jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew C. Novick | Diana Cai | Quan Dong Nguyen | Roman Garnett | Ryan P. Adams | Eric S. Toberer
Source: Materials Horizons (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Probabilistic prediction of material stability: integrating convex hulls into active learning Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase diagrams does not neatly fit into traditional active learning approaches due to their global nature. Specifically, the thermodynamic stability of a material is not simply a function of its own energy, but rather requires energetic information from all other competing compositions and phases. Here we present convex hull-aware active learning (CAL), a novel Bayesian algorithm that chooses experiments to minimize the uncertainty in the convex hull. CAL prioritizes compositions that are close to or on the hull, leaving significant uncertainty in other compositions that are quickly determined to be irrelevant to the convex hull. The convex hull can thus be predicted with significantly fewer observations than approaches that focus solely on energy. Intrinsic to this Bayesian approach is uncertainty quantification in both the convex hull and all subsequent predictions (<i>e.g.</i>, stability and chemical potential). By providing increased search efficiency and uncertainty quantification, CAL can be readily incorporated into the emerging paradigm of uncertainty-based workflows for thermodynamic prediction. Convex hull ; Hull ; Probabilistic logic ; Regular polygon ; Stability (learning theory) ; Convex combination ; Process (computing) ; Object (grammar) ; Mathematical optimization ; Mathematics ; Computer science ; Materials science ; Algorithm ; Artificial intelligence ; Convex optimization ; Composite material ; Machine learning ; Geometry ; Programming language Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Max Großmann | Malte Grunert | Erich Runge
Source: npj Computational Materials (article, 2024-06-27)
Field: Materials Science — Materials Chemistry

A robust, simple, and efficient convergence workflow for GW calculations Abstract A robust, simple, and efficient convergence workflow for GW calculations in plane-wave-based codes is derived from more than 7000 GW calculations on a diverse dataset of 70 semiconducting and insulating solids divided into 60 bulk and 10 2D materials. The workflow can significantly accelerate material screening projects and high-precision single-system studies. Our method is based on two main results: The convergence of the two interdependent parameters in the numerical implementation of the dynamically screened Coulomb interaction W in a plane-wave basis set is accelerated by a ‘cheap first, expensive later’ coordinate search that maintains the same accuracy as a state-of-the-art convergence algorithm, but converges faster. In addition, we empirically establish the practical independence of the k-point grid and the aforementioned parameterization of W . Incorporating both results into one workflow dramatically speeds up convergence. Workflow ; Convergence (economics) ; Simple (philosophy) ; Computer science ; Grid ; Set (abstract data type) ; Independence (probability theory) ; Algorithm ; Plane wave ; Mathematical optimization ; Computational science ; Applied mathematics ; Physics ; Mathematics ; Geometry ; Optics ; Database Machine Learning in Materials Science ; Semiconductor materials and devices ; Non-Destructive Testing Techniques
Authors: Ton V. W. Janssens | Elisa Borfecchia | Kirill A. Lomachenko | Henrik Grönbeck | Gloria Berlier
Source: ChemCatChem (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

The [(NH<sub>3</sub>)<sub>4</sub>Cu<sub>2</sub>O<sub>2</sub>]<sup>2+</sup>‐Peroxo Complex as the Key Intermediate for NH<sub>3</sub>‐SCR Activity and Deactivation of Cu‐CHA Catalysts. Abstract In NH 3 ‐SCR over Cu‐CHA catalysts in the low‐temperature range 150–300 °C, the activation of oxygen occurs via oxidation of a pair of mobile (NH 3 ) 2 Cu I ‐complexes located in the cages of the zeolite. In this step, a reactive [(NH 3 ) 4 Cu 2 O 2 ] 2+ ‐peroxo complex (μ‐η 2 ,η 2 ‐peroxo diamino dicopper(II)‐complex) is formed. The chemistry of this complex determines several catalytic properties of the Cu‐CHA catalyst. The reaction of NO with the [(NH 3 ) 4 Cu 2 O 2 ] 2+ ‐peroxo complex governs the NH 3 ‐SCR activity. A reaction of the [(NH 3 ) 4 Cu 2 O 2 ] 2+ ‐peroxo complex with ammonia hinders the reaction of NO with the complex, thus leading to an inhibition of the NH 3 ‐SCR reaction. Finally, the deactivation in presence of SO 2 is due to a reaction of SO 2 with the [(NH 3 ) 4 Cu 2 O 2 ] 2+ ‐peroxo complex, leading to the formation of Cu−S compounds in the catalyst. In this review, the characterization and the reactions of the [(NH 3 ) 4 Cu 2 O 2 ] 2+ ‐peroxo complex with NO, NH 3 , and SO 2 , and mean‐field kinetic models based on first principles calculations for NH 3 ‐SCR activity and SO 2 poisoning are discussed. Catalysis ; Chemistry ; Ammonia ; Photochemistry ; Combinatorial chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Nanocluster Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Taslima Akter | Jahirul Islam | Kamal Hossain | Rabeya Akter Rabu
Source: Heliyon (article, 2024-12-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First-principles investigation of half-metallic CaTGe2O6 (T = Mn, Fe, Co) clinopyroxenes: Potential for spintronics and optoelectronics applications Clinopyroxene is a subgroup of pyroxene that is found in a variety of igneous and metamorphic rocks. This study investigates the structural, mechanical, electronic, optical, and thermal characteristics of CaTGe<sub>2</sub>O<sub>6</sub> (T = Mn, Fe, Co) using density functional theory. These structures' lattice parameters have been optimized using GGA-PBE, GGA-PBESOL, and LDA exchange-correlation functionals, where all these structures are found to be stable in monoclinic symmetry having a little variation with experimental results. All the structures are mechanically stable and ductile in nature. The CaFeGe<sub>2</sub>O<sub>6</sub> has the highest melting point and Debye temperature among the three structures. The electronic band diagram and spin-polarized PDOS of these structures confirm the half-metallic nature of all three structures. The half-metallic band gaps of CaTGe<sub>2</sub>O<sub>6</sub> (T = Mn, Fe, Co) are 3.05, 1.69, 1.99 eV in GGA + U and 2.88, 1.52, 1.78 eV in the LDA + U method, respectively. CaMnGe<sub>2</sub>O<sub>6</sub> is metallic in the spin-up state, whereas both CaFeGe<sub>2</sub>O<sub>6</sub> and CaMnGe<sub>2</sub>O<sub>6</sub> are metallic in the spin-down state. Refractive indices and dielectric functions of these structures ensure the transparency of CaTGe<sub>2</sub>O<sub>6</sub> (T = Mn, Fe, Co) clinopyroxenes around 28.5 eV photon energy. These structures possess the highest reflectivity and absorption coefficients in the UV region. These salient features of these structures suggest that the spintronics and optoelectronics industries may benefit from these clinopyroxene structures in the future. Spintronics ; Condensed matter physics ; Materials science ; Electronic band structure ; Band gap ; Direct and indirect band gaps ; Density functional theory ; Electronic structure ; Ferromagnetism ; Chemistry ; Physics ; Computational chemistry Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity ; Multiferroics and related materials
Authors: Dinesh Kumar Saini | Anshul Rana | Jyotirmoy Halder | Mohammad Maruf Billah | Harsimardeep S. Gill | Jinfeng Zhang | Subash Thapa | Shaukat Ali | Brent Turnipseed | Karl D. Glover | Maitiniyazi Maimaitijiang | Sunish K. Sehgal
Source: The Plant Phenome Journal (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning Abstract Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat ( Triticum aestivum ). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R 2 P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies ( R 2 P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels. Hyperspectral imaging ; Content (measure theory) ; Range (aeronautics) ; Artificial intelligence ; Food science ; Mathematics ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Materials science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Bradley P. Sutliff | Peter A. Beaucage | Debra J. Audus | Sara V. Orski | Tyler B. Martin
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers Large cross-validation campaigns produce classification models from NIR spectroscopy measurements of polyolefins (the most common class of plastic produced), which may improve sortation at recycling facilities. Sorting ; Identification (biology) ; Infrared spectroscopy ; Infrared ; Spectroscopy ; Pipeline transport ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Optics ; Physics ; Algorithm ; Organic chemistry ; Astronomy Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Analytical Chemistry and Chromatography
Authors: Thanapat Worakul | Rubén Laplaza | Shubhajit Das | Matthew D. Wodrich | Clémence Corminbœuf
Source: ACS Catalysis (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to develop "microkinetic molecular volcano plots". The resulting unified computational framework allows the influence of important reaction parameters, including temperature, reaction time, and concentration, to be quickly incorporated and more complex situations, such as off-cycle resting states and coupled catalytic cycles, to be tackled. Compared to previous generations of molecular volcanoes, these microkinetic counterparts offer a more comprehensive understanding of catalytic behavior, in which selectivity and product ratios can be explicitly determined by tracking the evolution of each product concentration over time. This is demonstrated by examining two case studies, rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation, in which the unique insights provided by microkinetic modeling, as well as the ability to simultaneously screen catalysts and reaction conditions, are highlighted. To facilitate the construction of these plots/maps, we introduce <i>mikimo</i>, a Python program that seamlessly integrates with our previously developed automated volcano builder, <i>volcanic</i>. Catalysis ; Selectivity ; Chemistry ; Volcano ; Chemical engineering ; Organic chemistry ; Geology ; Geochemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Shaowei Li | Guodong Liu
Source: Bioresources and Bioprocessing (review, 2024-07-25)
Field: Materials Science — Biomaterials

Harnessing cellulose-binding protein domains for the development of functionalized cellulose materials Cellulosic materials are attracting increasing research interest because of their abundance, biocompatibility, and biodegradability, making them suitable in multiple industrial and medical applications. Functionalization of cellulose is usually required to improve or expand its properties to meet the requirements of different applications. Cellulose-binding domains (CBDs) found in various proteins have been shown to be powerful tools in the functionalization of cellulose materials. In this review, we firstly introduce the structural characteristics of commonly used CBDs belonging to carbohydrate-binding module families 1, 2 and 3. Then, we summarize four main kinds of methodologies for employing CBDs to modify cellulosic materials (i.e., CBD only, genetic fusion, non-covalent linkage and covalent linkage). Via different approaches, CBDs have been used to improve the material properties of cellulose, immobilize enzymes for biocatalysis, and design various detection tools. To achieve industrial applications, researches for lowering the production cost of CBDs, improving their performance (e.g., stability), and expanding their application scenarios are still in need. Cellulose ; Cellulosic ethanol ; Cellulase ; Biocompatibility ; Biochemical engineering ; Surface modification ; Industrial and production engineering ; Chemistry ; Engineering ; Organic chemistry ; Chemical engineering Advanced Cellulose Research Studies ; Enzyme Production and Characterization ; Biofuel production and bioconversion
Authors: Reza Ghanavati | Alma C. Escobosa | Thomas A. Manz
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

An automated protocol to construct flexibility parameters for classical forcefields: applications to metal–organic frameworks In this work, forcefield flexibility parameters were constructed and validated for more than 100 metal-organic frameworks (MOFs). We used atom typing to identify bond types, angle types, and dihedral types associated with bond stretches, angle bends, dihedral torsions, and other flexibility interactions. Our work used Manz's angle-bending and dihedral-torsion model potentials. For a crystal structure containing <i>N</i> <sub>atoms</sub> in its unit cell, the number of independent flexibility interactions is 3(<i>N</i> <sub>atoms</sub> - 1). Because the number of bonds, angles, and dihedrals is normally much larger than 3(<i>N</i> <sub>atoms</sub> - 1), these internal coordinates are redundant. To reduce (but not eliminate) this redundancy, our protocol prunes dihedral types in a way that preserves symmetry equivalency. Next, each dihedral type is classified as non-rotatable, hindered, rotatable, or linear. We introduce a smart selection method that identifies which particular torsion modes are important for each rotatable dihedral type. Then, we computed the force constants for all flexibility interactions together <i>via</i> LASSO regression (<i>i.e.</i>, regularized linear least-squares fitting) of the training dataset. LASSO automatically identifies and removes unimportant forcefield interactions. For each MOF, the reference dataset was quantum-mechanically-computed in VASP <i>via</i> DFT with dispersion and included: (i) finite-displacement calculations along every independent atom translation mode, (ii) geometries randomly sampled <i>via ab initio</i> molecular dynamics (AIMD), (iii) the optimized ground-state geometry using experimental lattice parameters, and (iv) rigid torsion scans for each rotatable dihedral type. After training, the flexibility model was validated across geometries that were not part of the training dataset. For each MOF, we computed the goodness of fit (<i>R</i>-squared value) and the root-mean-squared error (RMSE) separately for the training and validation datasets. We compared flexibility models with and without bond-bond cross terms. Even without cross terms, the model yielded <i>R</i>-squared values of 0.910 (avg across all MOFs) ± 0.018 (st. dev.) for atom-in-material forces in the validation datasets. Our SAVESTEPS protocol should find widespread applications to parameterize flexible forcefields for material datasets. We performed molecular dynamics simulations using these flexibility parameters to compute heat capacities and thermal expansion coefficients for two MOFs. Construct (python library) ; Flexibility (engineering) ; Protocol (science) ; Computer science ; Theoretical computer science ; Mathematics ; Programming language ; Statistics ; Medicine Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions
Authors: Vasilis Loukadakis | Spyros Papaefthymiou
Source: Crystals (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Advancements in and Applications of Crystal Plasticity Modelling of Metallic Materials Integrated Computational Materials Engineering (ICME) is a set of methodologies utilized by researchers and engineers assisting the study of material behaviour during production processes and/or service. ICME aligns with societal efforts for the twin green and digital transitions while improving the sustainability and cost efficiency of relevant products/processes. A significant link of the ICME chain, especially for metallic materials, is the crystal plasticity (CP) formulation. This review examines firstly the progress CP has made since its conceptualization and secondly the relevant thematic areas of its utilization and portraits them in a concise and condensed manner. CP is a proven tool able to capture complex phenomena and to provide realistic results, while elucidating on the material behaviour under complex loading conditions. To this end, a significant number of formulations falling under CP, each with their unique strengths and weaknesses, is offered. It is a developing field and there are still efforts to improve the models in various terms. One of the biggest struggles in setting up a CP simulation, especially a physics-based one, is the definition of the proper values for the relevant parameters. This review provides valuable data tables with indicative values. Crystal plasticity ; Plasticity ; Materials science ; Composite material Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Timur A. Aliev | Ilya Korolev | Olga Burdulenko | Ekaterina Alchinova | Anton Subbota | Mikhail Yasnov | Michael Nosonovsky | Ekaterina V. Skorb
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Automatic image processing of cavitation bubbles to analyze the properties of petroleum products Machine-vision analysis of a frame with a gas bubble in the resonance mode ( n = 8). Bubble ; Cavitation ; Frame (networking) ; Mode (computer interface) ; Image processing ; Petroleum engineering ; Image (mathematics) ; Resonance (particle physics) ; Computer science ; Acoustics ; Materials science ; Computer vision ; Mechanical engineering ; Process engineering ; Artificial intelligence ; Engineering ; Mechanics ; Physics ; Human–computer interaction Ultrasound and Cavitation Phenomena ; Advanced Chemical Sensor Technologies ; Fluid Dynamics and Mixing
Authors: Serena Moccia | Massimo Christian D’ Alterio | Eugenio Romano | Claudio De Rosa | Giovanni Talarico
Source: Macromolecular Rapid Communications (article, 2024-10-22)
Field: Materials Science — Biomaterials

Stereoselectivity Control Interplay in Racemic Lactide Polymerization by Achiral Al‐Salen Complexes The origin of stereocontrol in ring opening polymerization (ROP) of racemic lactide (rac-LA) promoted by achiral aluminium-based catalysts has been explained through DFT calculations combined with a molecular descriptor (%V<sub>Bur</sub>) and the activation strain model (ASM-NEDA) analysis. The proposed chain end control (CEC) model suggests that the ligand framework adopts a chiral configuration mimicking the enantiomorphic site control (ESC) while also incorporating control of the last inserted monomer unit. It is found that the ligand wrapping mode around the aluminium centre is dictated by the monomer configuration (R,R-LA and S,S-LA). A good correlation with experimental data is achieved only when accounting for the ligand dynamic features and its steric influences, as highlighted by %V<sub>Bur</sub> steric maps and ASM-NEDA analysis. Understanding the ESC and CEC interplay is an important target for obtaining stereoselective ROP polymerization for the synthesis of biodegradable materials with tailored properties. Steric effects ; Lactide ; Polymerization ; Ligand (biochemistry) ; Monomer ; Stereoselectivity ; Chemistry ; Ring-opening polymerization ; Polymer chemistry ; Stereochemistry ; Catalysis ; Materials science ; Crystallography ; Organic chemistry ; Polymer ; Receptor biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Organometallic Complex Synthesis and Catalysis