jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marc Paulus | Anja Paulus | Rüdiger‐A. Eichel | Josef Granwehr
Source: Zeitschrift für Physikalische Chemie (article, 2021-11-04)
Field: Materials Science — Materials Chemistry

Independent component analysis combined with Laplace inversion of spectrally resolved spin-alignment echo/ <i>T</i> <sub>1</sub> 3D <sup>7</sup> Li NMR of superionic Li <sub>10</sub> GeP <sub>2</sub> S <sub>12</sub> Abstract The use of independent component analysis (ICA) for the analysis of two-dimensional (2D) spin-alignment echo– T 1 7 Li NMR correlation data with transient echo detection as a third dimension is demonstrated for the superionic conductor Li 10 GeP 2 S 12 (LGPS). ICA was combined with Laplace inversion, or discrete inverse Laplace transform (ILT), to obtain spectrally resolved 2D correlation maps. Robust results were obtained with the spectra as well as the vectorized correlation maps as independent components. It was also shown that the order of ICA and ILT steps can be swapped. While performing the ILT step before ICA provided better contrast, a substantial data compression can be achieved if ICA is executed first. Thereby the overall computation time could be reduced by one to two orders of magnitude, since the number of computationally expensive ILT steps is limited to the number of retained independent components. For LGPS, it was demonstrated that physically meaningful independent components and mixing matrices are obtained, which could be correlated with previously investigated material properties yet provided a clearer, better separation of features in the data. LGPS from two different batches was investigated, which showed substantial differences in their spectral and relaxation behavior. While in both cases this could be attributed to ionic mobility, the presented analysis may also clear the way for a more in-depth theoretical analysis based on numerical simulations. The presented method appears to be particularly suitable for samples with at least partially resolved static quadrupolar spectra, such as alkali metal ions in superionic conductors. The good stability of the ICA analysis makes this a prospect algorithm for preprocessing of data for a subsequent automatized analysis using machine learning concepts. Laplace transform ; Inverse Laplace transform ; Nuclear magnetic resonance ; Inverse ; Computational physics ; Independent component analysis ; Spectral line ; Spin echo ; Relaxation (psychology) ; Chemistry ; Physics ; Biological system ; Analytical Chemistry (journal) ; Materials science ; Mathematics ; Mathematical analysis ; Computer science ; Artificial intelligence ; Quantum mechanics ; Geometry Solid-state spectroscopy and crystallography ; Blind Source Separation Techniques ; Advanced Battery Technologies Research
Authors: Carles Martí | Sarah Blanck | Ruben Staub | Sophie Loehlé | Carine Michel | Stephan N. Steinmann
Source: ChemRxiv (preprint, 2021-02-24)
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 ; Materials science ; Nanotechnology ; Dissociation (chemistry) ; Molecule ; Computer science ; Chemistry ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Kojiro Uetani | Naliharifetra Jessica Ranaivoarimanana | Mayumi Hatakeyama | Takuya Kitaoka
Source: BioResources (article, 2021-03-30)
Field: Materials Science — Biomaterials

Inherently Distinctive Potentialities and Uses of Nanocellulose Based on its Nanoarchitecture Native cellulose is mainly found in phytomass, such as trees and other plants. It has a regular hierarchical nanoarchitecture, in which the extended macromolecular chains are aligned and closely packed in parallel to form the crystalline nanofibrils of cell walls. In the context of material utilization, nanocellulose is a collective term for nano-ordered assemblies of cellulose chains. In recent times, it has been produced in large quantities from woody bioresources. In addition, nanocellulose has some fascinating physicochemical properties, such as high strength, light weight, transparency, birefringence, and low thermal expansion. These properties have enabled broad functional design of nanocellulose-based materials; but most of them are facing serious competition from various products that already exist. However, nanocellulose is not just a green alternative to existing materials. Rather, it is expected to make a profound difference in terms of pioneering novel functions. The present review focuses on the unexpected features of nanocellulose materials, triggered by details of the inherent nanoarchitecture of native cellulose. Nanocellulose ; Materials science ; Cellulose ; Nanofiber ; Context (archaeology) ; Nanotechnology ; Polymer science ; Composite material ; Chemical engineering Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Biofuel production and bioconversion
Authors: Yifan Wang | Tai-Ying Chen | Dionisios G. Vlachos
Source: ChemRxiv (preprint, 2021-06-04)
Field: Materials Science — Materials Chemistry

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Bayesian optimization ; Interfacing ; Computer science ; Python (programming language) ; Engineering optimization ; Bayesian probability ; Multidisciplinary design optimization ; Visualization ; Optimization problem ; Artificial intelligence ; Machine learning ; Programming language ; Algorithm ; Multidisciplinary approach Machine Learning in Materials Science ; Machine Learning and Algorithms ; Process Optimization and Integration
Authors: Adarsh V. Kalikadien | Evgeny A. Pidko | Vivek Sinha
Source: ChemRxiv (preprint, 2021-06-08)
Field: Materials Science — Materials Chemistry

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold Local chemical space exploration of an experimentally synthesized material can be done by making slight structuralvariations of the synthesized material. This generation of many molecular structures with reasonable quality,that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes inmaterial design. Large databases of geometry and chemical properties of transition metal complexes are notreadily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,to place functional groups on an input structure. For example, the input structure can be a catalyst for which onewants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placedsubstituents are optimized using a computationally cheap force-field optimization method. After placement ofnew substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possiblein the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated byChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applicationsof ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalizationof a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such asthe Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discoveryof catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTBmethod was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven materialdiscovery. Chemical space ; Catalysis ; Surface modification ; Porphyrin ; Combinatorial chemistry ; Workflow ; Transition metal ; Molecule ; Chemistry ; Materials science ; Computer science ; Nanotechnology ; Organic chemistry ; Physical chemistry ; Database ; Drug discovery Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Catalysis and Hydrodesulfurization Studies
Authors: Daniel Schwalbe‐Koda | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2021-01-21)
Field: Chemistry — Inorganic Chemistry

Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) for zeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective molecules that maximize binding towards a given zeolite framework. Over the last decades, a variety of methods and levels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus on the best calculation strategy for high-throughput virtual screening undertakings. In this work, we compare binding affinities from density functional theory (DFT) and force field calculations for 272 zeolite-OSDA pairs obtained from static and time-averaged simulations. Enabled by automation software, we show that binding energies from the frozen pose method correlate best with DFT time-averaged energies. They are also less sensitive to the choice of initial lattice parameters and optimization algorithms, as well as less computationally expensive. Furthermore, we demonstrate that a broader exploration of the conformation space from molecular dynamics simulations does not provide significant improvements in binding energy trends over single-point calculations. The code and benchmark data are open-sourced and provide robust and computationally-efficient guidelines to calculating binding energies in zeolite-OSDA pairs. Binding energy ; Density functional theory ; Benchmark (surveying) ; Zeolite ; Energy minimization ; Benchmarking ; Binding affinities ; Molecular dynamics ; Virtual screening ; Computational chemistry ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Atomic physics ; Catalysis Zeolite Catalysis and Synthesis ; Ammonia Synthesis and Nitrogen Reduction ; Molecular Junctions and Nanostructures
Authors: Vu Ngoc Tuoc | Nguyễn Thị Thảo | Lê Thị Hồng Liên | Phan Thanh Liem
Source: physica status solidi (b) (article, 2021-04-27)
Field: Materials Science — Materials Chemistry

Novel Chain and Ribbon ZnO Nanoporous Crystalline Phases in Cubic Lattice The chain‐ and ribbon‐like structural hierarchies are used to design two nanoporous polymorphs composed of only four‐member and six‐member rings of ZnO as primary building blocks in cubic lattice frameworks. Specifically, the bottom‐up polymeric assembly of these small simple clusters into high cubic symmetric phases is considered, with energetic, dynamic, and mechanical stabilities proven by density‐functional‐based calculations. Both phases are rivaling with those of known cage‐like nanoporous polymorphs in many porous characteristics while preserving an intrinsic wide bandgap from ZnO tetragonal bonding. Furthermore, the design scheme is feasible to apply with different morphology motifs or extend to more cluster prototypes and materials. Ribbon ; Nanoporous ; Tetragonal crystal system ; Materials science ; Cubic crystal system ; Lattice (music) ; Cluster (spacecraft) ; Nanotechnology ; Porosity ; Cage ; Crystallography ; Crystal structure ; Chemical physics ; Composite material ; Chemistry ; Computer science ; Physics ; Combinatorics ; Mathematics Nanoporous metals and alloys ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Kiran Vaddi | Olga Wodo
Source: ChemRxiv (preprint, 2021-05-11)
Field: Materials Science — Materials Chemistry

Active Knowledge Extraction from Cyclic Voltammetry Cyclic Voltammetry~(CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions.In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). However, often the settings to obtain such curve are not known \textit{a priori}. In this paper, an active search framework is defined to accelerate identification of settings that enable knowledge extraction from CV experiments.Towards this goal, a function space representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either \textit{S-shape} or not \textit{S-shape}. Using an active search with BMS oracle, we report a linear target identification in a 6-dimensional design space (comprising of thermodynamic, mass transfer and solution variables as dimensions). Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts etc. Chemical space ; Cyclic voltammetry ; Computer science ; Space (punctuation) ; Representation (politics) ; Biological system ; A priori and a posteriori ; Identification (biology) ; Oracle ; Virtual screening ; Characterization (materials science) ; Function (biology) ; Chemistry ; Materials science ; Computational chemistry ; Physical chemistry ; Nanotechnology ; Drug discovery ; Molecular dynamics ; Electrode Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Neural Networks and Applications
Authors: Hongbin Zhang
Source: TUbilio (Technical University of Darmstadt) (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput design of magnetic materials Materials design based on density functional theory (DFT) calculations is an emergent field of great potential to accelerate the development and employment of novel materials. Magnetic materials play an essential role in green energy applications as they provide efficient ways of harvesting, converting, and utilizing energy. In this review, after a brief introduction to the major functionalities of magnetic materials, we demonstrated how the fundamental properties can be tackled via high-throughput DFT calculations, with a particular focus on the current challenges and feasible solutions. Successful case studies are summarized on several classes of magnetic materials, followed by bird-view perspectives. Throughput ; Nanotechnology ; Density functional theory ; Focus (optics) ; Computer science ; Magnetic field ; Engineering physics ; Biochemical engineering ; Materials science ; Engineering ; Physics ; Chemistry ; Telecommunications ; Wireless ; Computational chemistry Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Multiferroics and related materials
Authors: Ruan Sousa Bastos | Joabe Lima Araújo | Vitória Santos Azevedo | Maria de Lourdes de Aguiar Silva Ferreira | Lúcio R. de Lima | Jefferson Almeida Rocha
Source: Research Society and Development (article, 2021-05-07)
Field: Materials Science — Polymers and Plastics

Cadmio complexes with biological activity: Scientific and technological prospection Cadmium is a toxic metal from 12th group and 5th period of the periodic table. It’s common its use in batteries manufacturing and metal alloys production. It is absorbed by the organism of animals and plants causing risks to human health by bioaccumulation. Even with proven levels of toxicity, complexes involving this atom have biological activity according to several studies. As a study methodology, data were prospected in different databases for patents and the scientific papers published in the period up 1962 to 2021. Only 3 patents were found regarding the biological activity of complexes containing cadmium, although 3.257 results have been found for Cd (II) articles. The database with the highest number of published articles was Scifinder with 63% (2.037) of the articles. Few articles approached anthelmintic activity, showing that future research related to chemical substances containing this metal is a possibility in the new anthelmintic drugs investigation. Bioaccumulation ; Anthelmintic ; Cadmium ; Toxicology ; Chemistry ; Environmental chemistry ; Biology ; Ecology ; Organic chemistry Polymer Synthesis and Characterization ; Extraction and Separation Processes ; Thallium and Germanium Studies
Authors: Benali Rerbal | Tarik Ouahrani
Source: The European Physical Journal B (article, 2021-09-01)
Field: Chemistry — Inorganic Chemistry

Enhancement of optoelectronic properties of layered MgIn$$_{2}$$Se$$_{4}$$ compound under uniaxial strain, an ab initio study Materials science ; Density functional theory ; Hydrostatic pressure ; Band gap ; Ab initio ; Hydrostatic equilibrium ; Semiconductor ; Strain (injury) ; Charge (physics) ; Deformation (meteorology) ; Optoelectronics ; Electronic band structure ; Condensed matter physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Composite material ; Physics ; Thermodynamics Inorganic Chemistry and Materials ; Crystal Structures and Properties ; Iron-based superconductors research
Authors: Peikun Zheng | R.I. Zubatyuk | Wei Wu | Olexandr Isayev | Pavlo O. Dral
Source:  (preprint, 2021-07-23)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Enhanced Quantum Chemical Method with Broad Applicability High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence–quantum mechanical method 1 (AIQM1). It approaches the accuracy of the ‘gold-standard’ coupled cluster QM method with low computational speed of the approximate low-level semiempirical QM methods. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C60) close to experiment. Noteworthy, our method’s accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties. Computer science ; Quantum ; Excited state ; Artificial neural network ; Quantum chemistry ; Ground state ; Quantum computer ; Quantum chemical ; Artificial intelligence ; Statistical physics ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Andrea Byekwaso | Alain C. Vaucher | Philippe Schwaller | Alessandra Toniato | Teodoro Laino
Source: ChemRxiv (preprint, 2021-12-07)
Field: Materials Science — Materials Chemistry

A Sequence-to-Sequence Transformer Model for Disconnection Aware Retrosynthesis Retrosynthesis is an approach commonly undertaken when considering the manufacture of novel molecules. During this process, a target molecule is broken down and analyzed by considering the bonds to be changed as well as the functional group interconversion. In modern computer-assisted synthesis planning tools, the predictions of these changes are typically carried out automatically. However there may be some benefit to the decision being guided by those executing the process: typically, chemists have a clear idea where the retrosynthetic change should happen, but not how such a transformation is to be realized. Using a data-driven model, the retrosynthesis task can be further explored by giving chemists the option to explore specific disconnections. In this work, we design an approach to provide this option by adapting a transformer-based model for single-step retrosynthesis. The model takes as input a product SMILES string, in which the atoms where the transformation should occur are tagged accordingly. This model predicts precursors corresponding to a disconnection occurring in the correct location in 88.9% of the test set reactions. The assessment with a forward prediction model shows that 76% of the predictions are chemically correct, with 14.1% perfectly matching the ground truth. Retrosynthetic analysis ; Transformer ; Computer science ; Transformation (genetics) ; Process (computing) ; Sequence (biology) ; Disconnection ; Artificial intelligence ; Engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation