jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simon Axelrod | Eugene I. Shakhnovich | Rafael Gómez‐Bombarelli
Source: Nature Communications (article, 2022-06-15)
Field: Materials Science — Materials Chemistry

Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light. In principle, photoswitches with desired photophysical properties like high isomerization quantum yields can be identified through virtual screening with reactive simulations. In practice, these simulations are rarely used for screening, since they require hundreds of trajectories and expensive quantum chemical methods to account for non-adiabatic excited state effects. Here we introduce a diabatic artificial neural network (DANN), based on diabatic states, to accelerate such simulations for azobenzene derivatives. The network is six orders of magnitude faster than the quantum chemistry method used for training. DANN is transferable to azobenzene molecules outside the training set, predicting quantum yields for unseen species that are correlated with experiment. We use the model to virtually screen 3100 hypothetical molecules, and identify novel species with high predicted quantum yields. The model predictions are confirmed using high-accuracy non-adiabatic dynamics. Our results pave the way for fast and accurate virtual screening of photoactive compounds. Photoisomerization ; Diabatic ; Azobenzene ; Adiabatic process ; Quantum ; Excited state ; Molecule ; Isomerization ; Surface hopping ; Quantum chemistry ; Virtual screening ; Computer science ; Chemical physics ; Molecular dynamics ; Nanotechnology ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Machine Learning in Materials Science ; Photoreceptor and optogenetics research
Authors: Alexey N. Beskopylny | Sergey A. Stel’makh | Evgenii M. Shcherban’ | Levon R. Mailyan | Besarion Meskhi | Irina Razveeva | Andrei Chernil’nik | Nikita Beskopylny
Source: Applied Sciences (article, 2022-10-26)
Field: Materials Science — Materials Chemistry

Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression Currently, one of the topical areas of application of machine learning methods in the construction industry is the prediction of the mechanical properties of various building materials. In the future, algorithms with elements of artificial intelligence form the basis of systems for predicting the operational properties of products, structures, buildings and facilities, depending on the characteristics of the initial components and process parameters. Concrete production can be improved using artificial intelligence methods, in particular, the development, training and application of special algorithms to determine the characteristics of the resulting concrete. The aim of the study was to develop and compare three machine learning algorithms based on CatBoost gradient boosting, k-nearest neighbors and support vector regression to predict the compressive strength of concrete using our accumulated empirical database, and ultimately to improve the production processes in construction industry. It has been established that artificial intelligence methods can be applied to determine the compressive strength of self-compacting concrete. Of the three machine learning algorithms, the smallest errors and the highest coefficient of determination were observed in the KNN algorithm: MAE was 1.97; MSE, 6.85; RMSE, 2.62; MAPE, 6.15; and the coefficient of determination R2, 0.99. The developed models showed an average absolute percentage error in the range 6.15−7.89% and can be successfully implemented in the production process and quality control of building materials, since they do not require serious computing resources. Machine learning ; Support vector machine ; Computer science ; Artificial intelligence ; Mean absolute percentage error ; Compressive strength ; Regression ; Correlation coefficient ; Process (computing) ; Data mining ; Artificial neural network ; Mathematics ; Materials science ; Statistics Material Properties and Failure Mechanisms ; Machine Learning in Materials Science ; Innovative concrete reinforcement materials
Authors: Shiou Xuan Tan | Hwai Chyuan Ong | Andri Andriyana | Steven Lim | Yean Ling Pang | Fitranto Kusumo | Gek Cheng Ngoh
Source: Polymers (article, 2022-01-11)
Field: Materials Science — Biomaterials

Characterization and Parametric Study on Mechanical Properties Enhancement in Biodegradable Chitosan-Reinforced Starch-Based Bioplastic Film Bioplastic has been perceived as a promising candidate to replace petroleum-based plastics due to its environment-friendly and biodegradable characteristics. This study presents the chitosan reinforced starch-based bioplastic film prepared by the solution casting and evaporation method. The effects of processing parameters, i.e., starch concentration, glycerol loading, process temperature and chitosan loading on mechanical properties were examined. Optimum tensile strength of 5.19 MPa and elongation at break of 44.6% were obtained under the combined reaction conditions of 5 wt.% starch concentration, 40 wt.% glycerol loading, 20 wt.% chitosan loading and at a process temperature of 70 °C. From the artificial neural network (ANN) modeling, the coefficient of determination (R<sup>2</sup>) for tensile strength and elongation at break were found to be 0.9955 and 0.9859, respectively, which proved the model had good fit with the experimental data. Interaction and miscibility between starch and chitosan were proven through the peaks shifting to a lower wavenumber in FTIR and a reduction of crystallinity in XRD. TGA results suggested the chitosan-reinforced starch-based bioplastic possessed reasonable thermal stability under 290 °C. Enhancement in water resistance of chitosan-incorporated starch-based bioplastic film was evidenced with a water uptake of 251% as compared to a 302% registered by the pure starch-based bioplastic film. In addition, the fact that the chitosan-reinforced starch-based bioplastic film degraded to 52.1% of its initial weight after 28 days suggests it is a more sustainable alternative than the petroleum-based plastics. Bioplastic ; Chitosan ; Starch ; Materials science ; Ultimate tensile strength ; Elongation ; Crystallinity ; Biodegradation ; Composite material ; Chemical engineering ; Glycerol ; Chemistry ; Organic chemistry ; Waste management Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Alexander Luce | Ali Mahdavi | Florian Marquardt | Heribert Wankerl
Source: Journal of the Optical Society of America A (article, 2022-04-15)
Field: Materials Science — Surfaces, Coatings and Films

TMM-Fast, a transfer matrix computation package for multilayer thin-film optimization: tutorial Achieving the desired optical response from a multilayer thin-film structure over a broad range of wavelengths and angles of incidence can be challenging. An advanced thin-film structure can consist of multiple materials with different thicknesses and numerous layers. Design and optimization of complex thin-film structures with multiple variables is a computationally heavy problem that is still under active research. To enable fast and easy experimentation with new optimization techniques, we propose the Python package Transfer Matrix Method - Fast (TMM-Fast), which enables parallelized computation of reflection and transmission of light at different angles of incidence and wavelengths through the multilayer thin film. By decreasing computational time, generating datasets for machine learning becomes feasible, and evolutionary optimization can be used effectively. Additionally, the subpackage TMM-Torch allows us to directly compute analytical gradients for local optimization by using PyTorch Autograd functionality. Finally, an OpenAI Gym environment is presented, which allows the user to train new reinforcement learning agents on the problem of finding multilayer thin-film configurations. Python (programming language) ; Computation ; Computer science ; Thin film ; Reflection (computer programming) ; Wavelength ; Transfer matrix ; Optimization problem ; Transfer-matrix method (optics) ; Matrix (chemical analysis) ; Computational science ; Materials science ; Algorithm ; Optoelectronics ; Nanotechnology ; Computer vision Optical Coatings and Gratings ; Photonic Crystals and Applications ; Perovskite Materials and Applications
Authors: Shreya Modi | Virendra Kumar Yadav | Amel Gacem | Ismat H. Ali | Dhruv Dave | Samreen Heena Khan | Krishna Kumar Yadav | Sami‐ullah Rather | Yongtae Ahn | Cao Truong Son | Byong‐Hun Jeon
Source: Water (article, 2022-05-29)
Field: Chemistry — Organic Chemistry

Recent and Emerging Trends in Remediation of Methylene Blue Dye from Wastewater by Using Zinc Oxide Nanoparticles Due to the increased demand for clothes by the growing population, the dye-based sectors have seen fast growth in the recent decade. Among all the dyes, methylene blue dye is the most commonly used in textiles, resulting in dye effluent contamination. It is carcinogenic, which raises the stakes for the environment. The numerous sources of methylene blue dye and their effective treatment procedures are addressed in the current review. Even among nanoparticles, photocatalytic materials, such as TiO2, ZnO, and Fe3O4, have shown greater potential for photocatalytic methylene blue degradation. Such nano-sized metal oxides are the most ideal materials for the removal of water pollutants, as these materials are related to the qualities of flexibility, simplicity, efficiency, versatility, and high surface reactivity. The use of nanoparticles generated from waste materials to remediate methylene blue is highlighted in the present review. Methylene blue ; Wastewater ; Environmental remediation ; Effluent ; Photocatalysis ; Materials science ; Pollutant ; Population ; Waste management ; Chemistry ; Environmental engineering ; Environmental science ; Contamination ; Organic chemistry ; Catalysis Nanomaterials for catalytic reactions ; Nanoparticles: synthesis and applications ; Dye analysis and toxicity
Authors: Hui‐Lei Hou | Cosimo Anichini | Paolo Samorı́ | Alejandro Criado | Maurizio Prato
Source: Advanced Functional Materials (article, 2022-10-10)
Field: Materials Science — Materials Chemistry

2D Van der Waals Heterostructures for Chemical Sensing Abstract During the last 15 years, 2D materials have revolutionized the field of materials science. Moreover, because of their highest surface‐to‐volume ratio and properties extremely susceptible to their interaction with the local environment they became powerful active components for the development the high‐performance chemical sensors. By combining different 2D materials to form van der Waals heterostructures (VDWHs) it is possible to overcome the drawback of individual materials (such as inertness and zero‐bandgap of pristine graphene and less environmental stability of transition metal dichalcogenides). Meanwhile, VDWHs possess unprecedented and fascinating properties arising from the intimate interaction between the components, which can yield superior sensitivities, higher selectivity, and stability when employed to detect gases, biomolecules, and other organic/inorganic molecules. Herein, the latest developments and advances in the field of chemical sensors based on VDWH of 2D materials, with specific insight into the sensing mechanisms, are reviewed and future directions, challenges, and opportunities for the development of the next generation of (bio)chemical sensors with potential impact in environmental sciences and biomedical applications, and more specifically in (bio)chemical defense, industrial safety, food, and environmental surveillance, and medical (early) diagnostics, are discussed. Heterojunction ; Nanotechnology ; Graphene ; van der Waals force ; Chemical stability ; Materials science ; Biomolecule ; Molecule ; Chemistry ; Optoelectronics ; Organic chemistry 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Jing Wang | Xi Zhuo Jiang | Kai Luo
Source: Fuel (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Exploring reaction mechanism for ammonia/methane combustion via reactive molecular dynamics simulations Combustion ; NOx ; Chemistry ; Methane ; Reaction mechanism ; Elementary reaction ; Molecular dynamics ; Ammonia ; Chemical kinetics ; Reaction dynamics ; Nitrogen ; Kinetics ; Computational chemistry ; Physical chemistry ; Catalysis ; Molecule ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Atmospheric chemistry and aerosols
Authors: Yang Cao | Junling Yang | Ao Lu | Jing Gong | Yuanxing Yang | Xi Lin | Minghui Li | Haiwei Xu
Source: Frontiers in Molecular Biosciences (review, 2022-07-15)
Field: Materials Science — Biomaterials

Nanoparticles in ocular applications and their potential toxicity Nanotechnology has been developed rapidly in recent decades and widely applied in ocular disease therapy. Nano-drug delivery systems overcome the bottlenecks of current ophthalmic drug delivery and are characterized with strong biocompatibility, stability, efficiency, sustainability, controllability, and few side effects. Nanoparticles have been identified as a promising and generally safe ophthalmic drug-delivery system based on the toxicity assessment in animals. Previous studies have found that common nanoparticles can be toxic to the cornea, conjunctiva, and retina under certain conditions. Because of the species differences between humans and animals, advanced <i>in vitro</i> cell culture techniques, such as human organoids, can mimic the human organism to a certain extent, bringing nanoparticle toxicity assessment to a new stage. This review summarizes the advanced application of nanoparticles in ocular drug delivery and the potential toxicity, as well as some of the current challenges and future opportunities in nanotoxicological evaluation. Drug delivery ; Toxicity ; Biocompatibility ; Drug ; Nanotechnology ; Medicine ; Pharmacology ; Chemistry ; Materials science Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Drug Delivery Systems
Authors: Jianye Xia | Benjamín J. Sánchez | Yu Chen | Kate Campbell | Sergo Kasvandik | Jens Nielsen
Source: Nature Communications (article, 2022-05-20)
Field: Chemistry — Spectroscopy

Proteome allocations change linearly with the specific growth rate of Saccharomyces cerevisiae under glucose limitation Saccharomyces cerevisiae is a widely used cell factory; therefore, it is important to understand how it organizes key functional parts when cultured under different conditions. Here, we perform a multiomics analysis of S. cerevisiae by culturing the strain with a wide range of specific growth rates using glucose as the sole limiting nutrient. Under these different conditions, we measure the absolute transcriptome, the absolute proteome, the phosphoproteome, and the metabolome. Most functional protein groups show a linear dependence on the specific growth rate. Proteins engaged in translation show a perfect linear increase with the specific growth rate, while glycolysis and chaperone proteins show a linear decrease under respiratory conditions. Glycolytic enzymes and chaperones, however, show decreased phosphorylation with increasing specific growth rates; at the same time, an overall increased flux through these pathways is observed. Further analysis show that even though mRNA levels do not correlate with protein levels for all individual genes, the transcriptome level of functional groups correlates very well with its corresponding proteome. Finally, using enzyme-constrained genome-scale modeling, we find that enzyme usage plays an important role in controlling flux in amino acid biosynthesis. Proteome ; Saccharomyces cerevisiae ; Transcriptome ; Metabolome ; Biology ; Glycolysis ; Biochemistry ; Flux (metallurgy) ; Enzyme ; Pentose phosphate pathway ; Gene ; Cell biology ; Chemistry ; Gene expression Advanced Proteomics Techniques and Applications ; Microbial Metabolic Engineering and Bioproduction ; Protein Structure and Dynamics
Authors: Da-Hyun Jeong | Il‐Young Jo | Seungjin Lee | Ji Hwan Kim | Young-Seok Kim | Donguk Kim | John R. Reynolds | Myung‐Han Yoon | Bumjoon J. Kim
Source: Advanced Functional Materials (article, 2022-01-04)
Field: Materials Science — Polymers and Plastics

High‐Performance <i>n</i>‐Type Organic Electrochemical Transistors Enabled by Aqueous Solution Processing of Amphiphilicity‐Driven Polymer Assembly Abstract Despite the growing attention on organic electrochemical transistors (OECTs), most research has focused on the design of p‐type active materials, and the number of high‐performance n‐type materials is limited. Herein, a series of naphthalene diimide‐based polymers incorporated with asymmetrically branched oligo(ethylene glycol) (OEG) side chains are developed to enable green‐solvent‐processed, high‐performance n‐type OECTs. The branched OEG side chains afford sufficient solubility in eco‐friendly ethanol/water solvent mixtures. Importantly, taking advantage of the amphiphilic nature of OEG‐based polymers, ethanol/water solvents selectively solvate hydrophilic OEG side chains, while producing assembled π−π stacks of hydrophobic backbones. This enables highly ordered polymer packing with a preferential edge‐on orientation, and thus excellent lateral charge transport. In particular, the fine‐tuned OEG side chains of P(NDIMTEG‐T) provide compact backbone packing, effective polaron generation, and superior electrochemical stability with optimized swelling capability. The resultant n‐type OECT shows the best electrical/electrochemical performance in the family, represented by a high transconductance ( g m ) of 0.38 S cm −1 and a large figure‐of‐merit (µ C *) of 0.56 F V −1 cm −1 s −1 . This study demonstrates the use of aqueous processing in OECTs, for the first time, and suggests important guidelines for the design of n‐type organic mixed ionic‐electronic conductors with excellent OECT characteristics. Side chain ; Materials science ; Ethylene glycol ; Polymer ; Aqueous solution ; Electrochemistry ; Chemical engineering ; Amphiphile ; Solubility ; Polymer chemistry ; Nanotechnology ; Organic chemistry ; Chemistry ; Electrode ; Copolymer ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Snehasish Mishra | Puneet Kumar Singh | Ritesh Pattnaik | Subrat Kumar | Sanjay Kumar Ojha | Haragobinda Srichandan | Pankaj Kumar Parhi | Rajesh Kumar Jyothi | Prakash Kumar Sarangi
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-03-08)
Field: Materials Science — Biomaterials

Biochemistry, Synthesis, and Applications of Bacterial Cellulose: A Review The potential of cellulose nanocomposites in the new-generation super-performing nanomaterials is huge, primarily in medical and environment sectors, and secondarily in food, paper, and cosmetic sectors. Despite substantial illumination on the molecular aspects of cellulose synthesis, various process features, namely, cellular export of the nascent polysaccharide chain and arrangement of cellulose fibrils into a quasi-crystalline configuration, remain obscure. To unleash its full potential, current knowledge on nanocellulose dispersion and disintegration of the fibrillar network and the organic/polymer chemistry needs expansion. Bacterial cellulose biosynthesis mechanism for scaled-up production, namely, the kinetics, pathogenicity, production cost, and product quality/consistency remain poorly understood. The bottom-up bacterial cellulose synthesis approach makes it an interesting area for still wider and promising high-end applications, primarily due to the nanosynthesis mechanism involved and the purity of the cellulose. This study attempts to identify the knowledge gap and potential wider applications of bacterial cellulose and bacterial nanocellulose. This review also highlights the manufacture of bacterial cellulose through low-cost substrates, that is, mainly waste from brewing, agriculture, food, and sugar industries as well as textile, lignocellulosic biorefineries, and pulp mills. Bacterial cellulose ; Cellulose ; Nanocellulose ; Chemistry ; Cellulosic ethanol ; Polymer science ; Biochemical engineering ; Pulp and paper industry ; Nanotechnology ; Biotechnology ; Materials science ; Biochemistry ; Biology ; Engineering Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Polysaccharides and Plant Cell Walls
Authors: Xue Jia | Yanshuai Deng | Xin Bao | Honghao Yao | Shan Li | Li Zhou | Chen Chen | Xinyu Wang | Jun Mao | Feng Cao | Jiehe Sui | Junwei Wu | Cuiping Wang | Qian Zhang | Xingjun Liu
Source: npj Computational Materials (article, 2022-03-04)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials Abstract Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa. The accelerated materials design based on machine learning has enabled the systematic discovery of promising materials. Herein we proposed a successful strategy to discover and design a series of promising half-Heusler thermoelectric materials through the iterative combination of unsupervised machine learning with the labeled known half-Heusler thermoelectric materials. Subsequently, optimized zT values of ~0.5 at 925 K for p-type Sc 0.7 Y 0.3 NiSb 0.97 Sn 0.03 and ~0.3 at 778 K for n-type Sc 0.65 Y 0.3 Ti 0.05 NiSb were experimentally achieved on the same parent ScNiSb. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Solid-state ; Electricity ; Nanotechnology ; Mechanical engineering ; Machine learning ; Optoelectronics ; Engineering physics ; Computer science ; Engineering ; Electrical engineering ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties
Authors: Bokai Liu | N. Vu‐Bac | Xiaoying Zhuang | Weizhuo Lu | Xiaolong Fu | Timon Rabczuk
Source: Advances in Engineering Software (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Al-DeMat: A web-based expert system platform for computationally expensive models in materials design Computer science ; Machine learning ; Particle swarm optimization ; Visualization ; Gradient boosting ; Artificial intelligence ; Boosting (machine learning) ; Artificial neural network ; Data mining ; Web application ; Set (abstract data type) ; Key (lock) ; Random forest ; Web server ; The Internet ; World Wide Web Machine Learning in Materials Science ; Neural Networks and Applications ; Industrial Vision Systems and Defect Detection
Authors: Yue Liu | Xinxin Zou | Shuchang Ma | Maxim Avdeev | Siqi Shi
Source: Acta Materialia (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Feature selection method reducing correlations among features by embedding domain knowledge Interpretability ; Feature selection ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Domain knowledge ; Domain (mathematical analysis) ; Machine learning ; Embedding ; Data mining ; Process (computing) ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography