jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Avanish Mishra | Ke Ma | Avinash M. Dongare
Source: Journal of Materials Science (article, 2022-07-01)
Field: Materials Science — Materials Chemistry

Virtual diffraction simulations using the quasi-coarse-grained dynamics method to understand and interpret plasticity contributions during in situ shock experiments Crystal twinning ; Plasticity ; Materials science ; Mesoscale meteorology ; Solid mechanics ; Deformation (meteorology) ; Slip (aerodynamics) ; Molecular dynamics ; Diffraction ; Deformation mechanism ; Shock (circulatory) ; Multiscale modeling ; Mechanics ; Statistical physics ; Microstructure ; Thermodynamics ; Physics ; Composite material ; Optics Microstructure and mechanical properties ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research
Authors: Samuel Genheden | Esben Jannik Bjerrum
Source: ChemRxiv (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

PaRoutes: a framework for benchmarking retrosynthesis route predictions We introduce a framework for benchmarking multi-step retrosynthesis methods, i.e. route predictions, called PaRoutes. The framework consists of two sets of 10,000 synthetic routes extracted from the patent literature, a list of stock compounds, and a curated set of reactions on which one-step retrosynthesis models can be trained. PaRoutes also comes with scripts to compute route quality and route diversity quantities that are important for comparing methods. We use the PaRoute framework to compare three methods implemented in the AiZynthFinder software: Monte Carlo tree search (MCTS), Retro*, and a depth-first proof-number search (DFPN) algorithm, all using a template-based one-step retrosynthesis model. It is found that DFPN is inferior to both MCTS and Retro* and cannot be recommended in its current implementation. MCTS and Retro* are on a par with regard to search speed and the ability to find routes in which all starting material is in stock. However, MCTS outperforms Retro* when it comes to route quality and route diversity. MCTS more easily recovers the reference routes and tends to find a diverse set of solutions for a greater portion of the targets. We encourage practitioners and developers to benchmark their algorithms using PaRoutes and we envisage that the framework will become the community standard to compare retrosynthesis route predictions. It is available at https://github.com/MolecularAI/PaRoutes Benchmarking ; Retrosynthetic analysis ; Computer science ; Set (abstract data type) ; Benchmark (surveying) ; Quality (philosophy) ; Data mining ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; RNA and protein synthesis mechanisms
Authors: Shruti Hariyani | Jakoah Brgoch | Florencio García‐Santamaría | Srinivas Sista | James E. Murphy | Anant Setlur
Source: Journal of Applied Physics (article, 2022-11-15)
Field: Materials Science — Materials Chemistry

From lab to lamp: Understanding downconverter degradation in LED packages Downconverters, primarily inorganic phosphors, are critical components in white solid-state LED-based lighting and liquid crystal display backlights. Research efforts have led to a fundamental understanding of a downconverter's absorption, photoluminescence, and efficiency as a function of composition, structure, and processing conditions. However, considerably less work has focused on the reliability of phosphors once they are incorporated into LED packages. Solving these issues is often the final step before the commercialization of new materials, but the significant resources and time required to evaluate and mitigate materials failure are rarely discussed in the literature. In this Perspective, we discuss the need for conducting downconverter reliability testing and the potential of accelerating, screening, and understanding downconverter failure modes. Our focus highlights the mechanisms of failure and discusses how this influences materials selection and the design of different LED packages. We also stress the potential for accelerated reliability testing protocols and note the potential role first-principles calculations and data-driven models could play in establishing the compositional-processing trends for different aspects of downconverter reliability. We close with possible research directions that could improve downconverter reliability and emphasize the importance of assessing a material's (chemical) stability where multiple manufacturing and processing steps can dictate system performance. Reliability (semiconductor) ; Commercialization ; Computer science ; Physics of failure ; Reliability engineering ; Degradation (telecommunications) ; Process engineering ; Systems engineering ; Engineering ; Telecommunications ; Physics Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Ga2O3 and related materials
Authors: Fan Bai | Alan Ritter | Peter B. Madrid | Dayne Freitag | John Niekrasz
Source:  (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

SynKB: Semantic Search for Synthetic Procedures In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures. By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes. Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SynKB has higher recall than Reaxsys, while maintaining high precision. We plan to make SynKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions. Computer science ; Transformer ; Plan (archaeology) ; Information retrieval ; Knowledge base ; Data science ; World Wide Web ; Engineering Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Semantic Web and Ontologies
Authors: Anna Yu. Romanchuk | Valentina Svitelman | Peter D. Blinov | Alena I. Larina | Iurii M. Nevolin | А. В. Егоров | Elena Saveleva | Stepan N. Kalmykov
Source: Frontiers in Nuclear Engineering (article, 2022-08-12)
Field: Chemistry — Inorganic Chemistry

U(VI), Np(V), Eu(III) sorption on goethite: A wide-ranging multiradionuclide dataset and uncertainty-aware parametrization of surface complexation models Obtaining reliable thermodynamic sorption data is essential for establishing databases that can be used for the numerical safety evaluations of radioactive waste storage and disposal facilities. For this purpose, new experimental data on the sorption of U(VI), Np(V), and Eu(III) onto synthesized goethite in a wide range of experimental conditions were collected. Thermodynamic surface complexation models based on different approaches were developed and parametrized to fit a large dataset, including experimental data from the current work as well as available literature data. The proposed heuristic optimization procedure allowed identifying and comparing different parametrization variants and their uncertainties for considered models. The developed models pass the test on additional Cd(II) and Zn(II) data, simultaneously providing a reliable description of the sorption process for five cations and could probably be extended using the obtained linear free energy relationship. Sorption ; Parametrization (atmospheric modeling) ; Goethite ; Heuristic ; Work (physics) ; Experimental data ; Computer science ; Ranging ; Range (aeronautics) ; Chemistry ; Thermodynamics ; Materials science ; Mathematical optimization ; Mathematics ; Physics ; Physical chemistry ; Statistics ; Adsorption Radioactive element chemistry and processing ; Groundwater flow and contamination studies ; Radioactive contamination and transfer
Authors: Fuchun Ge | Lina Zhang | Arif Ullah | Pavlo O. Dral
Source: ChemRxiv (preprint, 2022-03-17)
Field: Materials Science — Materials Chemistry

Four-dimensional spacetime atomistic artificial intelligence models We demonstrate that artificial intelligence (AI) can learn four-dimensional (4D) atomistic systems in the spacetime continuum. Given the initial conditions – nuclear positions and velocities at time zero – the proposed 4D-atomistic AI (4D-A2I) models can predict nuclear positions at any time in the future or past for the simplest systems as we show for H2. For larger polyatomic molecules, AI is capable of learning distant but finite future as we demonstrate for an ethanol molecule. 4D-A2I models provide direct access to a multitude of properties at a given time such as geometries, velocities, forces, and energies which can be used in simulating physicochemical transformations and spectra. Our approach can be used as a cost-efficient alternative to traditional molecular dynamics. We show an example of a 4D-A2I model describing the dynamical behavior of ethanol at the coupled-cluster level with the speed of one nanosecond simulation time per one hour wall-clock time on a single GPU card – a previously unachievable feat with traditional Born–Oppenheimer molecular dynamics. 4D-A2I model is also demonstrated to provide direct access to atomistic time-resolved details of physicochemical transformations. Molecular dynamics ; Spacetime ; Statistical physics ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: David Graff | Matteo Aldeghi | Joseph A. Morrone | Kirk E. Jordan | Edward O. Pyzer‐Knapp | Connor W. Coley
Source: arXiv (Cornell University) (preprint, 2022-05-03)
Field: Materials Science — Materials Chemistry

Self-focusing virtual screening with active design space pruning High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model-guided optimization has been employed to lower these costs through dramatic increases in sample efficiency compared to random selection. However, these techniques introduce new costs to the workflow through the surrogate model training and inference steps. In this study, we propose an extension to the framework of model-guided optimization that mitigates inferences costs using a technique we refer to as design space pruning (DSP), which irreversibly removes poor-performing candidates from consideration. We study the application of DSP to a variety of optimization tasks and observe significant reductions in overhead costs while exhibiting similar performance to the baseline optimization. DSP represents an attractive extension of model-guided optimization that can limit overhead costs in optimization settings where these costs are non-negligible relative to objective costs, such as docking. Computer science ; Workflow ; Inference ; Overhead (engineering) ; Pruning ; Digital signal processing ; Virtual screening ; Mathematical optimization ; Machine learning ; Computer engineering ; Artificial intelligence ; Mathematics ; Drug discovery ; Computer hardware Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: Xiaojuan Hu | Maja-Olivia Lenz | Carsten Baldauf
Source: Scientific Data (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Better force fields start with better data: A data set of cation dipeptide interactions We present a data set from a first-principles study of amino-methylated and acetylated (capped) dipeptides of the 20 proteinogenic amino acids - including alternative possible side chain protonation states and their interactions with selected divalent cations (Ca<sup>2+</sup>, Mg<sup>2+</sup> and Ba<sup>2+</sup>). The data covers 21,909 stationary points on the respective potential-energy surfaces in a wide relative energy range of up to 4 eV (390 kJ/mol). Relevant properties of interest, like partial charges, were derived for the conformers. The motivation was to provide a solid data basis for force field parameterization and further applications like machine learning or benchmarking. In particular the process of creating all this data on the same first-principles footing, i.e. density-functional theory calculations employing the generalized gradient approximation with a van der Waals correction, makes this data suitable for first principles data-driven force field development. To make the data accessible across domain borders and to machines, we formalized the metadata in an ontology. Force field (fiction) ; van der Waals force ; Chemistry ; Protonation ; Computer science ; Domain (mathematical analysis) ; Field (mathematics) ; Computational chemistry ; Molecule ; Mathematics ; Artificial intelligence ; Pure mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Dmitri Kalaev | Han Gil Seo | Harry L. Tuller
Source: Nanophotonics (article, 2022-06-10)
Field: Materials Science — Polymers and Plastics

Temporal and spatial tuning of optical constants in praseodymium doped ceria by electrochemical means Temporal and spatial tuning of the refractive index of optical thin films is desired for flat optics applications. The redistribution of mobile ions in mixed ionic-electronic conductors (MIEC) has been demonstrated to serve as a viable means for achieving optical tuning down to the nanoscale. Here we studied the dynamic range of the optical tuning achievable in the refractive index, in the MIEC oxide - Pr <sub><i>x</i></sub> Ce<sub>1-<i>x</i></sub> O<sub>2-<i>δ</i></sub> (PCO), for <i>x</i> = 0.1, 0.2 and 0.4, at 500 °C, by <i>in-situ</i> spectrophotometry. Significant increases in the modulation of both the imaginary and real optical constants in the visible and the adjacent spectra were obtained for increased doping levels. Device employing an electrochemical titration method was implemented to modulate the oxygen concentration, and thereby the optical transmission of PCO. Incorporation of a patterned top electrode allowed for the demonstration of spatial control of PCO thin film properties by <i>in-situ</i> video imaging of the optical switching process. The electrochemically induced optical state is shown to remain non-volatile upon quenching the device to room temperature under applied bias. Praseodymium ; Doping ; Nanomaterials ; Materials science ; Electrochemistry ; Optoelectronics ; Nanotechnology ; Physical chemistry ; Chemistry ; Metallurgy ; Electrode Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Phase-change materials and chalcogenides
Authors: Uchenna Luvia Ezeamaku | Chinyere Ezekannagha | Ochiagha Innocent Eze | Nkiru Odimegwu | Angela Nwakaudu | Amarachukwu Okafor | Innocent Ekuma | Okechukwu Dominic Onukwuli
Source: Arab Gulf Journal of Scientific Research (article, 2022-12-14)
Field: Materials Science — Polymers and Plastics

The impact of potassium permanganate (KMnO<sub>4</sub>) treatment on the tensile strength of pineapple leaf fiber reinforced with tapioca-based bio resin Purpose The impact of potassium permanganate (KMnO 4 ) treatment on the tensile strength of an alkali-treated pineapple leaf fiber (PALF) reinforced with tapioca-based bio resin (cassava starch) was studied. Design/methodology/approach The PALF was exposed to sodium hydroxide (NaOH) treatment in varying concentrations of 2.0, 3.7, 4.5 and 5.5g prior to the fiber treatment with KMnO 4 . The treated and untreated PALFs were reinforced with tapioca-based bio resin. Subsequently, they were subjected to Fourier transform infrared (FTIR) and tensile test analysis. Findings The FTIR analysis of untreated PALF revealed the presence of O-H stretch, N-H stretch, C=O stretch, C=O stretch and H-C-H bond. The tensile test result confirmed the highest tensile strength of 35N from fiber that was reinforced with 32.5g of cassava starch and treated with 1.1g of KMnO 4 . In comparison, the lowest tensile strength of 15N was recorded for fiber reinforced with 32.5g of cassava starch without KMnO 4 treatment. Originality/value Based on the results, it could be deduced that despite the enhancement of bioresin (cassava starch) towards strength-impacting on the fibers, KMnO 4 treatment on PALF is very vital for improved tensile strength of the fiber when compared to untreated fibers. Hence, KMnO 4 treatment on alkali-treated natural fibers preceding reinforcement is imperative for bio-based fibers. Ultimate tensile strength ; Potassium permanganate ; Materials science ; Fiber ; Sodium hydroxide ; Fourier transform infrared spectroscopy ; Composite material ; Starch ; Nuclear chemistry ; Tensile testing ; Alkali metal ; Natural fiber ; Chemistry ; Chemical engineering ; Food science ; Organic chemistry Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications
Authors: Anna Karagianni | Julian Quodbach | Oliver Weingart | Anastasia Tsiaxerli | Vasiliki Katsanou | Vera Vasylyeva | Christoph Janiak | Kyriakos Kachrimanis
Source: Solids (article, 2022-02-10)
Field: Chemistry — Physical and Theoretical Chemistry

Structural and Energetic Aspects of Entacapone-Theophylline-Water Cocrystal Pharmaceutical cocrystals are currently gaining interest among the scientific community, due to their great potential for providing novel crystalline forms with superior properties such as solubility, dissolution rate, bioavailability, and stability. Robust computational tools are valuable tools in the rationalization of cocrystal formation, by providing insight into the intermolecular interactions of multicomponent molecular solids. In this study, various computational techniques based on charge density analysis were implemented to assess structural and energetical perspectives of the interactions responsible for the formation and stability of entacapone-theophylline-water (ETP-THP-water, 1:1:1). Significant non-covalent interactions (NCIs) were identified and evaluated by Hirshfeld surface analysis and density functional theory (DFT) computations, and three-dimensional networks (energy vector diagrams, lattice energy frameworks) were constructed, outlining the crucial stabilizing role of water and the dominance of π-π stacking interactions in the cocrystal. Furthermore, thermal dehydration studies confirmed the strong binding of water molecules in the crystal lattice, as expressed by the high activation energy. Cocrystal ; Chemistry ; Chemical physics ; Intermolecular force ; Dissolution ; Density functional theory ; Lattice energy ; Solubility ; Computational chemistry ; Molecule ; Hydrogen bond ; Crystal structure ; Crystallography ; Physical chemistry ; Organic chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Energetic Materials and Combustion
Authors: Kang‐Hyun Lee | Gun Jin Yun
Source: arXiv (Cornell University) (preprint, 2022-11-20)
Field: Materials Science — Materials Chemistry

Microstructure reconstruction using diffusion-based generative models Microstructure reconstruction has been an essential part of computational material engineering to reveal the relationship between microstructures and material properties. However, finding a general solution for microstructure characterization and reconstruction (MCR) tasks is still challenging, although there have been many attempts such as the descriptor-based MCR methods. To address this generality problem, the denoising diffusion models are first employed for the microstructure reconstruction task in this study. The applicability of the diffusion-based models is validated with several types of microstructures (e.g., polycrystalline alloy, carbonate, ceramics, copolymer, fiber composite, etc.) that have different morphological characteristics. The quality of the generated images is assessed with the quantitative evaluation metrics (FID score, precision, and recall) and the conventional statistical microstructure descriptors. Furthermore, the formulation of implicit probabilistic models (which yields non-Markovian diffusion processes) is adopted to accelerate the sampling process, thereby controlling the computational cost considering the practicability and reliability. The results show that the denoising diffusion models are well applicable to the reconstruction of various types of microstructures with different spatial distributions and morphological features. The diffusion-based approach provides a stable training process with simple implementation for generating visually similar and statistically equivalent microstructures. In these regards, the diffusion model has great potential to be used as a universal microstructure reconstruction method for handling complex microstructures for materials science. Microstructure ; Computer science ; Diffusion ; Parametric statistics ; Algorithm ; Artificial intelligence ; Materials science ; Mathematics ; Statistics ; Composite material Machine Learning in Materials Science ; Composite Material Mechanics ; Generative Adversarial Networks and Image Synthesis
Authors: Qiulan Guo
Source: International Journal of Analytical Chemistry (article, 2022-06-24)
Field: Materials Science — Materials Chemistry

Preparation and Sound Insulation Performance of Polystyrene Building Flame Retardant and Thermal Insulation Building Materials In this study, magnesium hydroxide (MH) was modified by sodium dodecylbenzene sulfonate (SDBS) to prepare modified magnesium hydroxide (MMH) flame retardant, which was mixed with polystyrene (PS) to obtain flame retardant PS composite plate. The micromorphology, mechanical properties, thermal stability, and flame retardancy of flame retardant PS composite plate were analyzed. The experimental results show that MMH is well dispersed in PS matrix, PS-MMH-3 has the best tensile strength and elongation at break, the limit oxygen index (LOI) is 44.3% higher than that of pure PS, and the combustion rate is slow, indicating that PS-MMH-3 has good flame retardant properties. Fire retardant ; Dodecylbenzene ; Materials science ; Composite material ; Ultimate tensile strength ; Thermal stability ; Polystyrene ; Composite number ; Magnesium ; Combustion ; Sulfonate ; Polymer ; Chemical engineering ; Sodium ; Chemistry ; Organic chemistry Magnesium Oxide Properties and Applications ; Flame retardant materials and properties ; Material Properties and Applications
Authors: Saleem Abdulfattah Ahmed Al Dajani | Benjamin Dacus | Cody A. Dennett | M.G. Burke | Lawrence Waldron | Thak Sang Byun | James J. Wall | Kuba Bar-Din Anglin | Omar Abdulfattah Ahmed Al Dajani | Konrad J. Krakowiak | Franz‐Josef Ulm | Alan Schwartzmann | Cemal Cem Taşan | Peter Hosemann | Michael P. Short
Source: Acta Materialia (article, 2022-11-27)
Field: Materials Science — Metals and Alloys

Detecting Thermally-Induced Spinodal Decomposition with Picosecond Ultrasonics in Cast Austenitic Stainless Steels Materials science ; Spinodal decomposition ; Austenite ; Embrittlement ; Charpy impact test ; Toughness ; Ferrite (magnet) ; Stiffening ; Austenitic stainless steel ; Composite material ; Microstructure ; Phase (matter) Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Nuclear Physics and Applications