jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joshua T. Cantin
Source: cIRcle (University of British Columbia) (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Interference in complex quantum systems : from localization in high-dimensional lattices to surface spin echo with molecules This thesis considers (1) novel manifestations and applications of quantum interference in complex systems and (2) development of new approaches to study complex quantum systems. First, I examine a general model of particles with long-range hopping amplitudes. For at least 30 years, it has been widely accepted that these particles do not undergo Anderson localization in 3D lattices. We show that these particles do undergo Anderson localization in 3D lattices if their hopping amplitudes are isotropic. In contrast, particles with anisotropic long-range hopping appear to follow the widely held expectations. We show these results by demonstrating that cooperative shielding extends to 3D cubic lattices with isotropic long-range hopping, but not with anisotropic long-range hopping and by computing the scaling behaviour of participation ratios and energy level statistics. Secondly, I develop a fully quantum mechanical model of molecular surface spin-echo experiments, which study surface properties and dynamics by scattering molecules off the sample surface. This model, based on the transfer matrix method, incorporates molecular hyperfine degrees of freedom, allows for the efficient calculation of the experimental signal given a molecule-surface scattering matrix, and permits us to begin addressing the inverse scattering problem. This fully quantum model is required to properly describe these experiments as the semi-classical methods used to describe experiments using helium-3 atoms do not take magnetic-field induced momentum changes into account. We apply our method to the case of ortho-hydrogen and then apply Bayesian optimization to determine the molecule-surface scattering matrix elements from a calculated signal, for a scattering matrix defined by three parameters. Our work sets the stage for Bayesian optimization to solve the inverse scattering problem for these experiments. Finally, I propose using Bayesian model calibration to improve the convergence of Monte Carlo calculations in regions where the sign problem or critical slowing down are an issue. Specifically, Bayesian model calibration would correct poorly converged Monte Carlo calculations with the information from a small number of well-converged Monte Carlo calculations. As a simple proof of principle demonstration, we apply Bayesian model calibration to a diagrammatic Monte Carlo calculation of the scattering length of a spherical potential barrier. Quantum interference ; Echo (communications protocol) ; Interference (communication) ; Spin (aerodynamics) ; Physics ; Quantum ; Quantum mechanics ; Condensed matter physics ; Computer science ; Telecommunications ; Computer security Advanced Physical and Chemical Molecular Interactions ; Spectroscopy and Quantum Chemical Studies ; Quantum optics and atomic interactions
Authors: Ling, Jingwei (Mike)
Source: Scholarly and Creative Works from DePauw University (DePauw University) (article, 2019-10-02)
Field: Chemistry — Physical and Theoretical Chemistry

Developing a Digital Simulation For Exploring the Thermodynamics of a Chemical Reaction Shiny is a package used in the R programming environment to develop interactive applications without the need for a background in HTML, CSS, and/or JavaScript coding. Applications are run in a browser either remotely using shinyapps.io as a hosting service, or locally using a computer with R installed. The interactive nature of a Shiny application allows students to explore a (bio)chemical system in a manner that uses active-learning to promote critical thinking in the classroom. Work this summer focused on developing an application for Chem 260 that models the thermodynamic properties of a reaction, allowing students to explore how a change in temperature, enthalpy, entropy, and activation energy affects a reaction’s thermodynamic favorability and the reaction’s rate. Work (physics) ; JavaScript ; Energy (signal processing) ; R package Various Chemistry Research Topics ; Cancer and biochemical research ; Engineering Education and Pedagogy
Authors: Troy D. Loeffler | Tarak K. Patra | Henry Chan | Mathew J. Cherukara | Subramanian K. R. S. Sankaranarayanan
Source: arXiv (Cornell University) (preprint, 2019-09-25)
Field: Materials Science — Materials Chemistry

Active Learning the Coarse-Grained Energy Landscape For Water Clusters From Sparse Training Data ANNs are currently trained by generating large quantities (On the order of $10^{4}$ or greater) of structural data in hopes that the ANN has adequately sampled the energy landscape both near and far-from-equilibrium. This can, however, be a bit prohibitive when it comes to more accurate levels of quantum theory. As such it is desirable to train a model using the absolute minimal data set possible, especially when costs of high-fidelity calculations such as CCSD and QMC are high. Here, we present an Active Learning approach that iteratively trains an ANN model to faithfully replicate the coarse-grained energy surface of water clusters using only 426 total structures in its training data. Our active learning workflow starts with a sparse training dataset which is continually updated via a Monte Carlo scheme that sparsely queries the energy landscape and tests the network performance. Next, the network is retrained with an updated training set that includes failed configurations/energies from previous iteration until convergence is attained. Once trained, we generate an extensive test set of ~100,000 configurations sampled across clusters ranging from 1 to 200 molecules and demonstrate that the trained network adequately reproduces the energies (within mean absolute error (MAE) of ~ 2 meV/molecule) and forces (MAE ~ 40 meV/{Å}) compared to the reference model. More importantly, the trained ANN model also accurately captures both the structure as well as the free energy as a function of the various cluster sizes. Overall, this study reports a new active learning scheme with promising strategy to develop accurate force-fields for molecular simulations using extremely sparse training data sets. Training (meteorology) ; Computer science ; Energy (signal processing) ; Energy landscape ; Machine learning ; Geography ; Physics ; Mathematics ; Meteorology ; Statistics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Machine Learning and ELM
Authors: Wilson K. Gichuhi
Source: World journal of chemical education (article, 2019-07-09)
Field: Chemistry — Physical and Theoretical Chemistry

Astrochemistry as a Gateway to Teaching and Learning Threshold Concepts in Physical Chemistry The purpose of this paper is to examine the use of astrochemistry examples in teaching the potential threshold concepts (TCs) of physical chemistry that are contained in the recently published Physical Chemistry Anchoring Concepts Content Map (PChem-ACCM). The paper provides a brief overview of how selected astrochemical examples can be utilized to teach and learn suggested TCs that are commonly encountered in the three main overarching areas of physical chemistry curriculum, namely: chemical kinetics, quantum chemistry, and thermodynamics. Using astrochemical examples to decipher the abstract nature of the many fundamental physical chemistry concepts, which are usually accompanied by rigorous mathematical treatments, provides a rich ground in which to implement alternative teaching pedagogies and practices that can help the learner master the associated TCs. Chemistry ; Astrochemistry ; Gateway (web page) ; Curriculum ; Chemistry education ; Quantum chemistry ; Nanotechnology ; Mathematics education ; Computer science ; Physics ; Psychology ; Molecule ; Materials science ; Supramolecular chemistry ; Astrophysics ; Organic chemistry ; Quantum mechanics ; World Wide Web ; Pedagogy Various Chemistry Research Topics ; Science Education and Pedagogy
Authors: Evijola Llabani
Source: IDEALS (University of Illinois Urbana-Champaign) (article, 2019-01-16)
Field: Materials Science — Materials Chemistry

Discovery and biological evaluation of ferroptocide The chemical diversification of natural products provides a robust and general method for creation of stereochemically complex and structurally diverse small molecules. The resulting compounds have physicochemical traits different from those in most screening collections, and as such are a rich source for biological discovery. Herein, we subject the complexity-to-diversity library of compounds to an anticancer phenotypic screen. This effort resulted in identification of an active class of compounds derived from pleuromutilin natural product. Biological evaluation identified the novel compound ferroptocide, a small molecule that rapidly and robustly induces ferroptotic death of cancer cells. Target identification efforts and CRISPR knockout studies reveal that ferroptocide has a different target from other known pro-ferroptotic agents; it is an inhibitor of thioredoxin, a key component of the thioredoxin antioxidant system in the cell. Ferroptocide shows the ability to positively modulate the immune system in a murine model of breast cancer and will be a useful tool to study the utility of pro-ferroptotic agents for treatment of cancer. Computer science Corrosion Behavior and Inhibition ; Advanced biosensing and bioanalysis techniques
Authors: Elisabeth Grace Billman-Benveniste | Jacob Franz | Loredana Valenzano
Source: Digital Commons - Michigan Tech (Michigan Technological University) (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Printing 3D Models for Chemistry: A Step-by-Step Open-Source Guide for Hobbyists, Corporate Professionals, and Educators and Students in K-12 and Higher Education 3D-printing is a relatively inexpensive additive manufacturing method used to create tangible objects. As a relatively new technology, the true capability of 3D-printing has not yet been fully exploited, especially in academic and research/teaching settings. Through this work, 3D-printing in a classroom and research environment is employed as a tool for creation of fully customizable, three-dimensional molecular models and for the visualization of theoretical abstract concepts. Open source ; Medical education ; Mathematics education ; Chemistry ; Pedagogy ; Psychology ; Computer science ; Medicine Various Chemistry Research Topics
Authors: Kate J. McKnelly | William J. Howitz | Simon Lam | Renée D. Link
Source: ChemRxiv (preprint, 2019-10-22)
Field: Chemistry — Physical and Theoretical Chemistry

Extraction on Paper, an Active Learning Technique to Facilitate Student Understanding of Liquid-Liquid Extraction &lt;p&gt;Undergraduate students often find it difficult to understand the chemical principles underlying liquid-liquid extractions. Explanations on how extractions work at the molecular level in textbook and internet resources are plentiful, but students still do not seem able to grasp how extractions work before having to perform the technique in a laboratory course. To address this difficulty in understanding, we developed an Extraction on Paper Activity. We envision this activity as a tool to help students understand and apply the chemical principles underlying liquid-liquid extractions outside of and before entering into a laboratory setting.&lt;/p&gt; GRASP ; Extraction (chemistry) ; Computer science ; Liquid–liquid extraction ; Work (physics) ; Chemistry ; Mathematics education ; Chromatography ; Psychology ; Engineering ; Mechanical engineering Various Chemistry Research Topics
Authors: Yasasvi Bommireddy | Ankit Agarwal | Vikas Yettella | Vikas Tomar | Marcial Gonzalez
Source: Mechanics Research Communications (preprint, 2019-06-12)
Field: Materials Science — Biomaterials

Loading-unloading contact law for micro-crystalline cellulose particles under large deformations Materials science ; Ellipsoid ; Hardening (computing) ; Composite material ; Displacement (psychology) ; Strain hardening exponent ; Elasticity (physics) ; Mechanics ; Physics Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Granular flow and fluidized beds
Authors: Andrew Montalbano | Fadel | Li | Coutris
Source: TigerPrints (Clemson University) (article, 2019-01-01)
Field: Materials Science — General Materials Science

Investigation and Optimization of Metastructure with Energy Loss Hyperelastic materials such as rubber provide hysteretic energy loss when loaded. In contrast, elastic materials such as steel or titanium do not. Previous works show that designing a structure consisting of cells of curved-bistable beams produces a structure that provides energy loss. This research focuses on exploring the mechanisms behind energy loss as to maximize the energy loss. Computer science Material Properties and Applications ; Topology Optimization in Engineering ; Engineering Applied Research
Authors: Mahmoud Elsawy | Stéphane Lanteri | Régis Duvigneau | Gauthier Brière | Patrice Genevet
Source: SPIRE - Sciences Po Institutional REpository (article, 2019-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimized 3D metasurface for maximum light deflection at visible range International audience Solver ; Computer science ; Deflection (physics) ; Range (aeronautics) ; Optics ; Materials science ; Optoelectronics ; Electronic engineering ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Erin Carraher | Luke Leither
Source: Scholarworks (University of Massachusetts Amherst) (article, 2019-06-14)
Field: Materials Science — Materials Chemistry

Developing Evidence-based Tools and Resources for Material Selection Building construction costs over a trillion dollars and accounts for half of the non-renewable resources consumed on an annual basis in the US, with materials and equipment comprising three-quarters of these costs. While not the final arbiters, architects play a critical role in determining what materials are specified for construction projects. Material selection in architecture has historically been taught through high- level lectures accompanied by empirical, evidence- based exercises and precedent studies during school followed by “in the field” experience for interns in practice. While there are many great resources that discuss material properties and analyze the use of specific materials in iconic projects through a case study approach, there is a significant gap in the literature and support materials when it comes to how and why materials are selected in the first place. With the rapidly evolving nature of digital tools, ever-expanding range of materials available on the market, and increasing standards for building performance, there has never been a greater need for comprehensive resources to support architects’ and educators’ understanding of the interconnected factors that influence and support informed decisions that are justifiable to all project stakeholders. This paper presents the problem-setting process; findings from first-hand interviews with almost twenty practitioners at leading firms in New York City, Chicago, and San Francisco that have been recognized for their thoughtful use of materials; and plans for the next targeted phase of the work. University research seed funding supported the initial phase of this research, which was designed to validate assumptions about the unique nature of the material selection process. We plan that this study will serve as the first step toward developing codified resources to support a more evidence-based approach in education and practice. Selection (genetic algorithm) ; Computer science ; Risk analysis (engineering) ; Business ; Artificial intelligence Material Selection and Properties
Authors: Robert Valencia Agresoth | Villorina Ronal | Enrique Casais Padilla | Jaime Caballero | Sol Lázaro | Fabián E. Hernández | Andrés F. Jaramillo | Luis Anaya-Tatis | Kevin H. Libreros | Víctor Palencia | Víctor Atencio | Manuel Palencia | Ricardo Villalobos‐Carvajal | D. Rojas | Andrés Díaz-Campos | Jesús Ramírez | Jorge López-Villarreal | Virginia Castro-Peña | Francisco Solís-Pomar | C. D. Gutiérrez-Lazos | Manuel Meléndrez | E. Martı́nez | Eduardo Echevarría Pérez | Yuliana Sánchez | Mayra A. Mora | W Conard | W Nardin Favaro | A Lpez-Castillo
Source: Journal of Science with Technological Applications (article, 2019-05-01)
Field: Chemistry — Physical and Theoretical Chemistry

Periodic Table of elements: 'understanding of set from behavior of the elements' Set (abstract data type) ; Table (database) ; Periodic table ; Mathematics ; Computer science ; Physics ; Data mining ; Programming language History and advancements in chemistry ; Chemical Thermodynamics and Molecular Structure ; X-ray Diffraction in Crystallography
Authors: E. E. Osawa-Martínez | Benito Minjarez | Moisés Martín Morales-Rivera | Salvador Mena
Source: Journal-Agrarian and Natural Resource Economics (article, 2019-06-30)
Field: Chemistry — Spectroscopy

Use and application of bioinformatics for the characterization of plant proteomes Proteomics and some other cutting-edge technologies have generated information clusters in sequencing and protein studies for plants, which can be used in other areas, such as food in quality control, pharmacological in allergens, characterizations of organisms in studies biological and agronomic for vegetables. The following is a description of the information that can be found in the databases (DB) and their interrelations with other specialized DB, of all the references to describe a protein. For this investigation we used a storage protein, Glutelin-2 in (Zea mays), we show some of the interrelated DB that can offer information for multiple studies of proteins in plants like UniProt KB and STRING-DB. UniProt ; Proteome ; Proteomics ; Computational biology ; Computer science ; Glutelin ; Storage protein ; Biotechnology ; Biology ; Bioinformatics ; Genetics ; Gene Advanced Proteomics Techniques and Applications
Authors: 亮太 清水 | Ryota Shimizu | 成 小林 | Shigeru Kobayashi | 太郎 一杉 | Taro Hitosugi
Source: Institutional Repositories DataBase (IRDB) (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

AI-Robot-driven Materials Research for Inorganic Materials Quality (philosophy) ; Materials testing ; Work (physics) ; Raw material Machine Learning in Materials Science ; Nanoporous metals and alloys ; Advanced Sensor and Energy Harvesting Materials
Authors: Taissa R. Michel
Source: Rowan Digitals Works (Rowan University) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Functional porous polydimethlysiloxane as piezoresistive and piezoelectric materials In this paper, polydimethylsiloxane (PDMS), carbon nanotubes (CNTs), and zinc oxide (ZnO) were combined to create functionalized piezoresistive and piezoelectric sensors for pressure sensing and energy harvesting. Samples were foamed to show that the increased deformability of the foam sensors makes them suitable for a range of applications including dexterous robotics, tactile sensing, energy harvesting, and biosensing. Uniform dispersion of CNTs was achieved with chloroform as the solvent. Samples were foamed using chemical blowing and scaffolding but granulated sugar at 70% porosity resulted in foamed samples with the most consistent mechanical properties. Samples underwent tensile and compressive testing for their mechanical properties. These test's results showed that introducing pores did not significantly degrade sensor performance. Porous devices are more ductile and use less materials than their bulk counterparts. Piezoresistive sensors with 3.5% CNTs yielded the highest sensitivity with a Young's modulus of 0.42 MPa. To further functionalize the devices, ZnO is mixed into the samples to produce piezoelectric devices. Dipole alignment is done in an attempt to increase the output power of piezoelectric devices. This resulted in a 5x increase in performance of the devices and further research needs to be conducted. Overall, porous PDMS functions for both piezoelectric and piezoresistive device applications. Piezoresistive effect ; Materials science ; Composite material Silicone and Siloxane Chemistry ; Synthesis and properties of polymers
Authors: Frédéric Chapelle | Dimitri Gouot | Jean‐Jacques Lemaire | Yuri Lapusta
Source: ELARTU (Ternopil National Technical University) (article, 2019-09-24)
Field: Materials Science — General Materials Science

On the interest of reliability methods in robots design integrating smart materials International audience Reliability (semiconductor) ; Computer science ; Robot ; Reliability engineering ; Engineering ; Artificial intelligence Material Properties and Applications
Authors: Jingyu Qu | David Zageceta | Weiwei Zhang | Qiang Zhu
Source: Open MIND (preprint, 2019-09-09)
Field: Materials Science — Materials Chemistry

High Dielectric Ternary Oxides from Crystal Structure Prediction and High-throughput Screening The development of new high dielectric materials is essential for advancement in modern electronics. Oxides are generally regarded as the most promising class of high dielectric materials for industrial applications as they possess both high dielectric constants and large band gaps. Most previous researches on high dielectrics were limited to already known materials. In this study, we conducted an extensive search for high dielectrics over a set of ternary oxides by combining crystal structure prediction and density functional perturbation theory calculations. From this search, we adopted multiple stage screening to identify 441 new low-energy high dielectric materials. Among these materials, 33 were identified as potential high dielectrics favorable for modern device applications. Our research has opened an avenue to explore novel high dielectric materials by combining crystal structure prediction and high throughput screening Dielectric ; Ternary operation ; Materials science ; High-κ dielectric ; Crystal (programming language) ; High-throughput screening ; Characterization (materials science) ; Engineering physics ; Optoelectronics ; High energy ; Nanotechnology ; Electronic engineering ; Computer science ; Chemistry ; Physics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials