jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shahira H. EL‐Moslamy | Mohamed Elnouby | Ahmed H. Rezk | Esmail M. El‐Fakharany
Source: Scientific Reports (article, 2023-02-23)
Field: Materials Science — Materials Chemistry

Scaling-up strategies for controllable biosynthetic ZnO NPs using cell free-extract of endophytic Streptomyces albus: characterization, statistical optimization, and biomedical activities evaluation In this study, we identified a suitable precursor and good cellular compartmentalization for enhancing bioactive metabolites to produce biosynthetic zinc oxide nanoparticles (ZnO NPs). An effective medium for cultivating endophytic Streptomyces albus strain E56 was selected using several optimized approaches in order to maximize the yield of biosynthetic ZnO NPs. The highest biosynthetic ZnO NPs yield (4.63 g/L) was obtained when pipetting the mixed cell-free fractions with 100 mM of zinc sulfate as a precursor. The generation of biosynthetic ZnO NPs was quickly verified using a colored solution (white color) and UV-Visible spectroscopy (maximum peak, at 320 nm). On a small scale, the Taguchi method was applied to improve the culture medium for culturing the strain E56. As a result, its cell-dry weight was 3.85 times that of the control condition. And then the biosynthesis of ZnO NPs (7.59 g/L) was increased by 1.6 times. Furthermore, by using the Plackett-Burman design to improve the utilized biogenesis pathway, the biosynthesis of ZnO NPs (18.76 g/L) was increased by 4.3 times. To find the best growth production line, we used batch and fed batch fermentation modes to gradually scale up biomass output. All kinetics of studied cell growth were evaluated during fed-batch fermentation as follows: biomass yield was 271.45 g/L, yield coefficient was 94.25 g/g, and ZnO NPs yield was 345.32 g/L. In vitro, the effects of various dosages of the controllable biosynthetic ZnO NPs as antimicrobial and anticancer agents were also investigated. The treatments with controllable biosynthetic ZnO NPs had a significant impact on all the examined multidrug-resistant human pathogens as well as cancer cells. Streptomyces albus ; Yield (engineering) ; Zinc ; Biosynthesis ; Plackett–Burman design ; Fermentation ; Nanoparticle ; Chemistry ; Nuclear chemistry ; Streptomyces ; Biochemistry ; Materials science ; Food science ; Nanotechnology ; Biology ; Bacteria ; Organic chemistry ; Enzyme Nanoparticles: synthesis and applications ; Microbial Natural Products and Biosynthesis ; Bone Tissue Engineering Materials
Authors: Jungwon Park | Young‐Min Kim | Seonghun Hong | Byungchan Han | Ki Tae Nam | Yousung Jung
Source: Matter (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

Closed-loop optimization of nanoparticle synthesis enabled by robotics and machine learning Nanoparticle ; Computer science ; Artificial intelligence ; Automation ; Pipeline (software) ; Nanotechnology ; Robotics ; Materials science ; Machine learning ; Robot ; Engineering ; Mechanical engineering Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science
Authors: Kevin Maik Jablonka | Andrew Rosen | Aditi S. Krishnapriyan | Berend Smit
Source: ACS Central Science (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

An Ecosystem for Digital Reticular Chemistry The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry. Expediting ; Computer science ; Field (mathematics) ; Data science ; Reticular connective tissue ; Process (computing) ; Nanotechnology ; Chemistry ; Systems engineering ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Nilima Maji | Harmanjit Singh Dosanjh
Source: Magnetochemistry (review, 2023-06-14)
Field: Chemistry — Organic Chemistry

Ferrite Nanoparticles as Catalysts in Organic Reactions: A Mini Review Ferrites have excellent magnetic, electric, and optical properties that make them an indispensable choice of material for a plethora of applications, such as in various biomedical fields, magneto–optical displays, rechargeable lithium batteries, microwave devices, internet technology, transformer cores, humidity sensors, high-frequency media, magnetic recordings, solar energy devices, and magnetic fluids. Recently, magnetically recoverable nanocatalysts are one of the most prominent fields of research as they can act both as homogeneous and heterogenous catalysts. Nano-ferrites provide a large surface area for organic groups to anchor, increase the product and decrease reaction time, providing a cost-effective method of transformation. Various organic reactions were reported, such as the photocatalytic decomposition of a different dye, alkylation, dehydrogenation, oxidation, C–C coupling, etc., with nano-ferrites as a catalyst. Metal-doped ferrites with Co, Ni, Mn, Cu, and Zn, along with the metal ferrites doped with Mn, Cr, Cd, Ag, Au, Pt, Pd, or lanthanides and surface modified with silica and titania, are used as catalysts in various organic reactions. Metal ferrites (MFe2O4) act as a Lewis acid and increase the electrophilicity of specific groups of the reactants by accepting electrons in order to form covalent bonds. Ferrite nanocatalysts are easily recoverable by applying an external magnetic field for their reuse without significantly losing their catalytic activities. The use of different metal ferrites in different organic transformations reduces the catalyst overloading and, at the same time, reduces the use of harmful solvents and the production of poisonous byproducts, hence, serving as a green method of chemical synthesis. This review provides insight into the application of different ferrites as magnetically recoverable nanocatalysts in different organic reactions and transformations. Nanomaterial-based catalyst ; Catalysis ; Materials science ; Chemical engineering ; Ferrite (magnet) ; Nanomaterials ; Nanoparticle ; Dehydrogenation ; Metal ; Nanotechnology ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Composite material ; Metallurgy Nanomaterials for catalytic reactions ; Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials
Authors: Alexander Hagg | Karl N. Kirschner
Source: Journal of Chemical Information and Modeling (review, 2023-07-19)
Field: Materials Science — Materials Chemistry

Open-Source Machine Learning in Computational Chemistry The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community. Computer science ; Python (programming language) ; License ; Source code ; Software ; Machine learning ; Open source ; Artificial intelligence ; MIT License ; Data science ; World Wide Web ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Suvo Banik | Debdas Dhabal | Henry Chan | Sukriti Manna | Mathew J. Cherukara | Valeria Molinero | Subramanian K. R. S. Sankaranarayanan
Source: npj Computational Materials (article, 2023-02-16)
Field: Materials Science — Materials Chemistry

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases. CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D, clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal) noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture. Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approach is material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces to microscale grain boundaries. Grain boundary ; Microscale chemistry ; Computer science ; Materials science ; Ranging ; Artificial neural network ; Artificial intelligence ; Graph ; Pattern recognition (psychology) ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: M. Ghorbani | Mario Boley | Philip N. H. Nakashima | N. Birbilis
Source: Journal of Magnesium and Alloys (article, 2023-10-01)
Field: Materials Science — Biomaterials

A machine learning approach for accelerated design of magnesium alloys. Part A: Alloy data and property space Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of Mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning (ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital Mg-alloy design. Cluster analysis ; Alloy ; Curse of dimensionality ; Materials science ; Field (mathematics) ; Representation (politics) ; Computer science ; Property (philosophy) ; Process (computing) ; Magnesium alloy ; Space (punctuation) ; Work (physics) ; Database ; Machine learning ; Mechanical engineering ; Metallurgy ; Engineering ; Mathematics Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science
Authors: Andrew McNaughton | Rajendra P. Joshi | Carter Knutson | Fnu Anubhav | Kevin J Luebke | Jeremiah P. Malerich | Peter B. Madrid | Neeraj Kumar
Source: Journal of Chemical Information and Modeling (article, 2023-02-27)
Field: Materials Science — Materials Chemistry

Machine Learning Models for Predicting Molecular UV–Vis Spectra with Quantum Mechanical Properties Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers. Quantum ; Spectral line ; Computer science ; Materials science ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Dhiman Ray | Enrico Trizio | Michele Parrinello
Source: The Journal of Chemical Physics (article, 2023-05-22)
Field: Chemistry — Spectroscopy

Deep learning collective variables from transition path ensemble The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to address this problem rely on the identification of the slow modes of the system, which are referred to as collective variables. Recently, machine learning methods have been used to learn the collective variables as functions of a large number of physical descriptors. Among many such methods, Deep Targeted Discriminant Analysis has proven to be useful. This collective variable is built from data harvested from short unbiased simulations in the metastable basins. Here, we enrich the set of data on which the Deep Targeted Discriminant Analysis collective variable is built by adding data from the transition path ensemble. These are collected from a number of reactive trajectories obtained using the On-the-fly Probability Enhanced Sampling flooding method. The collective variables thus trained lead to more accurate sampling and faster convergence. The performance of these new collective variables is tested on a number of representative examples. Linear discriminant analysis ; Computer science ; Variable (mathematics) ; Collective behavior ; Artificial intelligence ; Convergence (economics) ; Path (computing) ; Statistical physics ; Machine learning ; Mathematics ; Physics Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Peitao Liu | Jian-Tao Wang | Noah Avargues | Carla Verdi | Andreas Singraber | Ferenc Karsai | Xing‐Qiu Chen | Georg Kresse
Source: Physical Review Letters (article, 2023-02-17)
Field: Materials Science — Materials Chemistry

Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111) Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in surface sciences and catalysis. Despite its simplicity, it has posed great challenges to theoretical modeling. Pretty much all existing density functionals fail to accurately describe surface energies and CO adsorption site preference as well as adsorption energies simultaneously. Although the random phase approximation (RPA) cures these density functional theory failures, its large computational cost makes it prohibitive to study the CO adsorption for any but the simplest ordered cases. Here, we address these challenges by developing a machine-learned force field (MLFF) with near RPA accuracy for the prediction of coverage-dependent adsorption of CO on the Rh(111) surface through an efficient on-the-fly active learning procedure and a Δ-machine learning approach. We show that the RPA-derived MLFF is capable to accurately predict the Rh(111) surface energy and CO adsorption site preference as well as adsorption energies at different coverages that are all in good agreement with experiments. Moreover, the coverage-dependent ground-state adsorption patterns and adsorption saturation coverage are identified. Adsorption ; Materials science ; Physics ; Atomic physics ; Physical chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Haotian Qiu | Shilie Pan | Miriding Mutailipu
Source: Fundamental Research (review, 2023-12-19)
Field: Chemistry — Inorganic Chemistry

Fluorination strategy toward chemical and functional modification Fluorination is a powerful strategy for chemical and functional modification of materials because the introduction of fluorine atoms can alter the physical, chemical and electronic properties of a material and thereby result in improved key properties. The fluorination strategy has enabled the modification of material chemistry and properties at the microscale of fluorooxysalts, providing a diversity previously unattainable in oxysalts. Here, we review the recent progress, status, future opportunities, and challenges with concern of the chemical and material aspects for fluorooxysalts that contain fluorine-involved M-F bonds. The entire evolution of fluorooxysalts-from synthesis to structural chemistry and their functionality-is examined from the perspective of the polyanion. This review paper details how qualities of optical crystals, battery materials, and inorganic framework materials can be greatly enhanced by understanding the chemistry of inorganic fluorooxysalts. This review centers on the critical role that fluorine plays in the synthesis, characterization, and physical properties of these materials. Chemistry ; Psychology ; Biochemical engineering ; Engineering Inorganic Fluorides and Related Compounds ; Crystal Structures and Properties ; Fluorine in Organic Chemistry
Authors: Liping Liao | Evgeniya Kovalska | Jakub Regner | Qunliang Song | Zdeněk Sofer
Source: Small (review, 2023-09-20)
Field: Materials Science — Materials Chemistry

Two‐Dimensional Van Der Waals Thin Film and Device In the rapidly evolving field of thin-film electronics, the emergence of large-area flexible and wearable devices has been a significant milestone. Although organic semiconductor thin films, which can be manufactured through solution processing, have been identified, their utility is often undermined by their poor stability and low carrier mobility under ambient conditions. However, inorganic nanomaterials can be solution-processed and demonstrate outstanding intrinsic properties and structural stability. In particular, a series of two-dimensional (2D) nanosheet/nanoparticle materials have been shown to form stable colloids in their respective solvents. However, the integration of these 2D nanomaterials into continuous large-area thin with precise control of layer thickness and lattice orientation still remains a significant challenge. This review paper undertakes a detailed analysis of van der Waals thin films, derived from 2D materials, in the advancement of thin-film electronics and optoelectronic devices. The superior intrinsic properties and structural stability of inorganic nanomaterials are highlighted, which can be solution-processed and underscor the importance of solution-based processing, establishing it as a cornerstone strategy for scalable electronic and optoelectronic applications. A comprehensive exploration of the challenges and opportunities associated with the utilization of 2D materials for the next generation of thin-film electronics and optoelectronic devices is presented. van der Waals force ; Materials science ; Hamaker constant ; Thin film ; Chemical physics ; Nanotechnology ; Van der Waals radius ; Chemistry ; Molecule ; Organic chemistry 2D Materials and Applications ; Nanowire Synthesis and Applications ; Graphene research and applications
Authors: Aamod V. Desai | Erlantz Lizundia | Andrea Laybourn | Daniel N. Rainer | A. Robert Armstrong | Russell E. Morris | Stefan Wuttke | Romy Ettlinger
Source: Advanced Functional Materials (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Green Synthesis of Reticular Materials Abstract To help ensure a prosperous future on Earth for coming generations, academia and industry need to transform the way they plan and carry out the synthesis of novel materials to make them more environmentally sustainable. In particular, the field of reticular materials, i.e., metal‐organic frameworks, zeolitic imidazolate frameworks, and covalent organic frameworks, has great potential to outperform other materials and revolutionize various fields of applications. This review highlights several key aspects from the choice of their starting materials, solvents and synthetic methodologies that fall under the umbrella of the Green Chemistry principles, and incorporates a Circular Economy perspective by providing relevant strategies such as reuse, regeneration, or recycling to maximize the value of the Earth's available resources. Moreover, it will shed light on the life cycle assessment results of selected reticular materials and consider how constraints imposed by Green Chemistry principles, life cycle assessment metrics, and circular patterns will shape the future rational sustainable design and discovery of reticular materials. Circular economy ; Reuse ; Nanotechnology ; Biochemical engineering ; Life-cycle assessment ; Reticular connective tissue ; Sustainability ; Management science ; Zeolitic imidazolate framework ; Computer science ; Construction engineering ; Materials science ; Engineering ; Metal-organic framework ; Chemistry ; Production (economics) ; Waste management ; Ecology Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications