jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aysel Gasimzadeh Aysel Gasimzadeh
Source: PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions (article, 2021-10-10)
Field: Chemistry — Analytical Chemistry

INVESTIGATION OF EFFECT OF NEW REAGENT COMPOSITIONS ON VARIOUS CRUDE OIL EMULSIONS OF AZERBAIJAN The article is devoted to the decomposition of strong water-crude oil emulsions formed during the preparation of crude oil for transportation. In the petroleum industry, there is a great need to develop new chemicals to improve the degradation efficiency of stable water- crude oil emulsions. Decomposition of strong water-oil emulsions is considered a key part of crude oil preparation for transportation. Therefore, the development of new demulsifying compositions to improve the degradation efficiency of stable crue oil emulsions remains relevant. This paper presents the results of studies of dehydration processes of Azerbaijani oils and emulsions depending on the degree of watering. The composition is based on Disulfan 4411, Disulfan 13280, Sarola 412, Difron 9426, ND-12 and Gossipol, they were also used to prepare Azerbaijani oil for transportation during laboratory tests of demulsification efficiency (except Difron 9426). During laboratory tests, it was determined that the best results on the degree of residual dehydration of petroleum phases were achieved using a composition called KAV-22. Keywords: Muradhanly, Bulla, Balakhany, Neft dashlari, Dehydration, Surahany, KAV-22. Dehydration ; Crude oil ; Petroleum ; Pulp and paper industry ; Decomposition ; Chemistry ; Reagent ; Environmental science ; Petroleum engineering ; Chromatography ; Organic chemistry ; Engineering Petroleum Processing and Analysis
Authors: Sébastien Plutniak
Source:  (preprint, 2021-11-03)
Field: Chemistry — Physical and Theoretical Chemistry

Assyrian Merchants meet Nuclear Physicists: History of the Early Contributions from Social Sciences to Computer Science. The Case of Automatic Pattern Detection in Graphs (1950s-1970s) Community detection is a major issue in network analysis. This paper combines a socio-historical approach with an experimental reconstruction of programs to investigate the early automation of clique detection algorithms, which remains one of the unsolved NP-complete problems today. The research led by the archaeologist Jean-Claude Gardin from the 1950s on non-numerical information and graph analysis is retraced to demonstrate the early contributions of social sciences and humanities. The limited recognition and reception of Gardin's innovative computer application to the humanities are addressed through two factors, in addition to the effects of historiography and bibliographies on the recording, discoverability, and reuse of scientific productions: (1) funding policies, evidenced by the transfer of research effort on graph applications from temporary interdisciplinary spaces to disciplinary organizations related to the then-emerging field of computer science; and (2) the erratic careers of algorithms, in which efficiency, flaws, corrections, and authors’ status, were determining factors. Discoverability ; Clique ; Computer science ; Historiography ; Discipline ; Data science ; Graph ; History of computing ; Field (mathematics) ; Leverage (statistics) ; Automation ; Artificial intelligence ; Sociology ; Social science ; Political science ; Theoretical computer science ; Engineering ; World Wide Web ; Algorithm ; Law History and advancements in chemistry ; History of Science and Medicine
Authors: Anh Tran | Julien Tranchida
Source:  (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Multi-fidelity Gaussian process and Bayesian optimization for materials design: Application to ternary random alloys. Abstract not provided. Ternary operation ; Bayesian optimization ; Gaussian process ; Bayesian probability ; Statistical physics ; Bulk modulus ; Fidelity ; Process (computing) ; Materials science ; Space (punctuation) ; Computer science ; Algorithm ; High fidelity ; Density functional theory ; Gaussian ; Mathematical optimization ; Mathematics ; Machine learning ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Georgios Soimoiris | Audrey Olivier | Richard C. Becker | Jaroslaw Knap | Michael D. Shields
Source:  (preprint, 2021-09-28)
Field: Materials Science — Materials Chemistry

A Diffusion Maps Surrogate Model for the Analysis and Simulation of Three-Dimensional Aluminum Materials with Random Voids Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions. Diffusion ; Aluminium ; Surrogate model ; Statistical physics ; Materials science ; Computer science ; Metallurgy ; Thermodynamics ; Physics ; Machine learning Machine Learning in Materials Science
Authors: Shuting Gu | Hongqiao Wang | Xiang Zhou
Source: arXiv (Cornell University) (preprint, 2021-08-10)
Field: Materials Science — Materials Chemistry

Active Learning for Saddle Point Calculation The saddle point (SP) calculation is a grand challenge for computationally intensive energy function in computational chemistry area, where the saddle point may represent the transition state (TS). The traditional methods need to evaluate the gradients of the energy function at a very large number of locations. To reduce the number of expensive computations of the true gradients, we propose an active learning framework consisting of a statistical surrogate model, Gaussian process regression (GPR) for the energy function, and a single-walker dynamics method, gentle accent dynamics (GAD), for the saddle-type transition states. SP is detected by the GAD applied to the GPR surrogate for the gradient vector and the Hessian matrix. Our key ingredient for efficiency improvements is an active learning method which sequentially designs the most informative locations and takes evaluations of the original model at these locations to train GPR. We formulate this active learning task as the optimal experimental design problem and propose a very efficient sample-based sub-optimal criterion to construct the optimal locations. We show that the new method significantly decreases the required number of energy or force evaluations of the original model. Hessian matrix ; Saddle point ; Computer science ; Function (biology) ; Mathematical optimization ; Surrogate model ; Correctness ; Gaussian process ; Artificial intelligence ; Algorithm ; Gaussian ; Applied mathematics ; Mathematics ; Machine learning ; Physics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Eric Taw | Jeffrey B. Neaton
Source: ChemRxiv (preprint, 2021-11-08)
Field: Chemistry — Inorganic Chemistry

Accelerated Discovery of CH4 Uptake Capacity MOFs using Bayesian Optimization High-throughput computational studies for discovery of metal-organic frameworks (MOFs) for separations and storage applications are often limited by the costs of computing thermodynamic quantities, with recent studies reliant ab initio results for a narrow selection of MOFs and empirical force-field methods for larger selections. Here, we conduct a proof-of-concept study using Bayesian optimization on CH4 uptake capacity of hypothetical MOFs for an existing dataset (Wilmer et al, Nature Chem. 2012, 4, 83). We show that less than 0.1% of the database needs to be screened with our Bayesian optimization approach to recover the top candidate MOFs. This opens the possibility of efficient screening of MOF databases using accurate ab-initio calculations for future adsorption studies on a minimal subset of MOFs. Furthermore, Bayesian optimization and the surrogate model presented here can offer interpretable material design insights and our framework will be applicable in the context of other target properties. Bayesian optimization ; Context (archaeology) ; Bayesian probability ; Computer science ; Selection (genetic algorithm) ; Ab initio ; Bayesian network ; Metal-organic framework ; Adsorption ; Bayesian inference ; Chemistry ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Nur Liyana Zahari | Hafsah Taha
Source: Journal of Science and Mathematics Letters (article, 2021-05-27)
Field: Chemistry — Physical and Theoretical Chemistry

The Effectiveness of Green Chemistry Experiments on Students’ Achievement in Electrochemistry Green chemistry could play an important role in the implementation of our national chemistry curriculum to ensure the success of education on sustainability or environmental education. Green chemistry experiments use the concept of reducing the use of chemicals by replacing chemicals with benign chemicals and recycled materials to approximate sustainable use of materials. This quasi-experiment study aimed to examine the effectiveness of the implementation of green chemistry experiments on students’ achievement in electrochemistry. The pre- and post-tests were administered to 22 secondary school students who took Form Four Chemistry subject and they were randomly divided into treatment and control groups. The treatment group carried out electrochemistry experiments based on the green chemistry lab manual developed while the control group carried out convensional chemistry experiments based on the practical textbook provided by the ministry. Data obtained were analysed using t-tests. Results showed significant difference for the treatment group in the pre and post achievement test [t(10)= −2.823, p&lt;0.05]. Therefore, it is indicated green chemistry experiments may have a positive impact on students’ achievement in electrochemistry. The green chemistry experiments seemed to engage students in actively learning the topic and facilitate teachers to diversify their laboratory pedagogy. Electrochemistry ; Mathematics education ; Chemistry ; Psychology ; Computer science ; Physical chemistry ; Electrode Various Chemistry Research Topics
Authors: Gregory J. McManus
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-07-30)
Field: Chemistry — Physical and Theoretical Chemistry

Introducing undergraduate students to single-crystal X-ray diffraction through a course-based undergraduate research experience The integration of active learning strategies within introductory STEM courses is an effective paradigm for improving student engagement and performance.The objective of this project was to introduce students to chemical crystallography and increase their desire to conduct undergraduate research.To accomplish these goals, a coursebased research experience (CURE) utilizing X-ray instrumentation was developed and implemented across a diverse undergraduate student population at Florida Gulf Coast University.The students in CHM 3610 Inorganic Chemistry were given a list containing dozens of organic compounds that had not had their crystal structure published in the Cambridge Structural Database (CSD).The students were able to select their own individual chemical from this list and research information associated with that chemical (i.e.properties, hazardous, applications).They researched what solvent systems they should use to grow single crystals of their chemical via slow evaporation.If the students obtain suitable crystals, they will be analyzed on the new single crystal X-ray diffractometer that FGCU has recently obtained through an NSF-MRI grant.An outline of the CURE activities implemented in this course and the results obtained will be presented herein. Course (navigation) ; Diffraction ; Undergraduate research ; Single crystal ; Materials science ; Crystallography ; X-ray ; Medical education ; Mathematics education ; Psychology ; Optics ; Physics ; Chemistry ; Medicine ; Astronomy Various Chemistry Research Topics
Authors: Jillian Kasman | Taylor Slouka | Alan L. Kiste | Javin P. Oza
Source:  (preprint, 2021-11-01)
Field: Chemistry — Physical and Theoretical Chemistry

A manipulative activity for exploring effective nuclear charge (Zeff) High-school and undergraduate college students may often rely on memorization when learning periodic trends, leading to an ineffective understanding of introductory chemistry concepts like atomic radius, ionization energy, and electron affinity. Comprehension of effective nuclear charge (Zeff) is foundational to a complete understanding of periodic trends. Zeff remains an abstract concept for many students, indicating that a manipulative activity for teaching the phenomenon could be a useful way to explain the concept for greater student comprehension. Here we report a hands-on learning activity to target this knowledge gap using magnetic attraction as an analogy for electrostatic attraction within the atom. This approach enables students to explore the phenomenon of effective nuclear charge at a macroscopic level and apply their learning to periodic trends and related concepts. We anticipate that this activity will fill a long-standing hole in the active learning of chemical principles. Importantly, this activity is low-cost and can be assembled using readily accessible materials to allow implementation in most classrooms and virtual learning environments. Memorization ; Comprehension ; Phenomenon ; Analogy ; Charge (physics) ; Atom (system on chip) ; Effective nuclear charge ; Mathematics education ; Physics ; Computer science ; Theoretical physics ; Electron ; Psychology ; Quantum mechanics ; Epistemology Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Jillian Kasman | Taylor Slouka | Alan L. Kiste | Javin P. Oza
Source: ChemRxiv (preprint, 2021-11-05)
Field: Chemistry — Physical and Theoretical Chemistry

A manipulative activity for exploring effective nuclear charge (Zeff) High-school and undergraduate college students may often rely on memorization when learning periodic trends, leading to an ineffective understanding of introductory chemistry concepts like atomic radius, ionization energy, and electron affinity. Comprehension of effective nuclear charge (Zeff) is foundational to a complete understanding of periodic trends. Zeff remains an abstract concept for many students, indicating that a manipulative activity for teaching the phenomenon could be a useful way to explain the concept for greater student comprehension. Here we report a hands-on learning activity to target this knowledge gap using magnetic attraction as an analogy for electrostatic attraction within the atom. This approach enables students to explore the phenomenon of effective nuclear charge at a macroscopic level and apply their learning to periodic trends and related concepts. We anticipate that this activity will fill a long-standing hole in the active learning of chemical principles. Importantly, this activity is low-cost and can be assembled using readily accessible materials to allow implementation in most classrooms and virtual learning environments. Memorization ; Comprehension ; Phenomenon ; Analogy ; Charge (physics) ; Effective nuclear charge ; Atom (system on chip) ; Mathematics education ; Physics ; Theoretical physics ; Electron ; Computer science ; Psychology ; Quantum mechanics ; Epistemology Various Chemistry Research Topics ; Science Education and Pedagogy ; Experimental and Theoretical Physics Studies
Authors: Zhan Ma | Shu Wang | Minhee Kim | Kaibo Liu | Chun‐Long Chen | Wenxiao Pan
Source: arXiv (Cornell University) (preprint, 2021-03-19)
Field: Materials Science — Materials Chemistry

Transfer Learning of Memory Kernels in Coarse-grained Modeling The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers. Computer science ; Kernel (algebra) ; Process (computing) ; Transfer of learning ; Biological system ; Transferability ; Artificial intelligence ; Machine learning ; Algorithm ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Alexander E. Siemenn | Matthew Beveridge | Tonio Buonassisi | Iddo Drori
Source: arXiv (Cornell University) (preprint, 2021-05-06)
Field: Materials Science — Materials Chemistry

Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop High-performance semiconductor optoelectronics such as perovskites have high-dimensional and vast composition spaces that govern the performance properties of the material. To cost-effectively search these composition spaces, we utilize a high-throughput experimentation method of rapidly printing discrete droplets via inkjet deposition, in which each droplet is comprised of a unique permutation of semiconductor materials. However, inkjet printer systems are not optimized to run high-throughput experimentation on semiconductor materials. Thus, in this work, we develop a computer vision-driven Bayesian optimization framework for optimizing the deposited droplet structures from an inkjet printer such that it is tuned to perform high-throughput experimentation on semiconductor materials. The goal of this framework is to tune to the hardware conditions of the inkjet printer in the shortest amount of time using the fewest number of droplet samples such that we minimize the time and resources spent on setting the system up for material discovery applications. We demonstrate convergence on optimum inkjet hardware conditions in 10 minutes using Bayesian optimization of computer vision-scored droplet structures. We compare our Bayesian optimization results with stochastic gradient descent. Bayesian optimization ; Throughput ; Computer science ; Bayesian probability ; Gradient descent ; Semiconductor device fabrication ; Materials science ; Nanotechnology ; Artificial intelligence Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Jillian Kasman | Taylor Slouka | Alan L. Kiste | Javin P. Oza
Source: ChemRxiv (preprint, 2021-11-01)
Field: Chemistry — Physical and Theoretical Chemistry

A manipulative activity for exploring effective nuclear charge (Zeff) High-school and undergraduate college students may often rely on memorization when learning periodic trends, leading to an ineffective understanding of introductory chemistry concepts like atomic radius, ionization energy, and electron affinity. Comprehension of effective nuclear charge (Zeff) is foundational to a complete understanding of periodic trends. Zeff remains an abstract concept for many students, indicating that a manipulative activity for teaching the phenomenon could be a useful way to explain the concept for greater student comprehension. Here we report a hands-on learning activity to target this knowledge gap using magnetic attraction as an analogy for electrostatic attraction within the atom. This approach enables students to explore the phenomenon of effective nuclear charge at a macroscopic level and apply their learning to periodic trends and related concepts. We anticipate that this activity will fill a long-standing hole in the active learning of chemical principles. Importantly, this activity is low-cost and can be assembled using readily accessible materials to allow implementation in most classrooms and virtual learning environments. Memorization ; Comprehension ; Phenomenon ; Analogy ; Atom (system on chip) ; Charge (physics) ; Effective nuclear charge ; Mathematics education ; Physics ; Theoretical physics ; Computer science ; Atomic physics ; Electron ; Psychology ; Quantum mechanics ; Epistemology Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Jillian Kasman | Taylor Slouka | Alan L. Kiste | Javin P. Oza
Source:  (preprint, 2021-11-05)
Field: Chemistry — Physical and Theoretical Chemistry

A manipulative activity for exploring effective nuclear charge (Zeff) High-school and undergraduate college students may often rely on memorization when learning periodic trends, leading to an ineffective understanding of introductory chemistry concepts like atomic radius, ionization energy, and electron affinity. Comprehension of effective nuclear charge (Zeff) is foundational to a complete understanding of periodic trends. Zeff remains an abstract concept for many students, indicating that a manipulative activity for teaching the phenomenon could be a useful way to explain the concept for greater student comprehension. Here we report a hands-on learning activity to target this knowledge gap using magnetic attraction as an analogy for electrostatic attraction within the atom. This approach enables students to explore the phenomenon of effective nuclear charge at a macroscopic level and apply their learning to periodic trends and related concepts. We anticipate that this activity will fill a long-standing hole in the active learning of chemical principles. Importantly, this activity is low-cost and can be assembled using readily accessible materials to allow implementation in most classrooms and virtual learning environments. Memorization ; Comprehension ; Analogy ; Phenomenon ; Effective nuclear charge ; Atom (system on chip) ; Mathematics education ; Physics ; Computer science ; Theoretical physics ; Electron ; Psychology ; Quantum mechanics ; Epistemology Various Chemistry Research Topics ; Science Education and Pedagogy ; Experimental and Theoretical Physics Studies