jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Teresa M. Karl | Samir Bouayad‐Gervais | Julian A. Hueffel | Theresa Sperger | Sebastian Wellig | Sherif J. Kaldas | Uladzislava Dabranskaya | Jas S. Ward | Kari Rissanen | Graham J. Tizzard | Franziska Schoenebeck
Source: Journal of the American Chemical Society (article, 2023-07-06)
Field: Chemistry — Organic Chemistry

Machine Learning-Guided Development of Trialkylphosphine Ni<sup>(I)</sup> Dimers and Applications in Site-Selective Catalysis Owing to the unknown correlation of a metal's ligand and its resulting preferred speciation in terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts remains challenging. With the goal to accelerate the identification of suitable ligands that form trialkylphosphine-derived dihalogen-bridged Ni<sup>(I)</sup> dimers, we herein employed an assumption-based machine learning approach. The workflow offers guidance in ligand space for a desired speciation without (or only minimal) prior experimental data points. We experimentally verified the predictions and synthesized numerous novel Ni<sup>(I)</sup> dimers as well as explored their potential in catalysis. We demonstrate C-I selective arylations of polyhalogenated arenes bearing competing C-Br and C-Cl sites in under 5 min at room temperature using 0.2 mol % of the newly developed dimer, [Ni<sup>(I)</sup>(μ-Br)PAd<sub>2</sub>(<i>n</i>-Bu)]<sub>2</sub>, which is so far unmet with alternative dinuclear or mononuclear Ni or Pd catalysts. Chemistry ; Catalysis ; Ligand (biochemistry) ; Dimer ; Combinatorial chemistry ; Stereochemistry ; Computational chemistry ; Organic chemistry Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Haobing Hou | Jianfeng Wang | Ye Li | Shijie Zhu | Liguo Wang | Shaokang Guan
Source: Materials Letters (article, 2023-05-27)
Field: Materials Science — Biomaterials

Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning Gradient boosting ; Materials science ; Support vector machine ; Ultimate tensile strength ; Random forest ; Machine learning ; Regression ; Ensemble learning ; Kriging ; Magnesium ; Regression analysis ; Boosting (machine learning) ; Predictive modelling ; Artificial intelligence ; Computer science ; Metallurgy ; Mathematics ; Statistics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Corrosion Behavior and Inhibition
Authors: Athanassios Z. Panagiotopoulos | Shuwen Yue
Source: The Journal of Physical Chemistry B (review, 2023-01-06)
Field: Materials Science — Materials Chemistry

Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. While direct <i>ab initio</i> simulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics. Electrolyte ; Chemical physics ; Aqueous solution ; Statistical physics ; Water model ; Molecular dynamics ; Polarizability ; Work (physics) ; Ab initio ; Charge (physics) ; Thermodynamics ; Chemistry ; Polarization (electrochemistry) ; Materials science ; Computational chemistry ; Physics ; Physical chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Feng-Yang Wu | Jin Yin | Shao-Chen Chen | Xueqin Gao | Li Zhou | Ying Lü | Jun Lei | Gan‐Ji Zhong | Zhong‐Ming Li
Source: Polymer (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Application of machine learning to reveal relationship between processing-structure-property for polypropylene injection molding Polypropylene ; Molding (decorative) ; Materials science ; Injection molding machine ; Artificial intelligence ; Machine learning ; Polymer ; Composite material ; Algorithm ; Computer science ; Mechanical engineering ; Mold ; Biological system ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Computational Drug Discovery Methods
Authors: Ludwig Schneider | Dylan J. Walsh | Bradley D. Olsen | Juan Pablo
Source: Digital Discovery (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Generative BigSMILES: an extension for polymer informatics, computer simulations &amp; ML/AI Generative BigSMILES (G-BigSMILES) serves as a tool, providing a concise description that enables the generation of realistic ensembles of polymeric molecules, facilitating automated simulations and machine learning. Extension (predicate logic) ; Generative grammar ; Computer science ; Informatics ; Artificial intelligence ; Programming language ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Wei‐Cheng Yan | Tao Dong | Yin‐Ning Zhou | Zheng‐Hong Luo
Source: Chemical Engineering Science (article, 2023-01-11)
Field: Materials Science — Polymers and Plastics

Computational modeling toward full chain of polypropylene production: From molecular to industrial scale Polypropylene ; Scale (ratio) ; Process (computing) ; Materials science ; Process engineering ; Polymer ; SCALE-UP ; Computational model ; Biochemical engineering ; Production (economics) ; Process design ; Computer science ; Mechanical engineering ; Engineering ; Composite material ; Simulation ; Physics Polymer crystallization and properties ; Fuel Cells and Related Materials ; biodegradable polymer synthesis and properties
Authors: Yuxin Xie | Yu Zhang | Lei He | Charles Q. Jia | Qiuxiang Yao | Ming Sun | Xiaoxun Ma
Source: Applied Catalysis A General (article, 2023-03-23)
Field: Chemistry — Inorganic Chemistry

Anti-deactivation of zeolite catalysts for residue fluid catalytic cracking Catalysis ; Zeolite ; Petrochemical ; Fluid catalytic cracking ; Cracking ; Raw material ; Chemical engineering ; Refining (metallurgy) ; Materials science ; Catalyst poisoning ; Catalyst support ; Waste management ; Chemistry ; Organic chemistry ; Metallurgy ; Composite material ; Engineering Zeolite Catalysis and Synthesis ; Hydrocarbon exploration and reservoir analysis ; Petroleum Processing and Analysis
Authors: William Dawson | Kathryn L. Shelley | Jordan M. Fletcher | D. Arne Scott | Lucia Lombardi | Guto G. Rhys | Tania J. LaGambina | Ulrike Obst | Antony J. Burton | Jessica A. Cross | George J. Davies | Freddie J. O. Martin | Francis J. Wiseman | R.L. Brady | David G. Tew | Christopher W. Wood | Derek N. Woolfson
Source: Nature Communications (article, 2023-01-24)
Field: Materials Science — Biomaterials

Differential sensing with arrays of de novo designed peptide assemblies Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings. Analyte ; Biomolecule ; Fluorophore ; Nanotechnology ; Differential (mechanical device) ; Peptide ; Biophysics ; Fluorescence ; Biological system ; Chemistry ; Computational biology ; Computer science ; Materials science ; Biology ; Physics ; Biochemistry ; Chromatography Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Lipid Membrane Structure and Behavior
Authors: Artem O. Surov | Anna G. Ramazanova | Alexander P. Voronin | Ksenia V. Drozd | Andrei V. Churakov | German L. Perlovich
Source: Pharmaceutics (article, 2023-03-03)
Field: Chemistry — Physical and Theoretical Chemistry

Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals In this study, the existing set of carbamazepine (CBZ) cocrystals was extended through the successful combination of the drug with the positional isomers of acetamidobenzoic acid. The structural and energetic features of the CBZ cocrystals with 3- and 4-acetamidobenzoic acids were elucidated via single-crystal X-ray diffraction followed by QTAIMC analysis. The ability of three fundamentally different virtual screening methods to predict the correct cocrystallization outcome for CBZ was assessed based on the new experimental results obtained in this study and data available in the literature. It was found that the hydrogen bond propensity model performed the worst in distinguishing positive and negative results of CBZ cocrystallization experiments with 87 coformers, attaining an accuracy value lower than random guessing. The method that utilizes molecular electrostatic potential maps and the machine learning approach named CCGNet exhibited comparable results in terms of prediction metrics, albeit the latter resulted in superior specificity and overall accuracy while requiring no time-consuming DFT computations. In addition, formation thermodynamic parameters for the newly obtained CBZ cocrystals with 3- and 4-acetamidobenzoic acids were evaluated using temperature dependences of the cocrystallization Gibbs energy. The cocrystallization reactions between CBZ and the selected coformers were found to be enthalpy-driven, with entropy terms being statistically different from zero. The observed difference in dissolution behavior of the cocrystals in aqueous media was thought to be caused by variations in their thermodynamic stability. Gibbs free energy ; Enthalpy ; Chemistry ; Thermodynamics ; Dissolution ; Hydrogen bond ; Aqueous solution ; Molecule ; Computational chemistry ; Physical chemistry ; Organic chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Saad Aldin Mohamed | Daohui Zhao | Jianwen Jiang
Source: Communications Materials (article, 2023-10-03)
Field: Chemistry — Inorganic Chemistry

Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture Abstract Metal–organic frameworks (MOFs) have been considered a unique class of hybrid materials for a wide variety of potential applications. With the existence of almost infinite MOFs, high-throughput computational screening (HTCS) is a robust technique to accelerate the search for promising MOFs. However, conventional HTCS studies reported in the literature neglect the stability of MOFs, which must be considered for practical applications. Here we integrate four stability metrics (thermodynamic, mechanical, thermal, and activation) with HTCS to identify top-performing, synthesizable, and stable hypothetical MOFs for CO 2 capture. The thermodynamic and mechanical stabilities are evaluated through molecular dynamics simulations, while the activation and thermal stabilities are predicted using machine learning models. Finally, we identify top-performing hypothetical MOFs satisfying all these stability metrics. This study underlines the central importance of integrating stability metrics when screening MOFs for applications. Stability (learning theory) ; Throughput ; Metal-organic framework ; Thermal stability ; Computer science ; Class (philosophy) ; Nanotechnology ; Biochemical engineering ; Materials science ; Biological system ; Chemistry ; Artificial intelligence ; Machine learning ; Engineering ; Biology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Asahi Tomitaka | Arti Vashist | Nagesh Kolishetti | Madhavan Nair
Source: Nanoscale Advances (review, 2023-01-01)
Field: Materials Science — Biomaterials

Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases Magnetic nanoparticles possess unique properties distinct from other types of nanoparticles developed for biomedical applications. Their unique magnetic properties and multifunctionalities are especially beneficial for central nervous system (CNS) disease therapy and diagnostics, as well as targeted and personalized applications using image-guided therapy and theranostics. This review discusses the recent development of magnetic nanoparticles for CNS applications, including Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and drug addiction. Machine learning (ML) methods are increasingly applied towards the processing, optimization and development of nanomaterials. By using data-driven approach, ML has the potential to bridge the gap between basic research and clinical research. We review ML approaches used within the various stages of nanomedicine development, from nanoparticle synthesis and characterization to performance prediction and disease diagnosis. Nanomedicine ; Bridge (graph theory) ; Artificial intelligence ; Computer science ; Nanoparticle ; Machine learning ; Nanotechnology ; Medicine ; Materials science ; Surgery Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Digital Imaging for Blood Diseases
Authors: Yiran Ding | Chenyang Wang | Mengqi Zeng | Lei Fu
Source: Advanced Materials (review, 2023-09-18)
Field: Materials Science — Materials Chemistry

Atomic Manufacturing of Advanced Nanomaterials The quest for cutting-edge materials and devices has necessitated elevated demands for manufacturing methodologies. Atomic manufacturing involves the meticulous design and fabrication of materials and devices at the atomic level, a process that has been facilitated by advancements in comprehending and manipulating atomic behavior. The attainment of atomic manufacturing is dependent on the capacity to precisely manipulate atoms, directing their reactions at will. In this review, five methodologies of atomic fabrication encompassing atomic manipulation, atomic programming, atomic epitaxy, atomic confinement, and atomic assembly are elucidated. Based on this, the utilization of atomic manufacturing in the most advanced domains including energy conversion, energy storage, quantum information technology, and optoelectronic devices is elucidated. Finally, the current challenges and outlook on the forthcoming advancement of atomic manufacturing are presented. Nanotechnology ; Materials science ; Nanomaterials ; Atomic energy ; Fabrication ; Atomic force microscopy ; Atomic layer deposition ; Thin film Electronic and Structural Properties of Oxides ; Quantum Dots Synthesis And Properties ; Copper-based nanomaterials and applications
Authors: Xing Peng | Long Chen | Yifan Liu | Chao Liu | Honglan Huang | Jinbo Fan | Pan Xiong | Junwu Zhu
Source: Chemical Synthesis (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Strain engineering of two-dimensional materials for energy storage and conversion applications Two-dimensional (2D) materials have garnered much interest due to their exceptional optical, electrical, and mechanical properties. Strain engineering, as a crucial approach to modulate the physicochemical characteristics of 2D materials, has been widely used in various fields, especially for energy storage and conversion. Herein, the recent progress in strain engineering of 2D materials is summarized for energy storage and conversion applications. The fundamental understanding of strain in 2D materials is first described. Then, some synthetic methods for modulating the properties of 2D materials via strain engineering are introduced. Further, the applications of strain engineering of 2D materials in energy storage, photocatalysis, and electrocatalysis are discussed. Finally, the challenges and perspectives on strain engineering of 2D materials are also outlined. Strain engineering ; Energy storage ; Materials science ; Strain (injury) ; Energy transformation ; Molecular engineering ; Strain energy ; Nanotechnology ; Mechanical engineering ; Engineering ; Structural engineering ; Optoelectronics ; Finite element method ; Physics ; Thermodynamics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Yakubu Adekunle Alli | Peter Olusakin Oladoye | Abdulmujeeb T. Onawole | Hazleen Anuar | Sheriff Adewuyi | Olutobi Daniel Ogunbiyi | Karine Philippot
Source: Fuel (article, 2023-03-12)
Field: Chemistry — Inorganic Chemistry

Photocatalysts for CO2 reduction and computational insights Photocatalysis ; Density functional theory ; Semiconductor ; Materials science ; Adsorption ; Oxide ; Metal ; Desorption ; Nanotechnology ; Selectivity ; Reduction (mathematics) ; Catalysis ; Photochemistry ; Chemistry ; Computational chemistry ; Optoelectronics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Andrew Rosen | Sudarshan Vijay | Kristin A. Persson
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Free-atom-like d states beyond the dilute limit of single-atom alloys Through a data-mining and high-throughput density functional theory approach, we identify a diverse range of metallic compounds that are predicted to have transition metals with "free-atom-like" d states that are highly localized in terms of their energetic distribution. Design principles that favor the formation of localized d states are uncovered, among which we note that site isolation is often necessary but that the dilute limit, as in most single-atom alloys, is not a pre-requisite. Additionally, the majority of localized d state transition metals identified from the computational screening study exhibit partial anionic character due to charge transfer from neighboring metal species. Using CO as a representative probe molecule, we show that localized d states for Rh, Ir, Pd, and Pt tend to reduce the binding strength of CO compared to their pure elemental analogues, whereas this does not occur as consistently for the Cu binding sites. These trends are rationalized through the d-band model, which suggests that the significantly reduced d-band width results in an increased orthogonalization energy penalty upon CO chemisorption. With the multitude of inorganic solids that are predicted to have highly localized d states, the results of the screening study are likely to result in new avenues for heterogeneous catalyst design from an electronic structure perspective. Atom (system on chip) ; Limit (mathematics) ; Atomic physics ; Physics ; Chemistry ; Mathematics ; Computer science ; Mathematical analysis Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Advanced Chemical Physics Studies
Authors: Matteo Manica | Jannis Born | Joris Cadow | Dimitrios Christofidellis | Ashish Dave | Dean Clarke | Yves Gaëtan Nana Teukam | Giorgio Giannone | Samuel C. Hoffman | Matthew J. Buchan | Vijil Chenthamarakshan | Timothy P. Donovan | Hsiang Han Hsu | Federico Zipoli | Oliver Schilter | Akihiro Kishimoto | Lisa Hamada | Inkit Padhi | Karl Wehden | Lauren McHugh | Alexy Khrabrov | Payel Das | Seiji Takeda | John R. Smith
Source: npj Computational Materials (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Accelerating material design with the generative toolkit for scientific discovery Abstract With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design. Generative grammar ; Scientific discovery ; Computer science ; Data science ; Generative model ; Generative Design ; Scientific literature ; Artificial intelligence ; Engineering ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Tushar Kanti Das | Sayan Ganguly
Source: Foods (review, 2023-05-30)
Field: Materials Science — Materials Chemistry

Revolutionizing Food Safety with Quantum Dot–Polymer Nanocomposites: From Monitoring to Sensing Applications The present review article investigates the prospective utilisation of quantum dot-polymer nanocomposites in the context of ensuring food safety. The text pertains to the advancement of nanocomposites, encompassing their distinctive optical and electrical characteristics, and their prospective to transform the detection and perception of food safety risks. The article explores diverse methodologies for producing nanocomposites and underscores their potential utility in identifying impurities, microorganisms, and harmful substances in food. The article provides an overview of the challenges and limitations associated with the utilisation of nanocomposites in food safety applications, encompassing concerns regarding toxicity and the necessity for standardised protocols. The review article presents a comprehensive examination of the present research status in this area and underscores the potential of quantum dots-polymer nanocomposites in transforming food safety monitoring and sensing. Context (archaeology) ; Food safety ; Nanocomposite ; Nanotechnology ; Quantum dot ; Food packaging ; Polymer nanocomposite ; Risk analysis (engineering) ; Computer science ; Materials science ; Business ; Engineering ; Chemistry ; Food science ; Mechanical engineering ; Biology Carbon and Quantum Dots Applications ; Biosensors and Analytical Detection ; Advanced Nanomaterials in Catalysis