jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amelia J. Cordwell | Roman R. Rafikov
Source: arXiv (Cornell University) (preprint, 2024-07-01)
Field: Chemistry — Spectroscopy

Early stages of gap opening by planets in protoplanetary discs ABSTRACT Annular substructures in protoplanetary discs, ubiquitous in sub-mm observations, can be caused by gravitational coupling between a disc and its embedded planets. Planetary density waves inject angular momentum into the disc leading to gap opening only after travelling some distance and steepening into shocks (in the absence of linear damping); no angular momentum is deposited in the planetary co-orbital region, where the wave has not shocked yet. Despite that, simulations show mass evacuation from the co-orbital region even in inviscid discs, leading to smooth double-trough gap profiles. Here, we consider the early time-dependent stages of planetary gap opening in inviscid discs. We find that an often-overlooked contribution to the angular momentum balance caused by the time-variability of the specific angular momentum of the disc fluid (caused, in turn, by the time-variability of the radial pressure support) plays a key role in gap opening. Focusing on the regime of shallow gaps with depths of $\lesssim 20~{{\ \rm per\ cent}}$, we demonstrate analytically that early gap opening is a self-similar process, with the amplitude of the planet-driven perturbation growing linearly in time and the radial gap profile that can be computed semi-analytically. We show that mass indeed gets evacuated from the co-orbital region even in inviscid discs. This evolution pattern holds even in viscous discs over a limited period of time. These results are found to be in excellent agreement with 2D numerical simulations. Our simple gap evolution solutions can be used in studies of dust dynamics near planets and for interpreting protoplanetary disc observations. Planet ; Protoplanetary disk ; Astrobiology ; Astrophysics ; Geology ; Astronomy ; Physics Molecular Spectroscopy and Structure ; Astrophysics and Star Formation Studies ; Astro and Planetary Science
Authors: Jennifer B. Wells | LORETTA DEAN
Source: Journal of Student Affairs Inquiry Improvement and Impact (article, 2024-12-31)
Field: Chemistry — Physical and Theoretical Chemistry

Evolution of the CAS Standards This article traces the evolution of the Council for the Advancement of Standards in Higher Education (CAS) over its forty-five-year history, emphasizing its pivotal role in promulgating professional standards for student-focused programs and services in higher education. Founded in 1979, CAS has grown from 13 to 43 member associations, expanded its portfolio from 17 to 51 functional area standards, and released 11 editions of the CAS Professional Standards for Higher Education (commonly known as the “CAS Blue Book”). The article examines CAS’s unwavering commitment to collaboration, consensus, and self-assessment, underscoring its enduring mission to enhance professional practice while addressing the evolving needs, challenges, and opportunities in the field. Computer science History and advancements in chemistry ; Data Quality and Management
Authors: Seyyedfaridoddin Fattahpour | Sara Kadkhodaei
Source: Physical Review Materials (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Improving <i>ab initio</i> diffusion calculations in materials through Gaussian process regression Saddle point search schemes are widely used to identify the transition state of different processes, like chemical reactions, surface and bulk diffusion, surface adsorption, and many more. In solid-state materials with relatively large numbers of atoms, the minimum mode following schemes such as dimer are commonly used because they alleviate the calculation of the Hessian on the high-dimensional potential energy surface. Here, we show that the dimer search can be further accelerated by leveraging Gaussian process regression (GPR). The GPR serves as a surrogate model to feed the dimer with the required energy and force input. We test the GPR-accelerated dimer method for predicting the diffusion coefficient of vacancy-mediated self-diffusion in body-centered cubic molybdenum and sulfur diffusion in hexagonal molybdenum disulfide. We use a multitask learning approach that utilizes a shared covariance function between energy and force input, and we show that the multitask learning significantly improves the performance of the GPR surrogate model compared to previously used learning approaches. Additionally, we demonstrate that a translation-hop sampling approach is necessary to avoid overfitting the GPR surrogate model to the minimum-mode-following pathway and thus succeeding in locating the saddle point. We show that our method reduces the number of evaluations compared to a conventional dimer method. Materials science ; Diffusion ; Ab initio ; Diffusion process ; Process (computing) ; Statistical physics ; Gaussian ; Thermodynamics ; Computational chemistry ; Chemical physics ; Computer science ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Yuxuan Yao | Harald Oberhofer
Source: The Journal of Chemical Physics (article, 2024-08-15)
Field: Materials Science — Materials Chemistry

Designing building blocks of covalent organic frameworks through on-the-fly batch-based Bayesian optimization In this work, we use a Bayesian optimization (BO) algorithm to sample the space of covalent organic framework (COF) components aimed at the design of COFs with a high hole conductivity. COFs are crystalline, often porous coordination polymers, where organic molecular units-called building blocks (BBs)-are connected by covalent bonds. Even though we limit ourselves here to a space of three-fold symmetric BBs forming two-dimensional COF sheets, their design space is still much too large to be sampled by traditional means through evaluating the properties of each element in this space from first principles. In order to ensure valid BBs, we use a molecular generation algorithm that, by construction, leads to rigid three-fold symmetric molecules. The BO approach then trains two distinct surrogate models for two conductivity properties, level alignment vs a reference electrode and reorganization free energy, which are combined in a fitness function as the objective that evaluates BBs' conductivities. These continuously improving surrogates allow the prediction of a material's properties at a low computational cost. It thus allows us to select promising candidates which, together with candidates that are very different from the molecules already sampled, form the updated training sets of the surrogate models. In the course of 20 such training steps, we find a number of promising candidates, some being only variations on already known motifs and others being completely novel. Finally, we subject the six best such candidates to a computational reverse synthesis analysis to gauge their real-world synthesizability. Computer science ; Covalent bond ; Function (biology) ; Space (punctuation) ; Probabilistic logic ; Bayesian probability ; Algorithm ; Artificial intelligence ; Chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Oliver Weismantel | Lakshani J. Weerarathna | Tanja Junkers
Source: ChemRxiv (preprint, 2024-06-18)
Field: Chemistry — Organic Chemistry

Autonomous Size-Targeting for Block Copolymer Nanoparticles Using the combination of flow synthesis with online dynamic light scattering (DLS) analysis for particle size characterization and fully autonomous computer control allows for reproducible and targetable synthesis of nanoparticles from block copolymer (BCP) solutions by rapid mixing with water in a defined micromixer environment. Using Bayesian optimization (BO), nanoparticle sizes become programmable and preselectable, and awide range of sizes can be obtained per used BCP. Specifically, we show for a series of polystyrene-b-poly(N,N- dimethyl acrylamide) and polystyrene-b-poly(poly(ethylene glycol) methyl ether acrylate) BCPs, how particles spanning from 130 to 280 nm can be systematically targeted, with sizes between 100 and 1000 nm being at least in principle also achievable. Further, Pareto fronts for the individual synthesis parameters overall flow rate, water volume fraction and polymer concentration are obtained from the established routines and presented. This BO approach highlights the efficacy of autonomous flow platforms in achieving precise control over polymer self-assembly processes, offering an optimal production window for the development and optimization of polymeric nanostructures in diverse fields such as drug delivery and materials science. Copolymer ; Nanoparticle ; Block (permutation group theory) ; Nanotechnology ; Materials science ; Computer science ; Polymer ; Mathematics ; Combinatorics Nanomaterials for catalytic reactions ; Nanoparticle-Based Drug Delivery ; Advanced Polymer Synthesis and Characterization
Authors: Nicholas Ury | Brandon Bocklund | Aurélien Perron | K.M. Bertsch
Source: Computational Materials Science (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

Automated path planning for functionally graded materials considering phase stability and solidification behavior: Application to the Mo-Nb-Ta-Ti system Materials science ; Stability (learning theory) ; Phase (matter) ; Path (computing) ; Metallurgy ; Engineering physics ; Computer science ; Chemistry ; Physics Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Injection Molding Process and Properties
Authors: Michael A. Pence | Gavin Hazen | Joaquín Rodríguez López
Source: ChemRxiv (preprint, 2024-12-23)
Field: Materials Science — Materials Chemistry

Closed-Loop Navigation of a Kinetic Zone Diagram for Redox-Mediated Electrocatalysis Using Bayesian Optimization, a Digital Twin, and Automated Electrochemistry Molecular electrocatalysis campaigns often require tuning multiple experimental parameters to obtain kinetically insightful electrochemical measurements, a prohibitively time-consuming task when performing comprehensive studies across multiple catalysts and substrates. In this work, we present an autonomous workflow that combines Bayesian optimization and automated electrochemistry to perform fully unsupervised cyclic voltammetry (CV) studies of molecular electrocatalysis. We developed CV descriptors that leveraged the conceptual framework of the EC’ kinetic zone diagram to enable efficient Bayesian optimization. The CV descriptor’s effect on optimization performance was evaluated using a digital twin of our autonomous experimental platform, quantifying the accuracy of obtained kinetic values against the known ground truth. We demonstrated our platform experimentally by performing autonomous studies of TEMPO-catalyzed ethanol and isopropanol electro-oxidation, demonstrating rapid identification of kinetically insightful conditions in 10 or less iterations through the closed-loop workflow. Overall, this work highlights the application of autonomous electrochemical platforms to accelerate mechanistic studies in molecular electrocatalysis and beyond. Electrocatalyst ; Redox ; Kinetic energy ; Electrochemistry ; Bayesian optimization ; Diagram ; Bayesian probability ; Computer science ; Loop (graph theory) ; Chemistry ; Combinatorial chemistry ; Electrode ; Inorganic chemistry ; Mathematics ; Physical chemistry ; Physics ; Artificial intelligence ; Combinatorics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Emmanuel Sansusthy Tardío | Santiago El Awad | Tian Chen | Theocharis Baxevanis
Source: Journal of Applied Mechanics (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Extraction of Mechanical Properties of Shape Memory Alloys From Instrumented Spherical Indentation Abstract An approach to infer mechanical properties of shape memory alloys (SMAs) from instrumented spherical indentation records of indenter tip loading force, P, versus the resultant indentation depth, h, during indentation loading and unloading is proposed. The approach is based on (i) a finite element (FE) model for describing the forward problem of determining the indentation response, i.e., the P−h curve, for a given set of SMA mechanical properties, (ii) a computationally efficient Gaussian process surrogate model to replace the costly FE one, and (iii) an iterative solution strategy, which approximates the unknown model parameters from an experimentally determined P−h record and their corresponding probability densities via a Bayesian inference framework and a Markov Chain Monte Carlo sampling strategy. The efficacy of the proposed approach is demonstrated using numerical experiments, i.e., indentation curves generated by evaluating the FE model at points in the parameter space. Indentation ; Shape-memory alloy ; Materials science ; Composite material ; Extraction (chemistry) Shape Memory Alloy Transformations ; Laser and Thermal Forming Techniques
Authors: Taehyun Park | Junho Song | Jinyoung Jeong | Seungpyo Kang | Joonchul Kim | Joonghee Won | Jungim Han | Kyoungmin Min
Source: Materials Today Advances (article, 2024-07-29)
Field: Materials Science — Materials Chemistry

Interpretable machine learning boosting the discovery of targeted organometallic compounds with optimal bandgap Organometallic compounds (OMCs) have attracted tremendous attention in various fields, such as photovoltaic cell and high-k dielectric application, due to their beneficial properties. Despite their potential, the progression of OMCs into industrial applications is hindered by the limited databases available for their properties and the absence of efficient surrogate models. To address this, in this study, optimally selected feature-based surrogate models for predicting the electronic properties of OMCs are constructed via various multiscale features and extensive database. To this end, high-throughput calculation was performed to obtain electronic properties of more than 18k materials generally known as organometallics, augmenting around 12k organic materials obtained from the public open data set, OMDB-GAP1. For generating features closely related to OMCs, descriptors encapsulating the information ranging local to global, also other widely-used composition-, structure-based features (more than 3.5k in total) were employed. Among these descriptors, we identified 48 critical features that elucidates the physicochemical underpinnings of OMCs’ properties, suggesting their impact on the properties of OMCs. The light gradient boosting machine model achieved high-accuracy predictions across the entire database with just 1 % of the total descriptors, sufficiently compared to the entire sets (decreased of around 0.01 by R2 score and 0.01 eV by MAE). Furthermore, the efficacy of active learning process was demonstrated to find OMCs with optimal properties rapidly. As a result, expected improvement outperforms other methods by identifying 69 % of the target materials only searching 46 % of the total search space. Our constructed platform with a high-throughput calculated database can pave the way for the rapid screening of OMCs for the targeted industrial application, and suggest a comprehensive grasp of the intrinsic properties of OMCs and related compounds. Boosting (machine learning) ; Gradient boosting ; Computer science ; Artificial intelligence ; Machine learning ; Ranging ; Throughput ; Band gap ; Materials science ; Wireless ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Kun Yu | Ryo Tachibana | Corentin Rumo | Nico V. Igareta | Thomas R. Ward
Source: ChemRxiv (preprint, 2024-02-20)
Field: Chemistry — Organic Chemistry

Artificial Metalloenzyme Catalyzed Enantioselective Carboamination of Alkenes Relying on ubiquitous alkenes, carboamination reactions enable the difunctionalization of the double bond by the concurrent formation of a C–N and a C–C single bond. In the past years, several groups have reported on elegant strategies for the carboamination of alkenes relying on homogeneous catalysts or enzymes. Herein, we report on an artificial metalloenzyme for the enantioselective carboamination of dihydrofuran. Genetic optimization, combined with a Bayesian optimization of catalytic performance, afforded the disubstituted tetrahydrofuran product in up to 22 TON and 85% ee. X-ray analysis of the evolved artificial carboaminase shed light on critical amino acid residues that affect catalytic performance. Enantioselective synthesis ; Chemistry ; Catalysis ; Homogeneous ; Combinatorial chemistry ; Tetrahydrofuran ; Organic chemistry ; Methanol Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Saeed Akbarishandiz | Soumya Ray | Fei Zhou | Xiao Chen | João M. Maia
Source: Molecular Simulation (article, 2024-04-05)
Field: Materials Science — Materials Chemistry

Machine learning of performance space mapping for the DPD simulation of drug delivery to endothelial cells Despite huge effort over the years, the design of functionalised nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative particle dynamics (DPD) simulation is used to study the adhesion of NCs to endothelial cells under the influence of series of parameters such as shape, size, and ligand density of the NCs. However, preparing a performance space mapping that illustrates the penetration depths of NCs as a function of variations in their properties requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by leveraging a Gaussian process regression (GPR)-informed active learning strategy and an extensive exploration of numerous samples, each representing different properties of NCs. The performance space mapping reveals that NCs with rod and disc shapes exhibit superior penetration capabilities compared to those with a spherical shape. Furthermore, it demonstrates that smaller-sized rod-shaped NCs and larger-sized disc-shaped NCs tend to achieve better penetration. When considering smaller NCs, the influence of ligand density appears to be limited. On the contrary, for larger NCs, an increase in ligand density correlates with greater penetration depth, underscoring its substantial role in shaping their penetration capabilities. Dissipative particle dynamics ; Penetration (warfare) ; Nanocarriers ; Drug delivery ; Biological system ; Computer science ; Materials science ; Molecular dynamics ; Nanotechnology ; Chemistry ; Mathematics ; Computational chemistry ; Composite material ; Polymer ; Biology Block Copolymer Self-Assembly ; Polymer Surface Interaction Studies ; Advanced Polymer Synthesis and Characterization
Authors: Siti Nur Fatin Hakimah Ibnuzaman | Siti Sarah Mohd Khalid | Thulasi Subramani | Siti Atiqah Nazar | Anis Afidah Baharudin | Siti Aminah Mohamad Razak
Source: Sains Humanika (article, 2024-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

Differentiated Instruction in Chemistry Education Differentiated Instruction (DI) is a comprehensive approach that addresses a variety of students’ requirements and needs in a classroom environment, including their interests, readiness, and learning styles. The idea behind this approach is based on the belief that every classroom is unique. Thus, educators should review and modify their teaching practice from time to time. Hence, this study aims to identify the learning methods in differentiated instruction, their advantages, and challenges to implement in chemistry education using qualitative methods through semi-structured interviews. Seven secondary school teachers with 10 years of teaching experience in Chemistry were selected purposely for this study. To achieve the research objectives, thematic analysis is used, involving reading through the data and analyzing them according to the themes. The experts validate the updated questioning instrument prepared by the researchers to ensure the validity of the questionnaire. Findings reveal that most of them have specific experiences of using differentiated pedagogy to teach chemistry. Three of them described that students, who tend to master visual-spatial intelligence, can interpret and enhance their understanding of a chemistry concept through drawing, namely for the formation of ionic bonds and covalent bonds. For students, who master linguistic-verbal intelligence, teachers can use presentation activities to enhance their understanding of the chemistry concept. Hence, it can promote active learning, encourage students to ask questions, increase students' confidence level in performing classroom activities and other advantages. Conversely, the difficulty faced by the teachers in implementing DI is time constraint. Hence, differentiated instruction is a valuable and practical approach to addressing the diverse needs of students, promoting engagement, collaboration, and the development of essential skills for lifelong learning. Integrating DI principles can contribute significantly to creating enriching and effective learning environments as education evolves. Chemistry education ; Mathematics education ; Chemistry ; Psychology Various Chemistry Research Topics ; Science Education and Pedagogy ; Problem and Project Based Learning
Authors: Helton Siqueira Maciel | Diego Telles Fernandes | Charlles R. A. Abreu | Argimiro R. Secchi | Frederico W. Tavares
Source: Energy & Fuels (article, 2024-07-30)
Field: Chemistry — Analytical Chemistry

Molecular Reconstruction of Heavy Petroleum Fractions: A Bayesian Approach We developed molecular stochastic reconstruction algorithms from a Bayesian perspective. The parameter estimation of stochastic reconstruction models is a challenge due to their intractable likelihood functions. The use of the Bayesian optimization framework for likelihood-free inference provides us with a natural method for uncertainty propagation and model regularization, which reduces overfitting issues. Using only 9 parameters, the model was able to represent the properties of all vacuum residues studied in this work, both for the properties provided to the model, such as molecular weight, elemental analysis, SARA fractions, simplified nuclear magnetic resonance, and simulated distillation and for the properties not provided, such as full magnetic resonance. The posterior distributions of the parameters and properties showed the prediction uncertainties of the model with the credible intervals containing both constrained and unconstrained observed properties, thereby confirming its robustness. Besides, the posterior predictive mean was shown to be a good estimator for the observed properties. Overfitting ; Bayesian probability ; Bayesian inference ; Marginal likelihood ; Estimator ; Posterior probability ; Computer science ; Robustness (evolution) ; Mathematics ; Algorithm ; Applied mathematics ; Biological system ; Mathematical optimization ; Chemistry ; Statistics ; Artificial intelligence ; Artificial neural network Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; NMR spectroscopy and applications