jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andreas Jeindl | Lukas Hörmann | Oliver T. Hofmann
Source: Applied Surface Science (preprint, 2021-10-28)
Field: Materials Science — Materials Chemistry

How much does surface polymorphism influence the work function of organic/metal interfaces? Molecules adsorbing on metal surfaces form a variety of different surface polymorphs. How strongly this polymorphism affects interface properties is a priori unknown. In this work we investigate how strongly the surface polymorphism influences the interface work functions for various organic/metal interfaces. To evaluate the whole bandwidth of possible polymorphs, we perform full theoretical structure search, probing millions of polymorph candidates. All of these candidates might be observed in reality, either by kinetic trapping or by thermodynamic occupation. Employing first-principles calculations and machine learning we predict and analyze the work function changes for those millions of candidates for three physically distinct model systems: the weakly interacting naphthalene on Cu(111), the strongly interacting anthraquinone on Ag(111), and tetracyanoethylene, which undergoes a reorientation from lying to standing polymorphs on the Cu(111) surface. These thorough investigations indicate that kinetic trapping of flat-lying molecules can lead to work function differences of a few hundred meV. If the molecules also reorientate, this can increase to a change of several eV. We further show that the spread in work function decreases when working in thermodynamic equilibrium, but thermally occupied phases still lead to an intrinsic uncertainty at elevated temperatures. Work function ; Chemical physics ; Chemistry ; Metal ; Molecule ; Polymorphism (computer science) ; Naphthalene ; Work (physics) ; Materials science ; Thermodynamics ; Crystallography ; Physics ; Organic chemistry Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; nanoparticles nucleation surface interactions
Authors: Sheena Agarwal | Kavita Joshi
Source: ChemRxiv (preprint, 2021-06-04)
Field: Materials Science — Materials Chemistry

Looking Beyond Adsorption Energies to Understand Interactions at Surface Using Machine Learning Abstract&lt;br&gt;Identifying factors that influence interactions at the surface is still an active area of research. In this study, we present the importance of analyzing bondlength activation, while interpreting Density Functional Theory (DFT) results, as yet another crucial indicator for catalytic activity. We studied the&lt;br&gt;adsorption of small molecules, such as O 2 , N 2 , CO, and CO 2 , on seven face-centered cubic (fcc) transition metal surfaces (M = Ag, Au, Cu, Ir, Rh, Pt, and Pd) and their commonly studied facets (100, 110, and 111). Through our DFT investigations, we highlight the absence of linear correlation between adsorption energies (E ads ) and bondlength activation (BL act ). Our study indicates the importance of evaluating both to develop a better understanding of adsorption at surfaces. We also developed a Machine Learning (ML) model trained on simple periodic table properties to predict both, E ads and BL act . Our ML model gives an accuracy of Mean Absolute Error (MAE) ∼ 0.2 eV for E ads predictions and 0.02 Å for BL act predictions. The systematic study of the ML features&lt;br&gt;that affect E ads and BL act further reinforces the importance of looking beyond adsorption energies to get a full picture of surface interactions with DFT.&lt;br&gt; Adsorption ; Density functional theory ; Chemistry ; Molecule ; Computational chemistry ; Metal ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Yen‐Lin Chen | Lois Pollack
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Decoding hidden structural information in solution wide-angle X-ray scattering (WAXS): from ensemble modeling to machine learning Solution X-ray scattering is a powerful method for determining in vitro macromolecular structures. Small-angle Xray scattering (SAXS) has been used to investigate global shape and size of a myriad of molecules at 10s-100 resolution. Its extension, wide-angle X-ray scattering (WAXS), sharpens the resolution of solution scattering to below 10 , enhancing information about macromolecular structures. These WAXS profiles, although informative, can be challenging to interpret, both as a result of contributions of the solvent, and the unknown structures related to the finer features of the solute. We applied WAXS to study the salt dependent structures of a short (12-base pair long) RNA duplexes. With a structural pool containing a variety of conformations, we leveraged ensemble modeling to understand the RNA's solution conformations [1]. Furthermore, we applied trained supervised machine learning (ML) models, mapping WAXS profiles to structural features, to interpret the noisy, yet information rich experimental WAXS profiles [2]. These well-trained ML models faithfully captured the WAXS fingerprints of RNAs. Importantly, these models identified specific scattering angles, or regions of scattering angles, which reflect (and can be used to predict) distinct structural parameters (WAXS feature importance). Therefore, we demonstrated that the ensemble and ML methods can provide novel frameworks for extracting highly relevant structural information from solution experiments on biological macromolecules. Decoding methods ; Small-angle X-ray scattering ; Materials science ; Scattering ; X-ray ; Ensemble learning ; Crystallography ; Computer science ; Artificial intelligence ; Optics ; Physics ; Algorithm ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Astrid Tarleton | Jorge C. Garcia-Alvarez | Anah Wynn | Cade Awbrey | Tomas Roberts | Samer Gozem
Source:  (preprint, 2021-10-11)
Field: Chemistry — Organic Chemistry

OS100: A Benchmark Set of 100 Digitized UV-Visible Spectra and Derived Experimental Oscillator Strengths The scientific method involves validating computational theories and methods against experimental results. However, the comparison between theory and experiments is not always straightforward; in UV-visible spectroscopy, experiments provide a plot of wavelength-dependent molar extinction/attenuation coefficients (ε) while computations typically provide single-valued excitation energies and oscillator strengths (ƒ) for each band. ε and ƒ are related, but this relation is complicated by various broadening and solvation effects. We describe a protocol to fit and integrate experimental UV-visible spectra to obtain ƒexp values for absorption bands and to estimate the uncertainty in the fitting. We apply this protocol to derive 164 ƒexp values from 100 organic molecules ranging in size from 6-34 atoms. The corresponding computed oscillator strengths (ƒcomp) are obtained with time-dependent density functional theory and a polarizable continuum solvent model. By expressing experimental and computed absorption strengths using a common quantity, we directly compare ƒcomp and ƒexp. While ƒcomp and ƒexp are well correlated (linear regression R2=0. 914), ƒcomp in most cases significantly overestimates ƒexp (regression slope=1.31). The agreement between absolute ƒcomp and ƒexp values is substantially improved by accounting for a solvent refractive index factor, as suggested in some derivations in the literature. The 100 digitized UV-visible spectra are included as plain text files in the supporting information to aid in benchmarking computational or machine-learning approaches that aim to simulate realistic UV-visible absorption spectra. Computational physics ; Spectral line ; Polarizable continuum model ; Radiative transfer ; HITRAN ; Absorption spectroscopy ; Solvation ; Chemistry ; Physics ; Optics ; Molecule ; Quantum mechanics Free Radicals and Antioxidants ; Spectroscopy and Quantum Chemical Studies ; Water Quality Monitoring and Analysis
Authors: Daria Osipova
Source: Aaltodoc (Aalto University) (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Nanostructured carbon from biomass as a catalyst for energy conversion devices The growth in demand for CO2 free energy sources stimulates the development of more metal-air batteries (MABs) and sustainable catalyst alternatives from renewable or waste resources. In this project some abattoir wastes were applied to obtain porous carbon-based bifunctional electrocatalysts for the MABs. The porous carbon structure obtained from bones were functionalised with transition metals to improve electrocatalyst activities toward oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), occurring in rechargeable MAB cathodes. The obtained electrocatalysts provide a more economical and sustainable alternative for commercially used catalysts proceeding from platinum on carbon black (Pt-C). &#13;\n&#13;\nCatalysts synthesised from bone decorated with transition metals (Fe, Co) could demonstrate eminent ORR and OER activity. Specifically, ORR attained a 4 electron-transfer similar to Pt-C with more positive onset potential (0.92 vs 0.82, NHE). For the OER, better performance was observed with an onset potential of 0.38 V (NHE) compared to 1.48 V (NHE). Researching the production of catalyst form animal waste resulted in the following discoveries: (1) the surface morphology of the catalyst does not show an effect neither on ORR nor on OER; (2) transitional metals improve electrocatalytic activity synergistically; (3) secondary pyrolysis positively affects the catalyst performance. Catalysis ; Biomass (ecology) ; Carbon fibers ; Environmental science ; Energy (signal processing) ; Process engineering ; Materials science ; Nanotechnology ; Chemistry ; Engineering ; Physics ; Ecology Carbon Nanotubes in Composites
Authors: Vaneck Y. Kounga
Source: Lehigh Preserve (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

Surface Preparation of MoTe2 Planar Thin Films for Low Energy Ion Scattering Measurements This thesis describes different surface preparations for molybdenum (IV) telluride (MoTe2) planar thin films to be characterized in a low energy ion scattering (LEIS) instrument. LEIS is a highly sensitive surface characterization technique which can produce spectrum by interacting with the outer most layers of a material. The spectra give qualitative information about the material's surface. Due to the highly sensitive nature of the instrument, being able to clean the material's surface is important to study the uniformity and surface coverage of the MoTe2 thin films. During nucleation and growth, the films are often susceptible to islanding and non-uniform growth. This is important as surface chemistry impacts the performance of the films. In this work, several post-deposition treatments were performed on MoTe2 thin films and later analyzed using LEIS to compare the surface compositions of each treatment. The surface treatments done were a sputter depth profile, a thermal treatment, and exposure to atomic oxygen at various times. It was found that the sputter depth profile would produce the best results from the experiments profiled in this thesis. The sputter depth profile would minimize both the amount of oxygen and carbon contaminants. Without a doubt the sample heating did not noticeably affect the surface of the films. Rather, in the case of MoTe2, the heating started to change the sample itself before it was sufficiently cleaned, so it was not a viable option. The atomic oxygen treatment provided good results. The sub 60 seconds exposure time proved to work favorably however there was still some residual oxygen left on the surface. The significance of the results demonstrates how reactive the Mo is in the film, how susceptible Te is to degradation via sample heating, the highly sensitive nature of the LEIS instrument in terms of thermal treatments and surface characterization, and the best treatment for MoTe2 planar thin films. Thin film ; Low-energy ion scattering ; Sputtering ; Scattering ; Analytical Chemistry (journal) ; Nucleation ; Oxygen ; Ion ; Planar Chemical and Physical Properties of Materials ; Metallurgical and Alloy Processes ; Phase-change materials and chalcogenides
Authors: Carles Martí | Sarah Blanck | Ruben Staub | Sophie Loehlé | Carine Michel | Stephan N. Steinmann
Source: ChemRxiv (preprint, 2021-06-09)
Field: Materials Science — Materials Chemistry

DockOnSurf: A Python code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces We present the open-source python package DockOnSurf which automates the generation and optimization of low-energy adsorption configurations of molecules on extended surfaces and nanoparticles. DockOnSurf is especially geared towards handling polyfunctional flexible adsorbates. The use of this high-throughput workflow allows to carry out the screening of adsorbate-surface configurations in a systematic, customizable and traceable way, while keeping the focus on the chemically relevant structures.The screening strategy consists on splitting the exploration of the adsorbate-surface configurational space into chemically meaningful domains, i.e., by choosing among different conformers to adsorb, surface adsorption sites, adsorbate anchoring points, orientations and allowing dissociation of (acidic) protons. We demonstrate the performance of the main features based on varying examples, ranging from CO adsorption on a gold nanoparticle to sorbitol adsorption on hematite. Through the use of the presented program, we aim to foster efficiency, traceability and ease of use in research within tribology, catalysis, nanoscience and surface science in general. Adsorption ; Python (programming language) ; Nanoparticle ; Nanotechnology ; Materials science ; Dissociation (chemistry) ; Molecule ; Computer science ; Chemistry ; Physical chemistry ; Organic chemistry ; Programming language Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Lubricants and Their Additives
Authors: Atsushi Ishikawa
Source: ChemRxiv (preprint, 2021-10-24)
Field: Materials Science — Materials Chemistry

Combining Generative Adversarial Network and First-Principles Based Microkinetics for Heterogeneous Catalyst Design Microkinetic analysis based on density functional theory (DFT) was combined with a generative adversarial network (GAN) to enable artificial proposal of heterogeneous catalysts based on the DFT-calculated dataset. The approach was applied to the NH3 formation reaction on Ru-Rh alloy surfaces as an example. The NH3 formation turnover frequency (TOF) was calculated by DFT-based microkinetics. Specifically, six elementary reactions (N2 dissociation, H2 dissociation, NHx (x = 1–3) formation, and NH3 desorption) were explicitly considered, and their reaction energies were evaluated by DFT. On the basis of TOF values and atomic compositions, new alloy surfaces were generated using the GAN. This approach successfully generated the surfaces not included in the initial dataset but have higher TOF values. The N2 dissociation reaction was more exothermic for the generated surfaces, leading to higher TOF. The present study shows that automatic improvement of catalyst materials is possible by using the iterative steps of DFT calculation and sample generation by GAN. Dissociation (chemistry) ; Density functional theory ; Catalysis ; Generative grammar ; Exothermic reaction ; Desorption ; Alloy ; Generative adversarial network ; Adsorption ; Computer science ; Adversarial system ; Materials science ; Chemistry ; Computational chemistry ; Physical chemistry ; Artificial intelligence ; Metallurgy ; Organic chemistry ; Deep learning Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Hydrogen Storage and Materials
Authors: Yuqing Song | Bin Qi | M.T. Li | Jixun Xie
Source: Research Square (preprint, 2021-10-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Facile Synthesis of Mn0.68Bi0.32OCl Mix-Crystals And Its Supercapacitive Behavior Abstract Mn 0.68 Bi 0.32 OCl mix-crystals for supercapacitor were successfully synthesized via a facile solid-phase method using Bi(NO 3 ) 3 and MnCl 2 with molar ratio of 1:1 as precursors. The Mn 0.68 Bi 0.32 OCl mix-crystals were characterized by scanning electron microscopy, X-ray diffraction, Brunauer-Emmett-Teller surface area measurements and thermogravimetry and differential scanning calorimetry, respectively. Cyclic voltammetry and galvanostatic charge/discharge technique were performed for the Mn 0.68 Bi 0.32 OCl mix-crystals in 1 M Na 2 SO 4 aqueous solutions; the specific capacitance of Mn 0.68 Bi 0.32 OCl was about 203 F.g -1 at the current density of 3 A. g -1 with a long life time, owing to the high power density of Mn 0.68 Bi 0.32 OCl mix-crystals and the higher surface area, good conductivity, and high stability of the Mn 0.68 Bi 0.32 OCl mix-crystals. Differential scanning calorimetry ; Thermogravimetry ; Scanning electron microscope ; Cyclic voltammetry ; Supercapacitor ; Materials science ; Phase (matter) ; Analytical Chemistry (journal) ; Capacitance ; Chemistry ; Electrochemistry ; Electrode ; Inorganic chemistry ; Physical chemistry ; Composite material ; Organic chemistry Supercapacitor Materials and Fabrication ; Electrical Contact Performance and Analysis ; Gas Sensing Nanomaterials and Sensors
Authors: Shree Sowndarya S. V. | Peter C. St. John | Robert S. Paton
Source: ChemRxiv (preprint, 2021-05-13)
Field: Chemistry — Physical and Theoretical Chemistry

A Quantitative Metric for Organic Radical Persistence Using Thermodynamic and Kinetic Features Long-lived organic radicals are promising candidates for the development of high-performance energy solutions such as organic redox batteries, transistors, and light-emitting diodes. However, “stable” organic radicals that remain unreactive for an extended time and that can be stored and handled under ambient conditions are rare. A necessary but not sufficient condition for organic radical stability is the presence of thermodynamic stabilization, such as conjugation with an adjacent p-bond or lone-pair, or hyperconjugation with a s-bond. However, thermodynamic factors alone do not result in radicals with extended lifetimes: many resonance-stabilized radicals are transient species that exist for less than a millisecond. Kinetic stabilization is also necessary for persistence, such as steric effects that inhibit radical dimerization or reaction with solvent molecules. We describe a quantitative approach to map organic radical stability, using molecular descriptors designed to capture thermodynamic and kinetic considerations. The comparison of an extensive dataset of quantum chemical calculations of organic radicals with experimentally-known stable radical species reveals a region of this feature space where long-lived radicals are located. These descriptors, based upon maximum spin density and buried volume are combined into a single metric, the Radical Stability Score, that outperforms thermodynamic scales based on bond dissociation enthalpies in identifying remarkably long-lived radicals. This provides an objective and accessible metric for used in future molecular design and optimization campaigns. We demonstrate this approach in identifying Pareto-optimal candidates for stable organic radicals. Radical ; Chemistry ; Radical disproportionation ; Kinetic energy ; Computational chemistry ; Chemical physics ; Photochemistry ; Thermodynamics ; Organic chemistry ; Disproportionation Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; CO2 Reduction Techniques and Catalysts
Authors: Angela F. Harper | Steffen Emge | Pieter C. M. M. Magusin | Clare P. Grey | Andrew J. Morris
Source:  (preprint, 2021-11-05)
Field: Chemistry — Spectroscopy

A directed sampling approach for modelling the structural and electronic properties of amorphous alumina Advancing the next generation of materials for solid-state devices requires an understanding of their underlying electronic structure; yet the amorphous nature of many materials hinders progress in creating an atomic-level model. One such material is amorphous alumina, which is used to enhance performance in electronic devices from Li-ion batteries to perovskite solar cells. We develop a method which captures local properties of alumina through experimentally informed direct sampling of static configurations from ab initio molecular dynamics. This model is validated with solid-state nuclear magnetic resonance and X-ray absorption spectroscopy, calculated from the first principles level of theory. We finally construct an average electronic density of states for alumina from our model and observe two highly localized states at the conduction band edge. Amorphous solid ; Materials science ; Electronic structure ; Chemical physics ; Ab initio ; Sampling (signal processing) ; Solid-state ; Perovskite (structure) ; Engineering physics ; Nanotechnology ; Condensed matter physics ; Computer science ; Chemistry ; Physics ; Crystallography ; Telecommunications ; Quantum mechanics Advanced NMR Techniques and Applications ; Nuclear Physics and Applications ; Crystallography and Radiation Phenomena
Authors: Kirill Shmilovich | Sayak Subhra Panda | Anna Stouffer | John D. Tovar | Andrew L. Ferguson
Source: ChemRxiv (preprint, 2021-08-10)
Field: Materials Science — Biomaterials

Hybrid Computational-Experimental Data-Driven Design of Self-Assembling Pi-Conjugated Peptides Biocompatible molecules with electronic functionality provide a promising substrate for biocompatible electronic devices and electronic interfacing with biological systems. Synthetic oligopeptides composed of an aromatic pi-core flanked by oligopeptide wings are a class of molecules that can self-assemble in aqueous environments into supramolecular nanoaggregates with emergent optical and electronic activity. We present an integrated computational-experimental pipeline employing all-atom molecular dynamics simulations and experimental UV-visible spectroscopy within an active learning workflow using deep representational learning and Bayesian optimization to design pi-conjugated peptides programmed to self-assemble into elongated pseudo-1D nanoaggregtes with a high degree of H-type co-facial stacking of the pi-cores. We consider as our design space the 694,982 unique pi-conjugated peptides comprising a quaterthtiophene pi-core flanked by symmetric oligopeptide wings up to five amino acids in length. After sampling only 1181 molecules (~0.17% of the design space) by computation and 28 (~0.004%) by experiment, we identify and experimentally validate a diversity of previously unknown high-performing molecules and extract interpretable design rules linking peptide sequence to emergent supramolecular structure and properties. Conjugated system ; Supramolecular chemistry ; Peptide ; Molecule ; Stacking ; Chemistry ; Oligopeptide ; Combinatorial chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Biochemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Chemical Synthesis and Analysis