jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ivan Novikov | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2018-08-11)
Field: Materials Science — Materials Chemistry

Improving accuracy of interatomic potentials: more physics or more data?\n A case study of silica In this paper we test two strategies to improving the accuracy of\nmachine-learning potentials, namely adding more fitting parameters thus making\nuse of large volumes of available quantum-mechanical data, and adding a\ncharge-equilibration model to account for ionic nature of the SiO2 bonding. To\nthat end, we compare Moment Tensor Potentials (MTPs) and MTPs combined with the\ncharge-equilibration (QEq) model (MTP+QEq) fitted to a density functional\ntheory dataset of alpha-quartz SiO2-based structures. In order to make a\nmeaningful comparison, in addition to the accuracy, we assess the uncertainty\nof predictions of each potential. It is shown that adding the QEq model to MTP\ndoes not make any improvement over the MTP potential alone, while adding more\nparameters does improve the accuracy and uncertainty of its predictions.\n Moment (physics) ; Chemistry ; Charge (physics) ; Moment tensor ; Tensor (intrinsic definition) ; Experimental data ; Ionic bonding ; Statistical physics ; Computational chemistry ; Physics ; Quantum mechanics ; Mathematics ; Ion ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; High-pressure geophysics and materials
Authors: Ivan S. Novikov | Alexander V. Shapeev
Source: arXiv (Cornell University) (preprint, 2018-08-11)
Field: Materials Science — Materials Chemistry

Improving accuracy of interatomic potentials: more physics or more data? A case study of silica In this paper we test two strategies to improving the accuracy of machine-learning potentials, namely adding more fitting parameters thus making use of large volumes of available quantum-mechanical data, and adding a charge-equilibration model to account for ionic nature of the SiO2 bonding. To that end, we compare Moment Tensor Potentials (MTPs) and MTPs combined with the charge-equilibration (QEq) model (MTP+QEq) fitted to a density functional theory dataset of alpha-quartz SiO2-based structures. In order to make a meaningful comparison, in addition to the accuracy, we assess the uncertainty of predictions of each potential. It is shown that adding the QEq model to MTP does not make any improvement over the MTP potential alone, while adding more parameters does improve the accuracy and uncertainty of its predictions. Moment (physics) ; Moment tensor ; Charge (physics) ; Chemistry ; Experimental data ; Tensor (intrinsic definition) ; Ionic bonding ; Computational chemistry ; Statistical physics ; Physics ; Quantum mechanics ; Mathematics ; Ion ; Statistics ; Organic chemistry Machine Learning in Materials Science ; High-pressure geophysics and materials ; X-ray Diffraction in Crystallography
Authors: Stephen Exarhos
Source: eScholarship (California Digital Library) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Nanoparticle Matter: Synthesis, Characterization, Control, and Application of YSZ, CZTS, and ZrN As a material is reduced down to sub-100 nm dimensions, its interaction with light, with heat, and with other matter changes due in part to increased confinement of free charges and to an increased surface area relative to volume. In practice, different materials and their characteristics can be tuned to control bulk-system properties like optical transparency, free charge generation, electric field enhancement, and localized thermal enhancement. In this dissertation, I will discuss the controlled synthesis and characterization of three different nanoparticle material systems: yttria-stabilized zirconia (YSZ), copper-zinc-tin-sulfide (CZTS), and zirconium nitride (ZrN). I will additionally discuss the viability of using the produced materials in proposed applications, namely: YSZ as the basis material for transparent sintered ceramic disks for use as cranial implants; CZTS as the basis material for earth-abundant, inexpensive, polycrystalline thin film photovoltaics; and ZrN as a visible spectrum plasmonic absorbing material for use in light-induced localized field enhancement applications. CZTS ; Materials science ; Characterization (materials science) ; Nanoparticle ; Cubic zirconia ; Photovoltaics ; Tin ; Nanotechnology ; Zirconium nitride ; Ceramic ; Crystallite ; Optoelectronics ; Nitride ; Metallurgy ; Thin film ; Photovoltaic system ; Titanium nitride Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Fleur Legrain | Ambroise van Roekeghem | Stefano Curtarolo | Jesús Carrete | Georg K. H. Madsen | Natalio Mingo
Source: arXiv (Cornell University) (preprint, 2018-03-26)
Field: Materials Science — Materials Chemistry

Vibrational Properties of Metastable Polymorph Structures by Machine\nLearning Despite vibrational properties being critical for the ab initio prediction of\nthe finite temperature stability and transport properties of solids, their\ninclusion in ab initio materials repositories has been hindered by expensive\ncomputational requirements. Here we tackle the challenge, by showing that a\ngood estimation of force constants and vibrational properties can be quickly\nachieved from the knowledge of atomic equilibrium positions using machine\nlearning. A random-forest algorithm trained on only 121 metastable structures\nof KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\\textrm\\AA^2$ for the\ninteratomic force constants, and it is much less expensive than training the\ncomplete force field for such compound. The predicted force constants are then\nused to estimate phonon spectral features, heat capacities, vibrational\nentropies, and vibrational free energies, which compare well with the ab initio\nones. The approach can be used for the rapid estimation of stability at finite\ntemperatures.\n Metastability ; Ab initio ; Force field (fiction) ; Stability (learning theory) ; Force constant ; Phonon ; Ab initio quantum chemistry methods ; Materials science ; Computational chemistry ; Molecular physics ; Thermodynamics ; Statistical physics ; Chemistry ; Molecule ; Physics ; Computer science ; Machine learning ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; Inorganic Chemistry and Materials
Authors: Hendrik H. Heenen
Source: mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Multiscale Modeling of Disorder in Solid-State Battery Materials Modern battery materials are governed by a considerable degree of disorder on the atomic scale. This likely impacts the materials’ properties and affects their performance in battery applications. In this thesis, computational approaches are addressed which describe the atomic disorder and its long-range effects within relevant ensembles. These are elaborated alongside example problems with a focus on ion diffusion mechanisms and their implications on ion conductivity at macroscopic scales. Battery (electricity) ; Multiscale modeling ; Atomic units ; Scale (ratio) ; Solid-state ; Range (aeronautics) ; Nanotechnology ; Computer science ; Materials science ; Statistical physics ; Engineering physics ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry ; Power (physics) Electrostatics and Colloid Interactions ; Surface and Thin Film Phenomena ; Zeolite Catalysis and Synthesis
Authors: Antonio Di Bartolomeo | Filippo Giubileo | Alessandro Grillo | Laura Iemmo | Giuseppe Luongo | Francesca Urban
Source: Research Explorer (The University of Manchester) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

MoS2 and WSe2 in field effect transistors Field (mathematics) ; Field-effect transistor ; Transistor ; Materials science ; Electrical engineering ; Optoelectronics ; Environmental science ; Engineering physics ; Computer science ; Physics ; Engineering ; Mathematics ; Voltage 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Semiconductor materials and interfaces
Authors: Rasmus Andersson
Source: Chalmers Research (Chalmers University of Technology) (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Novel Multi-Scale Modeling Framework for Structure and Transport in Complex Battery Electrolytes Affordable high energy rechargeable batteries are crucial for further electrification of the transport sector, which is necessary in order to contribute to limit our CO2 emissions to acceptable levels. While today’s lithium-ion batteries (LIBs) have indeed initiated the electrification of the transportation section successfully, electric vehicles are still expensive and typically have ranges limited to ca. 100-500 km depending on price class. There are also safety concerns with LIBs and limited abundance of necessary materials why new chemistries, and especially new electrolytes, need to be explored. Emerging classes of electrolytes, such as highly concentrated electrolytes, have more complex structures than conventional electrolytes, with implications for the ion transport mechanism. This complexity necessitates a multi-scale modeling approach starting at the atomic level to gain further fundamental understanding. This thesis outlines a framework where ab initio molecular dynamics initially is used to simulate small periodic systems (∼100 - 1000 atoms) over relatively short time spans (∼1 ps) to obtain trajectories that subsequently are used to train the parameters of a classical force field by force matching. This optimization is performed over all parameters simultaneously by a genetic algorithm. The force fields developed are then used to simulate larger systems (∼1000 - 100 000 atoms) over longer time scales classically (∼1 ns - 1μs). The resulting trajectories are used to collect statistics for a hierarchical analysis, which resolves the structure in terms of dynamic clusters, and quantifies the life-time distribution, population dynamics, and transport properties of identified clusters and non-covalent bonds. The method is ultimately to be of general use to both qualitatively and quantitatively elucidate the ion transport mechanism in novel types of electrolytes as a function of composition. Electrification ; Electrolyte ; Battery (electricity) ; Multiscale modeling ; Population ; Scale (ratio) ; Computer science ; Ab initio ; Statistical physics ; Chemical physics ; Chemistry ; Engineering ; Power (physics) ; Electricity ; Physics ; Computational chemistry ; Electrical engineering ; Thermodynamics Electrostatics and Colloid Interactions ; Scientific Research and Discoveries ; Advanced Battery Technologies Research
Authors: Corey Oses | Eric Gossett | David Hicks | Frisco Rose | Michael J. Mehl | Eric Perim | Ichiro Takeuchi | Stefano Sanvito | Matthias Scheffler | Yoav Lederer | Ohad Levy | Cormac Toher | Stefano Curtarolo
Source: arXiv (Cornell University) (article, 2018-06-18)
Field: Materials Science — Materials Chemistry

AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability analysis $\textit{A priori}$ prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically-competing structures at formation conditions. Large materials repositories $\unicode{x2014}$ housing properties of both experimental and hypothetical compounds $\unicode{x2014}$ offer a path to prediction through the construction of informatics-based, $\textit{ab-initio}$ phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability. Herein, a new module is presented for autonomous thermodynamic stability analysis implemented within the open-source, $\textit{ab-initio}$ framework AFLOW. Powered by the AFLUX Search-API, AFLOW-CHULL leverages data of more than 1.8 million compounds currently characterized in the AFLOW.org repository and can be employed locally from any UNIX-like computer. The module integrates a range of functionality: the identification of stable phases and equivalent structures, phase coexistence, measures for robust stability, and determination of decomposition reactions. As a proof-of-concept, thorough thermodynamic characterizations have been performed for more than 1,300 binary and ternary systems, enabling the identification of several candidate phases for synthesis based on their relative stability criterion $\unicode{x2014}$ including 18 promising $C15_{b}$-type structures and two half-Heuslers. In addition to a full report included herein, an interactive, online web application has been developed showcasing the results of the analysis, and is located at aflow.org/aflow-chull. Computer science ; Stability (learning theory) ; Phase diagram ; Identification (biology) ; Unicode ; Software ; Cloud computing ; Phase (matter) ; Data mining ; Computational science ; Theoretical computer science ; Operating system ; Physics ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Zachary Benjamin Weinstein
Source: Iowa State University Digital Repository (Iowa State University) (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Synthesis of aromatic dicarboxylates and unique approaches to automated synthesis The research presented and discussed in this dissertation involves the synthesis of aromatic dicarboxylates via catalytic isomerization or disproportionation, the synthesis of amines via catalytic hydrosilylation of amides and the development of new methods and approaches for studying catalytic reactions on an automated synthesis platform. The automated synthesis platform was utilized for studying kinetics of oxidation reactions of cyclohexane, as well as the synthesis of small molecules in specialized reactors and scalable quantities of heterogeneous catalysts.\nAromatic dicarboxylic acids such as terephthalic acid or 2,5-furandicarboxylic acid can be prepared through catalytic disproportionation or isomerization of their respective metal carboxylate precursors. Copper(I) iodide catalyzes the disproportionation of potassium furoate to 2,5-furandicarboxylate in up to 85% yield at temperatures of 280 – 300 °C under a carbon dioxide rich atmosphere (40 bar). CuI also catalyzes the disproportionation of potassium benzoate to terephthalate in up to 60% yield at temperatures of 320 – 350 °C under carbon dioxide (40 bar).\nXantphosRh(cod)BArF (cod = 1,5-cyclooctadiene; BArF = Tetrakis[3,5-bis(trifluoromethyl)phenyl]borate) was demonstrated to be an excellent catalyst for the reduction of secondary and tertiary amides to secondary and tertiary amines by phenylsilane. Most substrates could be converted at mild temperatures (25 – 60 °C) within 1 h. Tertiary amides required 2 equiv. of PhSiH3 for full conversion. Secondary amides exhibited side reactions involving disproportionation of N-R and N-H bonds. These side reactions were inhibited by the addition of 10 equiv. of PhSiH3.\nAn automated synthesis robot (Chemspeed SWING-XL) was used to prepare surface supported catalysts of Pd on CeO2, SiO2, TiO2 for hydrogenation reactions. These materials were prepared by iterative additions of a dilute pre-catalyst solution followed by slow evaporation of solvent. These materials were used as catalysts for hydrogenolysis experiments, affording cyclohexanol from guaiacol. This instrument was also used to study the kinetics of cyclohexane oxidation reactions catalyzed by a series [tris(oxazolinyl)borato]cobalt compounds previously reported in our group. Finally, this same instrument was used to prepare synthetically challenging diene compounds, by slowly adding catalyst to a heated, pressurized reactor using high-pressure peristaltic pumps. Computer science ; Business Inorganic and Organometallic Chemistry
Authors: Enrico Berardo | Lukas Turcani | Marcin Miklitz | Kim E. Jelfs
Source: ChemRxiv (preprint, 2018-08-10)
Field: Chemistry — Organic Chemistry

An Evolutionary Algorithm for the Discovery of Porous Organic Cages The phase space of possible supramolecular materials is enormous, as they can, in principle, be built from any combination of organic building blocks. Here we have developed an evolutionary algorithm (EA) that can assist in the efficient exploration of chemical space for supramolecules, helping to guide synthesis to materials with promising applications. We demonstrate the utility of our EA to porous organic cages, predicting both promising targets and identifying the chemical features that emerge as important for a cage to be shape persistent or to adopt a particular cavity size. We identify that shape persistent cages require a low percentage of rotatable bonds in their precursors (&lt;20%) and that the higher topicity building block in particular should use &lt;p&gt;double bonds for rigidity. We can use the EA to explore what size ranges for precursors are required for achieving a given pore size in a cage and show that 16 Å pores, which are absent in the literature, should be synthetically achievable. Our EA implementation is adaptable and easily extendable, not only to target specific properties of porous&lt;/p&gt; &lt;p&gt;organic cages, such as optimal encapsulants or molecular separation materials, but also to any easily calculable property of other supramolecular materials.&lt;/p&gt; Supramolecular chemistry ; Porosity ; Chemical space ; Nanotechnology ; Materials science ; Block (permutation group theory) ; Rigidity (electromagnetism) ; Cage ; Porous medium ; Computer science ; Algorithm ; Biological system ; Chemistry ; Crystallography ; Mathematics ; Combinatorics ; Drug discovery ; Biology ; Composite material ; Crystal structure Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Nanopore and Nanochannel Transport Studies
Authors: Travis Waller
Source: eScholarship (California Digital Library) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Investigation of Titanium Dioxide Engineered Nanomaterials in Microbially Driven Environments This dissertation characterized two forms of TiO2 engineered nanomaterials (ENMs) to determine the role of solution conditions in mediating differences in the environmental impacts of emerging nanomaterials. In general, ENMs designed for specific product matrices are functionalized to impart distinct characteristics capable of altering the environmental fate and transport (i.e. shape and surface composition). Observations of TiO2 ENM release during product life cycles presents a concern as nanoparticles with unique characteristics begin accumulating in environmental systems. TiO2 nanomaterials were introduced to three consecutive, environmental systems with microbial presence to characterize environmental implications of ENM presence, namely: a model human colon, septic tank, and a quartz sand filtration column. The following critical findings resulted from this dissertation research. Colonic exposure to food (FG) and industrial (IG) grade TiO2 inhibited a natural shift in microbial composition from Proteobacteria to Firmicutes phyla, with FG TiO2 having the greater impact. Colonic pH levels decreased from IG and FG TiO2 exposure where FG TiO2 (pH 4) again had the greatest impact compared to IG TiO2 (pH 5) and the control (pH 6). Therefore, inherent physical and chemical properties of FG and IG TiO2 can produce different microbial responses in the colonic environment. Next, exposure of the septic system to IG TiO2 particles had minimal effect on the composition of the denitrifying-dominant microbial community, while FG exposures resulted in a mixed functionality community less effective at waste treatment. Nano-FG TiO2 exposure in septic systems may facilitate considerable changes in microbial community activity. Lastly, FG TiO2 exhibited a high degree of stability in septic conditions and both monovalent electrolyte suspensions. Elution of FG and IG TiO2 was greatest in septic effluent at the higher nanoparticle concentration (100 ppm); however, FG TiO2 was well retained at the low (2 ppm) concentration suggesting low elution from the drainage field. Valuable insight into the role that inherent physical and chemical properties of emerging ENMs play in environmental systems is gained from this dissertation research. Additionally, the significance of the solution environment at mediating differences observed between uniquely, engineered nanomaterials sheds light on potential mechanisms for minimizing detrimental environmental implications of nanomaterials. Nanomaterials ; Environmental chemistry ; Firmicutes ; Chemistry ; Microbial population biology ; Titanium dioxide ; Environmental science ; Proteobacteria ; Chemical engineering ; Nanotechnology ; Materials science ; Bacteria ; Biology ; Engineering ; Biochemistry Nanoparticles: synthesis and applications
Authors: Bishal Bhattarai
Source: OhioLink ETD Center (Ohio Library and Information Network) (article, 2018-01-01)
Field: Materials Science — Ceramics and Composites

Ab initio Structure Inversion for Amorphous Materials Advancement and understanding of amorphous materials has been limited by conventional computer simulations [1].A generic approach of "Melt-Quenching" (MQ) with molecular dynamics (MD) has several shortcomings.First: a slow quenching rate limits model size for ab initio method while efficient empirical potentials undermine the accuracy of results.To overcome this restrictions in this dissertation we have modeled large and realistic models of different amorphous systems with an accuracy comparable to density functional theory (DFT) calculations.We have used a recently developed method Force Enhanced Atomic Refinement (FEAR) [2,3].FEAR offers the logical advantage of Ab initio ; Amorphous solid ; Inversion (geology) ; Geology ; Materials science ; Crystallography ; Physics ; Chemistry ; Paleontology ; Quantum mechanics Glass properties and applications