneous use of data from highly correlated properties can improve the predictability of surfactant properties for which only a small amount of experimental data is available. Finally, we test the ability of our CMC model to generalize on industrial grade pure component surfactants. The GNN yields highly accurate predictions for CMC, showing great potential for future industrial applications. Property (philosophy) ; Graph ; Computer science ; Artificial neural network ; Pulmonary surfactant ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Philosophy Petroleum Processing and Analysis ; Text and Document Classification Technologies ; Enhanced Oil Recovery Techniques
Authors: Mohammad Shakiba | Alexey V. Akimov
Source: Journal of Chemical Theory and Computation (article, 2024-04-06)
Field: Materials Science — Materials Chemistry

Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics In this work, we report a simple, efficient, and scalable machine-learning (ML) approach for mapping non-self-consistent Kohn–Sham Hamiltonians constructed with one kind of density functional to the nearly self-consistent Hamiltonians constructed with another kind of density functional. This approach is designed as a fast surrogate Hamiltonian calculator for use in long nonadiabatic dynamics simulations of large atomistic systems. In this approach, the input and output features are Hamiltonian matrices computed from different levels of theory. We demonstrate that the developed ML-based Hamiltonian mapping method (1) speeds up the calculations by several orders of magnitude, (2) is conceptually simpler than alternative ML approaches, (3) is applicable to different systems and sizes and can be used for mapping Hamiltonians constructed with arbitrary density functionals, (4) requires a modest training data, learns fast, and generates molecular orbitals and their energies with the accuracy nearly matching that of conventional calculations, and (5) when applied to nonadiabatic dynamics simulation of excitation energy relaxation in large systems yields the corresponding time scales within the margin of error of the conventional calculations. Using this approach, we explore the excitation energy relaxation in C60 fullerene and Si75H64 quantum dot structures and derive qualitative and quantitative insights into dynamics in these systems. Kohn–Sham equations ; Hamiltonian (control theory) ; Molecular dynamics ; Physics ; Computer science ; Statistical physics ; Density functional theory ; Classical mechanics ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics