jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: T. Sutojo | Supriadi Rustad | Muhamad Akrom | Abdul Syukur | Guruh Fajar Shidik | Hermawan Kresno Dipojono
Source: npj Materials Degradation (article, 2023-03-17)
Field: Materials Science — Materials Chemistry

A machine learning approach for corrosion small datasets Abstract In this work, we developed a QSAR model using the K-Nearest Neighbor (KNN) algorithm to predict the corrosion inhibition performance of the inhibitor compound. To overcome the small dataset problems, virtual samples are generated and added to the training set using a Virtual Sample Generation (VSG) method. The generalizability of the proposed KNN + VSG model is verified by using six small datasets from references and comparing their prediction performances. The research shows that for the six datasets, the proposed model is able to make predictions with the best accuracy. Adding virtual samples to the training data helps the algorithm recognize feature-target relationship patterns, and therefore increases the number of chemical quantum parameters correlated with corrosion inhibition efficiency. This proposed method strengthens the prospect of ML for developing material designs, especially in the case of small datasets. Generalizability theory ; k-nearest neighbors algorithm ; Computer science ; Artificial intelligence ; Machine learning ; Set (abstract data type) ; Training set ; Data mining ; Sample (material) ; Feature (linguistics) ; Data set ; Quantum chemical ; Virtual screening ; Pattern recognition (psychology) ; Mathematics ; Statistics ; Chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Maciej Majewski | Adrià Pérez | Philipp Thölke | Stefan H. Doerr | Nicholas E. Charron | Toni Giorgino | Brooke E. Husic | Cecilia Clementi | Frank Noé | Gianni De Fabritiis
Source: Nature Communications (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Machine learning coarse-grained potentials of protein thermodynamics A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics. Molecular dynamics ; Computer science ; Function (biology) ; Statistical mechanics ; Statistical physics ; Atom (system on chip) ; Physics ; Chemistry ; Computational chemistry ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Jiayu Wu | Tianyue Chen | Shengbo Ge | Wei Fan | Hui Wang | Zhongfeng Zhang | Éric Lichtfouse | Thuan Van Tran | Rock Keey Liew | Mashallah Rezakazemi | Runzhou Huang
Source: Environmental Chemistry Letters (review, 2023-08-12)
Field: Materials Science — Materials Chemistry

Synthesis and applications of carbon quantum dots derived from biomass waste: a review Context (archaeology) ; Nanotechnology ; Quantum dot ; Renewable energy ; Nanomaterials ; Supercapacitor ; Biomass (ecology) ; Carbon fibers ; Carbon quantum dots ; Energy storage ; Materials science ; Environmental science ; Chemistry ; Engineering ; Electrochemistry ; Physics ; Power (physics) ; Ecology Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Ahmed E. Alprol | Abdallah Tageldein Mansour | Abdelwahab M. Abdelwahab | Mohamed Ashour
Source: Catalysts (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

Advances in Green Synthesis of Metal Oxide Nanoparticles by Marine Algae for Wastewater Treatment by Adsorption and Photocatalysis Techniques The use of algae-based green synthesis of metal oxide nanoparticles (MONPs) for bioremediation is an environmentally friendly and cost-effective alternative to conventional approaches. Algal-mediated synthesis offers several benefits over other biogenic processes, such as plants, bacteria, and fungi, including ease of synthesis, scalability, and rapid synthesis. Algae are readily available in nature, nontoxic, and can produce various types of metal oxide nanoparticles. This approach could significantly accelerate the development of novel algae-nanomaterials with improved properties and performance, leading to more efficient and cost-effective bioremediation of pollutants from water solutions, seawater, and industrial effluent. This review focuses on the biogenic fabrication of metal oxide nanoparticles based on aquatic plants (microalgae and seaweeds) due to their many advantages and attractive applications in pollutant remediation from aqueous solutions. Additionally, photocatalysis is highlighted as a promising tool for the remediation of industrial effluents due to its efficacy, ease of use, quick oxidation, cost-effectiveness, and reduced synthesis of harmful byproducts. Environmental remediation ; Bioremediation ; Sewage treatment ; Wastewater ; Pollutant ; Algae ; Photocatalysis ; Effluent ; Environmental science ; Environmental chemistry ; Nanotechnology ; Chemistry ; Materials science ; Environmental engineering ; Contamination ; Ecology ; Organic chemistry ; Biology ; Catalysis Nanoparticles: synthesis and applications ; Algal biology and biofuel production ; Magnesium Oxide Properties and Applications
Authors: Roland Hellinger | Arnar Sigurdsson | Wenxin Wu | Elena V. Romanova | Lingjun Li | Jonathan V. Sweedler | Roderich D. Süßmuth | Christian W. Gruber
Source: Nature Reviews Methods Primers (article, 2023-03-30)
Field: Chemistry — Spectroscopy

Peptidomics Computational biology ; Proteomics ; Biology ; In silico ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Chemical Synthesis and Analysis
Authors: Muhammad Shahbaz | Abida Akram | Naveed Iqbal Raja | Tariq Mukhtar | Asma Mehak | Noor Fatima | Maryam Ajmal | Kishwar Ali | Nilofar Mustafa | Fozia Abasi
Source: PLoS ONE (article, 2023-02-07)
Field: Materials Science — Materials Chemistry

Antifungal activity of green synthesized selenium nanoparticles and their effect on physiological, biochemical, and antioxidant defense system of mango under mango malformation disease Plant extract-based green synthesis of nanoparticles is an emerging class of nanotechnology that has revolutionized the entire field of biological sciences. Green synthesized nanoparticles are used as super-growth promoters and antifungal agents. In this study, selenium nanoparticles (SeNPs) were synthesized using Melia azedarach leaves extract as the main reducing and stabilizing agent and characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray (EDX), and fourier transform infrared spectrometer (FTIR). The green synthesized SeNPs were exogenously applied on Mangifera indica infected with mango malformation disease. The SeNPs at a concentration of 30 μg/mL were found to be the best concentration which enhanced the physiological (chlorophyll and membrane stability index), and biochemical (proline and soluble sugar) parameters. The antioxidant defense system was also explored, and it was reported that green synthesized SeNPs significantly reduced the biotic stress by enhancing enzymatic and non-enzymatic activities. In vitro antifungal activity of SeNPs reported that 300 μg/mL concentration inhibited the Fusarium mangiferae the most. This study is considered the first biocompatible approach to evaluate the potential of green synthesized SeNPs to improve the health of mango malformation-infected plants and effective management strategy to inhibit the growth of F. mangifera. Mangifera ; Antioxidant ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Nanoparticle ; Chemistry ; Biochemistry ; Materials science ; Biology ; Botany ; Nanotechnology ; Chemical engineering Nanoparticles: synthesis and applications ; Selenium in Biological Systems ; Carbon and Quantum Dots Applications
Authors: Honghao Chen | Yingzhe Zheng | Jiali Li | Lanyu Li | Xiaonan Wang
Source: ACS Nano (review, 2023-06-02)
Field: Materials Science — Materials Chemistry

AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS) Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, and nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth in data, the adoption and exploitation of artificial intelligence (AI) as part of the materials research framework have had a tremendous impact on the development of nanomaterials. AI has enabled revolutionary next-generation paradigms to significantly accelerate all stages of material discovery and facilitate the exploration of the enormous design space. In this review, we summarize recent advancements of AI applications in nanomaterials discovery, with a special emphasis on the selected applications of AI and nanotechnology for the net-zero emission future including the development of solar cells, hydrogen energy, battery materials for renewable energy, and CO<sub>2</sub> capture and conversion materials for carbon capture, utilization and storage (CCUS) technologies. In addition, we discuss the limitations and challenges of current AI applications in this area by identifying the gaps that exist in current development. Finally, we present the prospect for future research directions in order to facilitate the large-scale applications of artificial intelligence for advancements in nanomaterials. Nanotechnology ; Renewable energy ; Computer science ; Nanomaterials ; Energy storage ; Materials science ; Engineering ; Physics ; Electrical engineering ; Power (physics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Maria Silvia Morlino | Rebecca Serna García | Filippo Savio | Guido Zampieri | Tomas Morosinotto | Laura Treu | Stefano Campanaro
Source: Biotechnology Advances (review, 2023-09-27)
Field: Materials Science — Biomaterials

Cupriavidus necator as a platform for polyhydroxyalkanoate production: An overview of strains, metabolism, and modeling approaches Cupriavidus necator is a bacterium with a high phenotypic diversity and versatile metabolic capabilities. It has been extensively studied as a model hydrogen oxidizer, as well as a producer of polyhydroxyalkanoates (PHA), plastic-like biopolymers with a high potential to substitute petroleum-based materials. Thanks to its adaptability to diverse metabolic lifestyles and to the ability to accumulate large amounts of PHA, C. necator is employed in many biotechnological processes, with particular focus on PHA production from waste carbon sources. The large availability of genomic information has enabled a characterization of C. necator's metabolism, leading to the establishment of metabolic models which are used to devise and optimize culture conditions and genetic engineering approaches. In this work, the characteristics of available C. necator strains and genomes are reviewed, underlining how a thorough comprehension of the genetic variability of C. necator is lacking and it could be instrumental for wider application of this microorganism. The metabolic paradigms of C. necator and how they are connected to PHA production and accumulation are described, also recapitulating the variety of carbon substrates used for PHA accumulation, highlighting the most promising strategies to increase the yield. Finally, the review describes and critically analyzes currently available genome-scale metabolic models and reduced metabolic network applications commonly employed in the optimization of PHA production. Overall, it appears that the capacity of C. necator of performing CO<sub>2</sub> bioconversion to PHA is still underexplored, both in biotechnological applications and in metabolic modeling. However, the accurate characterization of this organism and the efforts in using it for gas fermentation can help tackle this challenging perspective in the future. Cupriavidus necator ; Polyhydroxyalkanoates ; Bioplastic ; Biochemical engineering ; Metabolic engineering ; Organism ; Computational biology ; Biotechnology ; Metabolic network ; Synthetic biology ; Biology ; Adaptability ; Bacteria ; Biochemistry ; Gene ; Genetics ; Engineering ; Ecology biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Biofuel production and bioconversion
Authors: Kun Xu | Songtao Zhang | Xiaoli Zhuang | Guangxun Zhang | Yijian Tang | Huan Pang
Source: Advances in Colloid and Interface Science (review, 2023-11-10)
Field: Chemistry — Inorganic Chemistry

Recent progress of MOF-functionalized nanocomposites: From structure to properties Nanotechnology ; Materials science ; Metal-organic framework ; Nanocomposite ; Supercapacitor ; Nanopore ; Porosity ; Hybrid material ; Characterization (materials science) ; Rational design ; Composite number ; Mesoporous material ; Composite material ; Catalysis ; Chemistry ; Electrochemistry ; Electrode ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Supercapacitor Materials and Fabrication ; Boron and Carbon Nanomaterials Research
Authors: Sara N. Alektiar | Jimin Han | Yun Dang | Camille Rubel | Zachary K. Wickens
Source: Journal of the American Chemical Society (article, 2023-05-15)
Field: Chemistry — Organic Chemistry

Radical Hydrocarboxylation of Unactivated Alkenes via Photocatalytic Formate Activation Herein we disclose a strategy to promote the hydrocarboxylation of unactivated alkenes using photochemical activation of formate salts. We illustrate that an alternative initiation mechanism circumvents the limitations of prior approaches and enables hydrocarboxylation of this challenging substrate class. Specifically, we found that accessing the requisite thiyl radical initiator without an exogenous chromophore eliminates major byproducts that have plagued attempts to exploit similar reactivity for unactivated alkene substrates. This redox-neutral method is technically simple to execute and effective across a broad range of alkene substrates. Feedstock alkenes, such as ethylene, are hydrocarboxylated at ambient temperature and pressure. A series of radical cyclization experiments indicate how the reactivity described in this report can be diverted by more complex radical processes. Chemistry ; Alkene ; Formate ; Reactivity (psychology) ; Photochemistry ; Chromophore ; Ethylene ; Substrate (aquarium) ; Redox ; Organic chemistry ; Combinatorial chemistry ; Catalysis Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: Xiaoxun Gong | He Li | Nianlong Zou | Runzhang Xu | Wenhui Duan | Yong Xu
Source: Nature Communications (article, 2023-05-18)
Field: Materials Science — Materials Chemistry

General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian The combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure, which can naturally preserve the Euclidean symmetry even in the presence of spin-orbit coupling. Our DeepH-E3 method enables efficient electronic structure calculation at ab initio accuracy by learning from DFT data of small-sized structures, making the routine study of large-scale supercells (>10<sup>4</sup> atoms) feasible. The method can reach sub-meV prediction accuracy at high training efficiency, showing state-of-the-art performance in our experiments. The work is not only of general significance to deep-learning method development but also creates opportunities for materials research, such as building a Moiré-twisted material database. Ab initio ; Computer science ; Density functional theory ; Hamiltonian (control theory) ; Equivariant map ; Artificial neural network ; Deep learning ; Artificial intelligence ; Electronic structure ; A priori and a posteriori ; Physics ; Quantum mechanics ; Mathematics ; Mathematical optimization ; Pure mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Tengyan Xu | Jiaqi Wang | Shuang Zhao | Dinghao Chen | Hongyue Zhang | Yu Fang | Nan Kong | Ziao Zhou | Wenbin Li | Huaimin Wang
Source: Nature Communications (article, 2023-06-30)
Field: Materials Science — Biomaterials

Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector C<sub>g</sub>, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation reaches 87.1%. Notably, the de novo-designed peptide hydrogel selected from this work boosts the immune response of the receptor binding domain of SARS-CoV-2 in the mice model. Our approach taps into the potential of machine learning for predicting peptide hydrogelator and significantly expands the scope of natural peptide hydrogels. Self-healing hydrogels ; Peptide ; Construct (python library) ; Computer science ; Amino acid ; Function (biology) ; Peptide sequence ; Artificial intelligence ; Combinatorial chemistry ; Chemistry ; Biochemistry ; Biology ; Cell biology Supramolecular Self-Assembly in Materials ; Antimicrobial Peptides and Activities ; Glycosylation and Glycoproteins Research
Authors: Jingchen Sui | Na Wang | Jingkang Wang | Xin Huang | Ting Wang | Lina Zhou | Hongxun Hao
Source: Chemical Science (review, 2023-01-01)
Field: Chemistry — Spectroscopy

Strategies for chiral separation: from racemate to enantiomer Chiral separation has become a crucial topic for effectively utilizing superfluous racemates synthesized by chemical means and satisfying the growing requirements for producing enantiopure chiral compounds. However, the remarkably close physical and chemical properties of enantiomers present significant obstacles, making it necessary to develop novel enantioseparation methods. This review comprehensively summaries the latest developments in the main enantioseparation methods, including preparative-scale chromatography, enantioselective liquid-liquid extraction, crystallization-based methods for chiral separation, deracemization process coupling racemization and crystallization, porous material method and membrane resolution method, focusing on significant cases involving crystallization, deracemization and membranes. Notably, potential trends and future directions are suggested based on the state-of-art "coupling" strategy, which may greatly reinvigorate the existing individual methods and facilitate the emergence of cross-cutting ideas among researchers from different enantioseparation domains. Enantiomer ; Separation (statistics) ; Resolution (logic) ; Separation method ; Chemistry ; Combinatorial chemistry ; Computer science ; Stereochemistry ; Chromatography ; Artificial intelligence ; Machine learning Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications
Authors: Olajide Sunday Faleye | Bharath Reddy Boya | Jin-Hyung Lee | Inho Choi | Jintae Lee
Source: Pharmacological Reviews (review, 2023-10-16)
Field: Chemistry — Organic Chemistry

Halogenated Antimicrobial Agents to Combat Drug-Resistant Pathogens Antimicrobial ; Anti-Infective Agents ; Drug ; Antimicrobial drug ; Microbiology ; Drug resistance ; Pharmacology ; Medicine ; Chemistry ; Biology Synthesis and Biological Evaluation ; Phenothiazines and Benzothiazines Synthesis and Activities ; Pneumocystis jirovecii pneumonia detection and treatment
Authors: Anton R. Egorov | Anatoly A. Kirichuk | В. В. Рубаник | Alexander G. Tskhovrebov | Andreii S. Kritchenkov
Source: Materials (review, 2023-09-05)
Field: Materials Science — Biomaterials

Chitosan and Its Derivatives: Preparation and Antibacterial Properties This comprehensive review illuminates the various methods of chitosan extraction, its antibacterial properties, and its multifarious applications in diverse sectors. We delve into chemical, physical, biological, hybrid, and green extraction techniques, each of which presents unique advantages and disadvantages. The choice of method is dictated by multiple variables, including the desired properties of chitosan, resource availability, cost, and environmental footprint. We explore the intricate relationship between chitosan’s antibacterial activity and its properties, such as cationic density, molecular weight, water solubility, and pH. Furthermore, we spotlight the burgeoning applications of chitosan-based materials like films, nanoparticles, nonwoven materials, and hydrogels across the food, biomedical, and agricultural sectors. The review concludes by highlighting the promising future of chitosan, underpinned by technological advancements and growing sustainability consciousness. However, the critical challenges of optimizing chitosan’s production for sustainability and efficiency remain to be tackled. Chitosan ; Sustainability ; Self-healing hydrogels ; Biochemical engineering ; Materials science ; Nanotechnology ; Extraction (chemistry) ; Process engineering ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Polymer chemistry ; Ecology Nanocomposite Films for Food Packaging ; Nanoparticles: synthesis and applications ; Pineapple and bromelain studies