jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rachel R. Chan | Jacob Pietryga | Kaitlin M. Landy | Kyle J. Gibson | Chad A. Mirkin
Source: ACS Nano (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Microcrystal Growth Pathways Investigated with Machine Learning Segmentation and Classification in Scanning Electron Microscopy Advances in electron microscopy have revolutionized material characterization on the nano- and microscales, providing important insights into local ordering, structure, and size and quality distributions. While shape and size can be rigorously quantified through microscopy, it is often limited to local structure analysis and fails to describe bulk sample quality. Herein, a flexible machine learning (ML) tool is described that can segment and classify faceted crystals in scanning electron microscopy (SEM) micrographs to determine sample quality through the crystal size and product distribution. As a case study, this tool was applied to investigate crystal growth pathways (classical nucleation and growth compared to nonclassical growth) in DNA-mediated nanoparticle assembly through size and product (single crystal, fused crystal, or noncrystal) distribution of samples containing over 13000 colloidal crystal products. Strong DNA bond strengths (controlled by DNA sequence) lead to fast nucleation that exhausts the monomer concentration, resulting in smaller colloidal crystals. Alternatively, increased thermal energy and crystallization time lead to nonclassical crystallization pathways (coalescence) that result in larger colloidal crystals. This tool is useful since experimental conditions can now be deliberately identified to control colloidal crystal size and size distribution, important considerations for researchers interested in designing and synthesizing colloidal crystal metamaterials. Nucleation ; Crystallization ; Colloidal crystal ; Materials science ; Crystal growth ; Crystal (programming language) ; Scanning electron microscope ; Coalescence (physics) ; Nanotechnology ; Colloid ; Crystallography ; Chemical physics ; Chemistry ; Computer science ; Physics ; Physical chemistry Machine Learning in Materials Science ; Iron oxide chemistry and applications ; Spectroscopy and Quantum Chemical Studies
Authors: Feiteng Wang | Jiaxin Zhu | Chang Liu | Ke Xiong | Xiandong Liu | Jun Cheng
Source: Chemical Science (article, 2024-12-23)
Field: Chemistry — Electrochemistry

Spatial correlation of desorption events accelerates water exchange dynamics at Pt/water interfaces The altered solvation structures and dynamical properties of water molecules at the metal/water interfaces will affect the elementary step of an electrochemical process. Simulating the interfacial structure and dynamics with a realistic representation will provide us with a solid foundation to make a connection with experimental studies. To surmount the accuracy-efficiency tradeoff and provide dynamical insights, we use state-of-the-art machine learning molecular dynamics (MLMD) to study the water exchange dynamics, which are fundamental to adsorption/desorption and electrochemical reaction steps. We reproduce interfacial structures consistent with <i>ab initio</i> molecular dynamics (AIMD) results and obtain diffusion and reorientation dynamics in agreement with the experiment. We show that the hydrogen bonds at the interface become stronger than those in bulk water, which makes the diffusion, reorientation, and hydrogen-bond dynamics slower. Our findings reveal that the spatial correlation of desorption events, driven by the breaking and making of hydrogen bonds, accelerates water exchange dynamics. These dynamics occur on timescales of several hundred picoseconds at 337 K and 302 K. We take a solid step forward toward studying the <i>in situ</i> interface water dynamics and attribute the fast water exchange dynamics to the spatial correlation of the desorption events. Desorption ; Dynamics (music) ; Molecular dynamics ; Chemical physics ; Correlation ; Spatial correlation ; Environmental science ; Materials science ; Chemistry ; Physics ; Physical chemistry ; Computational chemistry ; Adsorption ; Mathematics ; Geometry ; Statistics Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Electrocatalysts for Energy Conversion
Authors: Jisu Jung | Hyungmin An | Jinhee Lee | Seungwu Han
Source: Journal of Chemical Theory and Computation (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Modified Activation-Relaxation Technique (ARTn) Method Tuned for Efficient Identification of Transition States in Surface Reactions Exploring potential energy surfaces (PES) is essential for unraveling the underlying mechanisms of chemical reactions and material properties. While the activation-relaxation technique (ARTn) is a state-of-the-art method for identifying saddle points on PES, it often faces challenges in complex energy landscapes, especially on surfaces. In this study, we introduce iso-ARTn, an enhanced ARTn method that incorporates constraints on an orthogonal hyperplane and employs an adaptive active volume. By leveraging a neural network potential (NNP) to conduct an exhaustive saddle point search on the Pt(111) surface with 0.3 monolayers of surface oxygen coverage, iso-ARTn achieves a success rate that is 8.2% higher than the original ARTn, with 40% fewer force calls. Moreover, this method effectively finds various saddle points without compromising the success rate. Combined with kinetic Monte Carlo simulations for event table construction, iso-ARTn with NNP demonstrates the capability to reveal structures consistent with experimental observations. This work signifies a substantial advancement in the investigation of PES, enhancing both the efficiency and breadth of saddle point searches. Identification (biology) ; Relaxation (psychology) ; Transition (genetics) ; Transition state ; Chemical physics ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Neuroscience ; Psychology ; Catalysis ; Biology ; Biochemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Yifan Wang | Benjamin R. Schrank | Wen Jiang | Betty Y.S. Kim
Source: Nature Biomedical Engineering (article, 2024-09-13)
Field: Materials Science — Biomaterials

Learning what keeps nanomedicines in tumours Computer science ; Medicine Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods ; Graphene and Nanomaterials Applications
Authors: Zubair Sadiq | Wenhong Yang | Md Mostakim Meraz | Weisheng Yang | Wen‐Hua Sun
Source: Molecules (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression <i>(RR)</i> model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of <i>R</i><sup>2</sup><i>=</i> 0.952 and a cross-validation value of <i>Q</i><sup>2</sup><i>=</i> 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the <i>RR</i> model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity. Catalysis ; Steric effects ; Linear regression ; Anticipation (artificial intelligence) ; Correlation coefficient ; Chemistry ; Interpretation (philosophy) ; Quantitative structure–activity relationship ; Cobalt ; Molecular descriptor ; Mathematics ; Computer science ; Artificial intelligence ; Machine learning ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Organometallic Complex Synthesis and Catalysis
Authors: Junlin Yu | Yun Wang | Jian Peng | Jingwei Wu | Cong Zhou | Genliang Li
Source: Fundamental Research (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Geometric deep learning-enabled metal-binding site identification and grafting Metal-binding sites can participate in enzymes’ catalytic reactions, as well as in protein folding, stability, and protein-protein interactions. In-depth research on metal-binding sites can aid in identifying new protein functions and designing new metalloproteins. We present MeSiteIG, a geometric deep learning-driven bifunctional tool with capabilities for both Metal-binding Site Identification and Grafting, established based on the geometric conservation of metal-binding sites. It comprises three main modules: residue triplet searcher (ResTriS), metal-binding site identifier (MeSI), and metal-binding site alignment (MeSA). As the core module, MeSI adopts E3-equivarient graph neural networks to predict potential metal-binding site residues, without the coordinating residue types and side-chain information. MeSI achieved superior performance on the independent test set, with an impressive speed of approximately 300 samples per second. Using MeSiteIG, we identified previously neglected protein metal-binding sites and potential protein bimetallic sites. Furthermore, it could graft metal sites onto antibody surfaces and protein pockets, creating new potentially valuable metalloproteins. Identification (biology) ; Grafting ; Artificial intelligence ; Metal ; Computer science ; Pattern recognition (psychology) ; Materials science ; Metallurgy ; Biology ; Botany ; Composite material Corrosion Behavior and Inhibition ; Advanced Surface Polishing Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: A. B. Abdel-Aziz | Nora Mohamed | Reem M. El-taweel | Shimaa Husien | Yung‐Tse Hung | Lobna A. Said | Irene S. Fahim | Ahmed G. Radwan
Source: Materials Research Express (article, 2024-05-21)
Field: Chemistry — Organic Chemistry

Crystal violet removal using algae-based activated carbon and its composites with bimetallic Fe<sup>0</sup>-Cu Abstract The textile industry is considered a source of pollution because of the discharge of dye wastewater. The dye wastewater effluent has a significant impact on the aquatic environment. According to the World Bank, textile dyeing, and treatment contribute 17 to 20% of the pollution of water. This paper aims to prepare the bimetallic nano zero-valent iron-copper (Fe 0 -Cu), algae-activated carbon, and their composites (AC-Fe 0 -Cu), which are employed as adsorbents. In this paper, Synthetic adsorbents are prepared and examined for the adsorption and removal of soluble cationic crystal violet (CV) dye. The influence of synthetic adsorbents on the adsorption and removal of soluble cationic crystal violet (CV) dye is investigated using UV-V spectroscopy at different pH (3–10), time intervals (15–180) min, and initial dye concentrations (50–500 ppm). Raw algae exhibit an impressive 96.64% removal efficiency under the following conditions: pH 7, contact time of 180 min, rotational speed of 120 rpm, temperature range of 25 °C–30 °C, concentration of 300 ppm in the CV dye solution, and a dose of 4 g l −1 of raw algae adsorbent. The best removal efficiencies of Raw algae Fe 0 -Cu, and H 3 PO 4 chemical AC-Fe 0 -Cu are 97.61 % and 97.46 %, respectively, at pH = 7, contact time = 150 min, rotational speed = 120 rpm, T = (25–30) °C, concentration = 75 ppm of CV dye solution, and 1.5 g l −1 doses of raw algae F e0-Cu adsorbent and 1 g l −1 dose of H 3 PO 4 chemical AC-Fe 0 -Cu adsorbent. The maximum amounts ( q max ) of Bi-RA and RA adsorbed for the adsorption process of CV are 85.92 mg g −1 and 1388 mg g −1 , respectively. The Bi-H 3 A-AC model, optimized using PSO, demonstrates superior performance, with the highest adsorption capacity estimated at 83.51 mg g −1 . However, the Langmuir model predicts a maximum adsorption capacity ( q e ) of 275.6 mg g −1 for the CV adsorption process when utilizing Bi-H 3 A-AC. Kinetic and isothermal models are used to fit the data of time and concentration experiments. DLS, zeta potential, FT-IR, XRD, and SEM are used to characterize the prepared materials. Response surface methodology (RSM) is used to model the removal efficiency and then turned into a numerical optimization approach to determine the ideal conditions for improving removal efficiency. An artificial neural network (ANN) is also used to model the removal efficiency. Crystal violet ; Adsorption ; Activated carbon ; Nuclear chemistry ; Effluent ; Wastewater ; Algae ; Chemistry ; Dyeing ; Cationic polymerization ; Materials science ; Environmental engineering ; Organic chemistry ; Botany Nanomaterials for catalytic reactions ; Environmental remediation with nanomaterials ; Adsorption and biosorption for pollutant removal
Authors: Xujie Gong | Ruichao Lei | Ruize Sun | Xue Jiang | Yanjing Su | Yu Yan
Source: Materials Genome Engineering Advances (article, 2024-05-01)
Field: Materials Science — Metals and Alloys

An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information Abstract Accurately and quickly predicting hydrogen embrittlement performance is critical for the service of metal materials. However, due to multi‐source heterogeneity, existing hydrogen embrittlement data are missing, making it impractical to train reliable machine learning models. In this study, we proposed an ensemble learning training strategy for missing data based on the Adaboost algorithm. This method introduced a mask matrix with missing data and enabled each round of training to generate sub‐datasets, considering missing value information. The strategy first trained a subset of features based on the existing dataset and a selected method and continuously focused on the combination of features with the highest error for iterative training, where the mask matrix of the missing data was used as the input to fit the weights of each base learner using a neural network. Compared with directly modeling on highly sparse data, the predictive ability of this strategy was significantly improved by approximately 20%. In addition, in the testing of new samples, the predicted mean absolute error of the new model was successfully reduced from 0.2 to 0.09. This strategy offers good adaptability to the hydrogen embrittlement sensitivity of different sizes and can avoid interference from feature importance caused by filling data. Missing data ; Artificial neural network ; AdaBoost ; Computer science ; Ensemble learning ; Hydrogen embrittlement ; Artificial intelligence ; Pattern recognition (psychology) ; Data mining ; Machine learning ; Materials science Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Lorenzo Monacelli | Nicola Marzari
Source: arXiv (Cornell University) (preprint, 2024-12-02)
Field: Materials Science — Materials Chemistry

Electrostatic interactions in atomistic and machine-learned potentials for polar materials Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions. This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO$_3$). Polar ; Chemical physics ; Materials science ; Nanotechnology ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Pavel A. Galenko-Yaroshevsky | I. Yu. Torshin | А. Н. Громов | О. А. Громова | Р. А. Мурашко | A. V. Zelenskaya | Lusine O. Alukhanyan | Olga V. Shelemekh
Source: FARMAKOEKONOMIKA Modern Pharmacoeconomics and Pharmacoepidemiology (article, 2024-03-19)
Field: Chemistry — Organic Chemistry

Chemoreactomic analysis of acyzole in comparison with zinc derivatives of nonsteroidal anti-inflammatory drugs Background. Nonsteroidal anti-inflammatory drugs (NSAIDs) are characterized by ulcerogenic effects and used for effective and safe pharmacotherapy of inflammation and pain. The zinc-containing drug acyzole is a promising anti-inflammatory drug that potentially does not have this drawback. Objective: to conduct chemoreactomic modeling of the pharmacological effects of acyzole and zinc derivatives of well-known NSAIDs (diclofenac, nimesulide, ketorolac). Material and methods . The analysis of the pharmacological capabilities of NSAIDs was based on a chemoinformatic approach, i.e. comparing the chemical structure of the studied molecules with the structures of millions of other molecules with established molecular pharmacological properties. The analysis procedure was based on the latest machine learning technologies developed in the theory of topological and metric analysis of feature descriptions. Results. It was shown that acyzole might have an anti-inflammatory effect due to its impact on the activity of cytokines and, partly, on the metabolism of prostaglandins and leuktrienes. The central effects of acyzole are comparable to those of zinc-NSAIDs. The analgesic effect of acyzole may be associated with kinin receptors inhibition, and weak antihistamine and antinociceptin effects. Acizol may also exhibit a gastroprotective effect. It was established that acyzole, to a lesser extent than the reference molecules, negatively affected the metabolism of vitamins and microelements. Conclusion. Chemoreactomic profiling of acyzole indicates prospects for its use as an anti-inflammatory drug. Nimesulide ; Pharmacology ; Diclofenac ; Drug ; Anti-inflammatory ; Nonsteroidal ; Analgesic ; Antihistamine ; Chemistry ; Medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Inflammatory mediators and NSAID effects
Authors: Vincenzo Fazio | Ali D. Malay | Keiji Numata | Nicola M. Pugno | Giuseppe Puglisi
Source: Advanced Functional Materials (article, 2024-12-26)
Field: Materials Science — Biomaterials

A Physically‐Based Machine Learning Approach Inspires an Analytical Model for Spider Silk Supercontraction Abstract Scientific and industrial interest in spider silk stems from its remarkable properties, including supercontraction—an activation effect induced by wetting. Understanding the underlying molecular scale mechanisms is then also crucial for biomimetic applications. In this study, it is illustrated how the effective integration of physically‐based machine learning with scientific interpretations can lead to significant physical insights and enhance the predictive power of an existing microstructure‐inspired model. A symbolic data modeling technique, known as Evolutionary Polynomial Regression (EPR), is employed, which integrates regression capabilities with the genetic programming paradigm, enabling the derivation of explicit analytical formulas for deducing structure‐function relationships emerging across different scales, to investigate the impact of protein primary structures on supercontraction. This analysis is based on recent multiscale experimental data encompassing a diverse range of scales and a wide variety of different spider silks. Specifically, this analysis reveals a correlation between supercontraction and the repeat length of the MaSp2 protein as well as the polyalanine region of MaSp1. Straightforward microstructural interpretations that align with experimental observations are proposed. The MaSp2 repeat length governs the cross‐links that stabilize amorphous chains in dry conditions. When hydrated, these cross‐links are disrupted, leading to entropic coiling and fiber contraction. Furthermore, the length of the polyalanine region in MaSp1 plays a critical role in supercontraction by restricting the extent of crystal misalignment necessary to accommodate the shortening of the soft regions. The validation of the model is accomplished by comparing experimental data from the Silkome database with theoretical predictions derived from both the machine learning and the proposed model. The enhanced model offers a more comprehensive understanding of supercontraction and establishes a link between the primary structure of silk proteins and their macroscopic behavior, thereby advancing the field of biomimetic applications. Materials science ; Spider silk ; SILK ; Spider ; Polymer science ; Artificial intelligence ; Nanotechnology ; Computer science ; Composite material ; Physics Silk-based biomaterials and applications ; Adhesion, Friction, and Surface Interactions ; Advanced Materials and Mechanics
Authors: I. S. Popov | А. А. Валеева | Andrey N. Enyashin
Source: Chemical Physics (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Identifying stable Nb-O clusters using evolutionary algorithm and DFT: A foundation for machine learning potentials Foundation (evidence) ; Chemistry ; Algorithm ; Artificial intelligence ; Computer science ; Archaeology ; Geography Machine Learning in Materials Science ; Semiconductor materials and devices ; Ferroelectric and Negative Capacitance Devices
Authors: Weiming Liu | Lingxin YOU | S. Wang | Jie Li | Zhiyong Chen | Buchun Si | Yasir Iqbal | Shuai Xue | Tongcheng Fu | Zili Yi | Meng Li
Source: Agronomy (article, 2024-06-11)
Field: Materials Science — Biomaterials

Screening of Miscanthus Genotypes for Sustainable Production of Microcrystalline Cellulose and Cellulose Nanocrystals Miscanthus spp. has been regarded as a promising industrial plant for the sustainable production of bio-based materials. To assess its potential for microcrystalline cellulose (MCC) and cellulose nanocrystals (CNCs) production, 50 representative clones of M. sinensis and M. floridulus were selected from a nationwide collection showcasing the extensive diversity of germplasm resources. Descriptive analysis indicates that the dry biomass weight of M. floridulus is advantageous whereas M. sinensis demonstrates higher MCC and CNCs yields as well as a smaller CNCs particle size. Correlation analyses indicated that MCC yield is solely influenced by the cellulose content whereas the yield of CNCs is affected by both the cellulose content and CrI. Comparative analyses of the chemical composition, physical features (degree of polymerization, crystalline index, particle size distribution and zeta potential), and scanning electron microscopy indicated that the MCC and CNCs extracted from M. sinensis and M. floridulus exhibited remarkable stability and quality. Additionally, the CNCs derived from M. sinensis and M. floridulus exhibited a distinctive ball-shaped structure. Notably, machine learning has demonstrated its efficacy and effectiveness in the high-throughput screening of large populations of Miscanthus spp. for predicting the yield of MCC and CNCs. Our results have also laid the theoretical foundation for the exploration, cultivation, and genetic breeding of M. sinensis and M. floridulus germplasm resources with the purpose of MCC and CNCs preparation. Microcrystalline cellulose ; Cellulose ; Germplasm ; Miscanthus ; Materials science ; Agronomy ; Biomass (ecology) ; Bioenergy ; Biofuel ; Chemistry ; Biology ; Biotechnology ; Organic chemistry Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Advanced Data Storage Technologies