jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gregory Houchins | Venkatasubramanian Viswanathan
Source: The Journal of Chemical Physics (article, 2020-08-07)
Field: Materials Science — Materials Chemistry

An accurate machine-learning calculator for optimization of Li-ion battery cathodes There is significant interest in improving the performance of batteries to increase electrification of transportation and aviation. Recently, performance improvements have been in large part due to changes in the composition of the cathode material family, LiNi<sub>x</sub>Mn<sub>y</sub>Co<sub>(1-x-y)</sub>O<sub>2</sub> (e.g., 111-622-811). Despite the importance of these materials and tremendous progress with density functional theory (DFT) calculations in understanding basic design principles, it is computationally prohibitively expensive to make this problem tractable. Specifically, predicting the open circuit voltage for any cathode material in this family requires evaluation of stability in a quaternary phase space. In this work, we develop machine-learning potentials using fingerprinting based on atom-centered symmetry functions, used with a neural network model, trained on DFT calculations with a prediction accuracy of 3.7 meV/atom and 0.13 eV/Å for energy and force, respectively. We perform hyperparameter optimization of the fingerprinting parameters using Bayesian optimization through the Dragonfly package. Using this ML calculator, we first test its performance in predicting thermodynamic properties within the Debye-Grüneisen model and find good agreement for most thermodynamic properties, including the Gibbs free energy and entropy. Then, we use this to calculate the Li-vacancy ordering as a function of Li composition to simulate the process of discharging/charging of the cathode using grand canonical Monte Carlo simulations. The predicted voltage profiles are in good agreement with the experimental ones and provide an approach to rapidly perform design optimization in this phase space. This study serves as a proof-point of machine-learned DFT surrogates to enable battery materials optimization. Bayesian optimization ; Computer science ; Statistical physics ; Monte Carlo method ; Density functional theory ; Algorithm ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Nan Xu | Yao Shi | Yi He | Qing Shao
Source: The Journal of Physical Chemistry C (article, 2020-06-30)
Field: Materials Science — Materials Chemistry

A Deep-Learning Potential for Crystalline and Amorphous Li–Si Alloys This work investigates the ability of the deep-learning potential (DP) to describe structural, dynamic and energetic properties of crystalline and amorphous Li–Si alloys. Li–Si systems play an important role in the development of high-energy lithium ion batteries. One challenge in simulating Li–Si systems is to balance the proper description of complex Li–Si interactions and the system size. Molecular simulations implemented with DP provide a promising alternative to achieve this balance and enable us to investigate the fine details of Li–Si systems that the classical force fields cannot offer. We develop a DP for Li–Si systems with Li/Si ratio ranging from 0 to 4.2 based on a vast data set generated using the quantum mechanical calculations in an active learning procedure. Then we investigate the structural and dynamic properties of several crystalline and amorphous Li–Si systems using this developed DP. The DP can predict bulk densities, the radial distribution functions, and diffusivity of Li in amorphous Li–Si systems with an accuracy close to quantum mechanical calculations with the benefit of 20 times faster speed than the ab initio molecular dynamics simulations. Several issues related to the development of DP are also discussed. Amorphous solid ; Molecular dynamics ; Materials science ; Lithium (medication) ; Work (physics) ; Ab initio ; Thermal diffusivity ; Amorphous metal ; Quantum ; Chemical physics ; Nanotechnology ; Statistical physics ; Thermodynamics ; Computational chemistry ; Physics ; Chemistry ; Crystallography Machine Learning in Materials Science ; Fuel Cells and Related Materials ; VLSI and Analog Circuit Testing
Authors: Michael Fortunato | Connor W. Coley | Brian C. Barnes | Klavs F. Jensen
Source: Journal of Chemical Information and Modeling (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high-accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access data sets of organic reactions with explicitly calculated template applicability and pretraining a template-relevance neural network on this augmented applicability data set, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small data set of well-curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating that these strategies can be very useful for small data sets. Template ; Computer science ; Retrosynthetic analysis ; Relevance (law) ; Set (abstract data type) ; Artificial neural network ; Machine learning ; Artificial intelligence ; Task (project management) ; Data set ; Software ; Training set ; Data mining ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Krzysztof B. Beć | Justyna Grabska | Günther K. Bonn | Michael Popp | Christian W. Huck
Source: Frontiers in Plant Science (review, 2020-08-07)
Field: Chemistry — Analytical Chemistry

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature. Computer science ; Context (archaeology) ; Data science ; Biochemical engineering ; Scope (computer science) ; Chemical imaging ; Spectral imaging ; Plant metabolism ; Nanotechnology ; Biological system ; Artificial intelligence ; Remote sensing ; Chemistry ; Hyperspectral imaging ; Biology ; Materials science ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Jianmin Wang | Lei Wang | Jun Liu
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Overview of Phase-Change Materials Based Photonic Devices Non-volatile storage memory is widely considered to be one of the most promising candidates to replace dynamic random access memory and even static random access memory. It has recently received particular attention because of its great potential for brain-like neuromorphic applications. Phase-change materials, also known as Chalcogenide alloys, exhibit several especially advantageous traits for non-volatile applications. These include scalability, fast switching speeds, low switching energy and outstanding thermal stability. As a result, most research to date has sought to identify electrical applications for phase-change materials in relation to phase-change random access memory, phase-change memristors and phase-change neuro networks, while overlooking their potential for non-volatile photonic applications. To address this issue, we provide a comprehensive review that examines the remarkable physical properties of phase-change materials for photonic applications, together with emerging phase-change photonic devices. The review begins by presenting the atomic structure and physical properties of phase-change materials, followed by an elaboration of the issues that phase-change materials are currently facing and the strategies being developed to overcome them. The current state-of-the-art and technical challenges confronting phase-change materials in relation to non-volatile photonic applications, such as phase-change photonic memory, phase-change photonic neuro-networks, phase-change metasurfaces and phase-change color displays are then considered. The review concludes by discussing the outlook for successfully implementing phase-change materials in emerging photonic domains. Photonics ; Phase change ; Phase-change memory ; Computer science ; Neuromorphic engineering ; Phase (matter) ; Scalability ; Chalcogenide ; Materials science ; Nanotechnology ; Optoelectronics ; Engineering physics ; Artificial neural network ; Engineering ; Artificial intelligence ; Physics Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Damoon Ranjbar | Milad Raeiszadeh | Lev Lewis | Mark J. MacLachlan | Savvas G. Hatzikiriakos
Source: Cellulose (article, 2020-02-03)
Field: Materials Science — Biomaterials

Adsorptive removal of Congo red by surfactant modified cellulose nanocrystals: a kinetic, equilibrium, and mechanistic investigation Adsorption ; Zeta potential ; Chemical engineering ; Pulmonary surfactant ; Congo red ; Diffusion ; Fourier transform infrared spectroscopy ; Nanocrystal ; Materials science ; Cellulose ; Aqueous solution ; Chemistry ; Physical chemistry ; Nanoparticle ; Nanotechnology ; Thermodynamics Advanced Cellulose Research Studies ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Chandreyee Manas Das | Lixing Kang | Qingling Ouyang | Ken‐Tye Yong
Source: InfoMat (article, 2020-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced low‐dimensional carbon materials for flexible devices Abstract We live in a digitized era, where we are completely surrounded by a plethora of automated electronic systems, be it a smart home energy controller or a self‐operated diagnostic kiosk in a clinic. With the recent advent of one‐dimensional (1D) and two‐dimensional (2D) nanomaterials like carbon nanotube (CNT) and graphene, the world of electronics has revolutionized with state‐of‐the‐art product paradigms. These nanomaterials possess desirable features of large surface area, excellent electrical conductivity, and high mechanical strength. Electronic devices made out of these materials have the added advantages of being flexible, light‐weight, and durable. Thus, present‐day devices that utilize these substances as channel or electrode materials have been able to undergo a positive transformation as compared with conventional structures. Flexibility and bendability are some of the coveted aesthetics of modern‐day electronics and the use of these 1D and 2D nanomaterials imparts such features to the devices, without having to compromise on key output characteristics like sensitivity and efficiency. In this short review, we discuss about various new configurations that are based on graphene, CNT, and other materials like transition metal dichalcogenides, and how these materials have been able to metamorphose the attributes of conventional devices. image Graphene ; Nanotechnology ; Electronics ; Flexibility (engineering) ; Nanomaterials ; Materials science ; Flexible electronics ; Carbon nanotube ; Printed electronics ; Computer science ; Electrical engineering ; Engineering Supercapacitor Materials and Fabrication ; Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Paweł A. Krawczyk | M. Jurczyszyn | Jakub Pawlak | Wojciech Salamon | Paweł Baran | Angelika Kmita | Ł. Gondek | Marcin Sikora | Cz. Kapusta | Tomasz Strączek | Jan Wyrwa | Antoni Żywczak
Source: ACS Applied Electronic Materials (article, 2020-09-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Entropy Perovskites as Multifunctional Metal Oxide Semiconductors: Synthesis and Characterization of (Gd<sub>0.2</sub>Nd<sub>0.2</sub>La<sub>0.2</sub>Sm<sub>0.2</sub>Y<sub>0.2</sub>)CoO<sub>3</sub> Single-phase multicomponent perovskite-type cobalt oxide containing five cations in equiatomic amounts on the A-site, namely, (Gd<sub>0.2</sub>Nd<sub>0.2</sub>La<sub>0.2</sub>Sm<sub>0.2</sub>Y<sub>0.2</sub>)CoO<sub>3</sub>, has been synthesized via the modified coprecipitation hydrothermal method. Using an original approach for heat treatment, which comprises quenching utilizing liquid nitrogen as a cooling medium, a single-phase ceramic with high configuration entropy, crystallizing in an orthorhombic distorted structure was obtained. It reveals the anomalous temperature dependence of the lattice expansion with two weak transitions at approx. 80 and 240 K that are assigned to gradual crossover from the low- via intermediate- to high-spin state of Co<sup>3+</sup>. The compound exhibits weak ferromagnetism at <i>T</i> ≤ 10 K and signatures of antiferromagnetic correlations in the paramagnetic phase. Ab initio calculations predict a band gap Δ = 1.18 eV in the ground-state electronic structure with the dominant contribution of O_p and Co_d orbitals in the valence and conduction bands, respectively. Electronic transport measurements confirm the negative temperature coefficient of resistivity characteristic to a semiconducting material and reveal a sudden drop in activation energy at <i>T</i> ∼ 240 K from <i>E</i> <sub>a</sub> ∼ 1 eV in the low-temperature phase to <i>E</i> <sub>a</sub> ∼ 0.3 eV at room temperature. The possibility of fine tuning of the semiconducting band gap via a subtle change in A-site stoichiometry is discussed. Materials science ; Antiferromagnetism ; Band gap ; Condensed matter physics ; Orthorhombic crystal system ; Paramagnetism ; Oxide ; Crystal structure ; Chemistry ; Crystallography Magnetic and transport properties of perovskites and related materials ; Thermal Expansion and Ionic Conductivity ; Advanced Thermoelectric Materials and Devices
Authors: Yingqian Zhang | Chuanlong Zang | Guo‐Ce An | Mengdi Shang | Zenghui Cui | Gong Chen | Zhen Xi | Chuanzheng Zhou
Source: Nature Communications (article, 2020-02-21)
Field: Chemistry — Organic Chemistry

Cysteine-specific protein multi-functionalization and disulfide bridging using 3-bromo-5-methylene pyrrolones Many reagents have been developed for cysteine-specific protein modification. However, few of them allow for multi-functionalization of a single Cys residue and disulfide bridging bioconjugation. Herein, we report 3-bromo-5-methylene pyrrolones (3Br-5MPs) as a simple, robust, and versatile class of reagents for cysteine-specific protein modification. These compounds can be facilely synthesized via a one-pot mild reaction and they show comparable tagging efficiency but higher cysteine specificity than the maleimide counterparts. The addition of cysteine to 3Br-5MPs generates conjugates that are amenable to secondary addition by another thiol or cysteine, making 3Br-5MPs valuable for multi-functionalization of a single cysteine and disulfide bridging bioconjugation. The labeling reaction and subsequent treatments are mild enough to produce stable and active protein conjugates for biological applications. Bioconjugation ; Cysteine ; Chemistry ; Reagent ; Surface modification ; Maleimide ; Thiol ; Combinatorial chemistry ; Conjugate ; Disulfide bond ; Methylene ; Residue (chemistry) ; Bridging (networking) ; Biochemistry ; Organic chemistry ; Enzyme Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; Chemical Synthesis and Analysis
Authors: Franz Steppeler | Dominika Iwan | Elżbieta Wojaczyńska | Jacek Wojaczyński
Source: Molecules (review, 2020-01-18)
Field: Chemistry — Organic Chemistry

Chiral Thioureas—Preparation and Significance in Asymmetric Synthesis and Medicinal Chemistry For almost 20 years, thioureas have been experiencing a renaissance of interest with the emerged development of asymmetric organocatalysts. Due to their relatively high acidity and strong hydrogen bond donor capability, they differ significantly from ureas and offer, appropriately modified, great potential as organocatalysts, chelators, drug candidates, etc. The review focuses on the family of chiral thioureas, presenting an overview of the current state of knowledge on their synthesis and selected applications in stereoselective synthesis and drug development. The Renaissance ; Stereoselectivity ; Organocatalysis ; Enantioselective synthesis ; Combinatorial chemistry ; Chemistry ; Organic synthesis ; Catalysis ; Organic chemistry ; Stereochemistry ; Art Asymmetric Synthesis and Catalysis ; Chemical Reaction Mechanisms ; Synthesis and Catalytic Reactions
Authors: Yu Wang | Da‐Ming Du
Source: Organic Chemistry Frontiers (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Recent advances in organocatalytic asymmetric oxa-Michael addition triggered cascade reactions In this review, we summarized recent advances in organocatalytic asymmetric oxa-Michael addition triggered cascade reactions. Chemistry ; Cascade ; Organocatalysis ; Michael reaction ; Combinatorial chemistry ; Enantioselective synthesis ; Organic chemistry ; Catalysis ; Chromatography Synthesis of Indole Derivatives ; Asymmetric Synthesis and Catalysis ; Axial and Atropisomeric Chirality Synthesis
Authors: Xiaokang Wang | Ke Chen | Luize Scalco de Vasconcelos | Jiazhi He | Yung C. Shin | Jianguo Mei | Kejie Zhao
Source: Nature Communications (article, 2020-01-10)
Field: Materials Science — Polymers and Plastics

Mechanical breathing in organic electrochromics The repetitive size change of the electrode over cycles, termed as mechanical breathing, is a crucial issue limiting the quality and lifetime of organic electrochromic devices. The mechanical deformation originates from the electron transport and ion intercalation in the redox active material. The dynamics of the state of charge induces drastic changes of the microstructure and properties of the host, and ultimately leads to structural disintegration at the interfaces. We quantify the breathing strain and the evolution of the mechanical properties of poly(3,4-propylenedioxythiophene) thin films in-situ using customized environmental nanoindentation. Upon oxidation, the film expands nearly 30% in volume, and the elastic modulus and hardness decrease by a factor of two. We perform theoretical modeling to understand thin film delamination from an indium tin oxide (ITO) current collector under cyclic load. We show that toughening the interface with roughened or silica-nanoparticle coated ITO surface significantly improves the cyclic performance. Materials science ; Thin film ; Composite material ; Indium tin oxide ; Deformation (meteorology) ; Microstructure ; Nanoindentation ; Electrode ; Delamination (geology) ; Nanotechnology ; Chemistry Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Advanced Sensor and Energy Harvesting Materials
Authors: Anand Chandrasekaran | Chiho Kim | Shruti Venkatram | Rampi Ramprasad
Source: Macromolecules (article, 2020-06-11)
Field: Materials Science — Materials Chemistry

A Deep Learning Solvent-Selection Paradigm Powered by a Massive Solvent/Nonsolvent Database for Polymers Polymer solubility is critical for a variety of industrial and research applications such as plastics recycling, drug delivery, membrane science, and microlithography. For novel polymers, it is often an arduous process to find the appropriate solvents for polymer dissolution. Heuristic approaches, such as solubility parameters, provide only limited guidance with respect to solvent prediction and design. The present work highlights a novel data-driven paradigm for solvent selection in polymers. For this purpose, we utilize a deep neural network trained on a massive data set of over 4500 polymers and their corresponding solvents/nonsolvents. This deep-learning framework maps high-dimensional fingerprints/features to compact chemically relevant latent space representations of solvents and polymers. When these low-dimensional representations are visualized, we observe the spontaneous clustering of nonpolar, polar-aprotic, and polar-protic behavior. This large-scale data-driven approach possesses an overall classification accuracy of above 93% (on a hold-out set) and significantly outperforms existing methods to determine polymer/solvent compatibility such as the Hildebrand criteria. Polymer ; Solvent ; Solubility ; Dissolution ; Computer science ; Cluster analysis ; Materials science ; Chemistry ; Biochemical engineering ; Organic chemistry ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration