jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhengkai Tu | Connor W. Coley
Source: Journal of Chemical Information and Modeling (review, 2022-07-26)
Field: Materials Science — Materials Chemistry

Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not efficient for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boost empirical performance. Here, we describe a novel Graph2SMILES model that combines the power of Transformer models for text generation with the permutation invariance of molecular graph encoders that mitigates the need for input data augmentation. In our encoder, a directed message passing neural network (D-MPNN) captures local chemical environments, and the global attention encoder allows for long-range and intermolecular interactions, enhanced by graph-aware positional embedding. As an end-to-end architecture, Graph2SMILES can be used as a drop-in replacement for the Transformer in any task involving molecule(s)-to-molecule(s) transformations, which we empirically demonstrate leads to improved performance on existing benchmarks for both retrosynthesis and reaction outcome prediction. Computer science ; Retrosynthetic analysis ; Artificial intelligence ; Transformer ; Theoretical computer science ; Encoder ; Machine learning ; Machine translation ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Jonathan Vandermause | Yu Xie | Jin Soo Lim | Cameron J. Owen | Boris Kozinsky
Source: Nature Communications (article, 2022-09-02)
Field: Materials Science — Materials Chemistry

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous "on-the-fly" training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H<sub>2</sub> turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment. ReaxFF ; Force field (fiction) ; Molecular dynamics ; Computer science ; Bayesian probability ; Gaussian process ; Ab initio ; Field (mathematics) ; Bayesian optimization ; Gaussian ; Statistical physics ; Computational chemistry ; Artificial intelligence ; Chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Fuyao Liu | Qianqian Wang | Gongxun Zhai | Hengxue Xiang | Jialiang Zhou | Chao Jia | Liping Zhu | Qilin Wu | Meifang Zhu
Source: Nature Communications (article, 2022-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Continuously processing waste lignin into high-value carbon nanotube fibers High value utilization of renewable biomass materials is of great significance to the sustainable development of human beings. For example, because biomass contains large amounts of carbon, they are ideal candidates for the preparation of carbon nanotube fibers. However, continuous preparation of such fibers using biomass as carbon source remains a huge challenge due to the complex chemical structure of the precursors. Here, we realize continuous preparation of high-performance carbon nanotube fibers from lignin by solvent dispersion, high-temperature pyrolysis, catalytic synthesis, and assembly. The fibers exhibit a tensile strength of 1.33 GPa and an electrical conductivity of 1.19 × 10<sup>5 </sup>S m<sup>-1</sup>, superior to that of most biomass-derived carbon materials to date. More importantly, we achieve continuous production rate of 120 m h<sup>-1</sup>. Our preparation method is extendable to other biomass materials and will greatly promote the high value application of biomass in a wide range of fields. Carbon nanotube ; Biomass (ecology) ; Lignin ; Materials science ; Pyrolysis ; Carbon fibers ; Dispersion (optics) ; Renewable energy ; Chemical engineering ; Nanotechnology ; Pulp and paper industry ; Composite material ; Chemistry ; Organic chemistry ; Composite number ; Agronomy Supercapacitor Materials and Fabrication ; Lignin and Wood Chemistry ; Fiber-reinforced polymer composites
Authors: Olga Garcı́a Mancheño | Mario Waser
Source: European Journal of Organic Chemistry (review, 2022-10-17)
Field: Chemistry — Organic Chemistry

Recent Developments and Trends in Asymmetric Organocatalysis Asymmetric organocatalysis has experienced a long and spectacular way since the early reports over a century ago by von Liebig, Knoevenagel and Bredig, showing that small (chiral) organic molecules can catalyze (asymmetric) reactions. This was followed by impressive first highly enantioselective reports in the second half of the last century, until the hype initiated in 2000 by the milestone publications of MacMillan and List, which finally culminated in the 2021 Nobel Prize in Chemistry. This short Perspective aims at providing a brief introduction to the field by first looking on the historical development and the more classical methods and concepts, followed by discussing selected advanced recent examples that opened new directions and diversity within this still growing field. Organocatalysis ; Enantioselective synthesis ; Milestone ; Chemistry ; Knoevenagel condensation ; Nanotechnology ; Organic chemistry ; History ; Catalysis ; Archaeology Asymmetric Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Axial and Atropisomeric Chirality Synthesis
Authors: Thomas C. Terwilliger | Billy K. Poon | Pavel V. Afonine | Christopher J. Schlicksup | Tristan I. Croll | Claudia Millán | Jane S. Richardson | Randy J. Read | Paul D. Adams
Source: Nature Methods (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

Improved AlphaFold modeling with implicit experimental information Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps. Computer science ; Experimental data ; Basis (linear algebra) ; Simple (philosophy) ; Artificial intelligence ; Machine learning ; Algorithm ; Data mining ; Mathematics ; Statistics Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Jiawen Guo | Huimin Liu | Dezheng Li | Jian Wang | Xavier Djitcheu | Dehua He | Qijian Zhang
Source: RSC Advances (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

A minireview on the synthesis of single atom catalysts Single atom catalysis is a prosperous and rapidly growing research field, owing to the remarkable advantages of single atom catalysts (SACs), such as maximized atom utilization efficiency, tailorable catalytic activities as well as supremely high catalytic selectivity. Synthesis approaches play crucial roles in determining the properties and performance of SACs. Over the past few years, versatile methods have been adopted to synthesize SACs. Herein, we give a thorough and up-to-date review on the progress of approaches for the synthesis of SACs, outline the general principles and list the advantages and disadvantages of each synthesis approach, with the aim to give the readers a clear picture and inspire more studies to exploit novel approaches to synthesize SACs effectively. Catalysis ; Atom (system on chip) ; Combinatorial chemistry ; Chemistry ; Nanotechnology ; Computer science ; Materials science ; Organic chemistry ; Parallel computing Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Hui‐Qing Peng | Wenping Zhu | Wu‐Jie Guo | Qingyun Li | Shixiang Ma | Christophe Bucher | Bin Liu | Xiaofan Ji | Feihe Huang | Jonathan L. Sessler
Source: Progress in Polymer Science (article, 2022-12-14)
Field: Materials Science — Biomaterials

Supramolecular polymers: Recent advances based on the types of underlying interactions Supramolecular chemistry ; Supramolecular polymers ; Non-covalent interactions ; Nanotechnology ; Polymer ; Supramolecular assembly ; Materials science ; Hydrogen bond ; Chemistry ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials
Authors: Xiujuan Liu | Pengcheng Xu | Juanjuan Zhao | Wencong Lu | Minjie Li | Gang Wang
Source: Journal of Alloys and Compounds (article, 2022-06-21)
Field: Materials Science — Materials Chemistry

Material machine learning for alloys: Applications, challenges and perspectives High entropy alloys ; Titanium alloy ; Workflow ; Materials science ; Computer science ; Aluminium ; Alloy ; Metallurgy ; Database Machine Learning in Materials Science ; High Entropy Alloys Studies ; Metal and Thin Film Mechanics
Authors: Maximilian Jaugstetter | Niclas Blanc | Markus Kratz | Kristina Tschulik
Source: Chemical Society Reviews (review, 2022-01-01)
Field: Chemistry — Electrochemistry

Electrochemistry under confinement Although the term 'confinement' regularly appears in electrochemical literature, elevated by continuous progression in the research of nanomaterials and nanostructures, up until today the various aspects of confinement considered in electrochemistry are rather scattered individual contributions outside the established disciplines in this field. Thanks to a number of highly original publications and the growing appreciation of confinement as an overarching link between different exciting new research strategies, 'electrochemistry under confinement' is the process of forming a research discipline of its own. To aid the development a coherent terminology and joint basic concepts, as crucial factors for this transformation, this review provides an overview on the different effects on electrochemical processes known to date that can be caused by confinement. It also suggests where boundaries to other effects, such as nano-effects could be drawn. To conceptualize the vast amount of research activities revolving around the main concepts of confinement, we define six types of confinement and select two of them to discuss the state of the art and anticipated future developments in more detail. The first type concerns nanochannel environments and their applications for electrodeposition and for electrochemical sensing. The second type covers the rather newly emerging field of colloidal single entity confinement in electrochemistry. In these contexts, we will for instance address the influence of confinement on the mass transport and electric field distributions and will link the associated changes in local species concentration or in the local driving force to altered reaction kinetics and product selectivity. Highlighting pioneering works and exciting recent developments, this educational review does not only aim at surveying and categorizing the state-of-the-art, but seeks to specifically point out future perspectives in the field of confinement-controlled electrochemistry. Nanotechnology ; Terminology ; Field (mathematics) ; Computer science ; Materials science Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Anamol Pokharel | Kehinde James Falua | Amin Babaei‐Ghazvini | Bishnu Acharya
Source: Journal of Composites Science (review, 2022-09-05)
Field: Materials Science — Polymers and Plastics

Biobased Polymer Composites: A Review Global environmental concerns, as well as the rapid depletion of non-renewable fossil fuel-based resources, have prompted research into the development of sustainable, environmentally friendly, and biodegradable materials for use in a variety of high-end applications. To mitigate the environmental setbacks caused by nonbiodegradable materials, the development of biocomposites with improved mechanical performance is gradually gaining momentum. Natural fibers such as hemp, flax, and sisal have been well incorporated into biocomposite development. Nonetheless, the impact of functional moieties in their life cycle cannot be underestimated. In this review paper, a detailed discussion of the characteristics and components of biocomposites is presented. The treatment of composite materials (alkali and acetylation), as well as several manufacturing processes (hand layup, 3D printing, extrusion, etc.) and the applications of biocomposites, which are not limited to the aerospace industry, packaging, biomedicine, etc., are presented. Biocomposites with excellent durability, performance, serviceability, and reliability must be produced to expand their applications. Biocomposite ; Durability ; Environmentally friendly ; Materials science ; Aerospace ; Life-cycle assessment ; Renewable resource ; Composite material ; Composite number ; Renewable energy ; Engineering Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; biodegradable polymer synthesis and properties
Authors: Yibo Ni | Chen Chen | Shun Wen | Xinyuan Xue | Liqun Sun | Yuanmu Yang
Source: eLight (article, 2022-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational spectropolarimetry with a tunable liquid crystal metasurface Abstract While conventional photodetectors can only measure light intensity, the vectorial light field contains much richer information, including polarization and spectrum, that are essential for numerous applications ranging from imaging to telecommunication. However, the simultaneous measurement of multi-dimensional light field information typically requires the multiplexing of dispersive or polarization-selective elements, leading to excessive system complexity. Here, we demonstrate a near-infrared spectropolarimeter based on an electrically-tunable liquid crystal metasurface. The tunable metasurface, which acts as an encoder of the vectorial light field, is tailored to support high-quality-factor guided-mode resonances with diverse and anisotropic spectral features, thus allowing the full Stokes parameters and the spectrum of the incident light to be computationally reconstructed with high fidelity. The concept of using a tunable metasurface for multi-dimensional light field encoding may open up new horizons for developing vectorial light field sensors with minimized size, weight, cost, and complexity. Light field ; Polarization (electrochemistry) ; Optics ; Multiplexing ; Ray ; Stokes parameters ; Photodetector ; Physics ; Optoelectronics ; Liquid crystal ; Computer science ; Telecommunications ; Scattering ; Chemistry Metamaterials and Metasurfaces Applications ; Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry
Authors: Arnab Pal | Shuo Zhang | Tanmay Chavan | Kunjesh Agashiwala | Chao‐Hui Yeh | Wei Cao | Kaustav Banerjee
Source: Advanced Materials (review, 2022-04-25)
Field: Materials Science — Materials Chemistry

Quantum‐Engineered Devices Based on 2D Materials for Next‐Generation Information Processing and Storage As an approximation to the quantum state of solids, the band theory, developed nearly seven decades ago, fostered the advance of modern integrated solid-state electronics, one of the most successful technologies in the history of human civilization. Nonetheless, their rapidly growing energy consumption and accompanied environmental issues call for more energy-efficient electronics and optoelectronics, which necessitate the exploration of more advanced quantum mechanical effects, such as band-to-band tunneling, spin-orbit coupling, spin-valley locking, and quantum entanglement. The emerging 2D layered materials, featured by their exotic electrical, magnetic, optical, and structural properties, provide a revolutionary low-dimensional and manufacture-friendly platform (and many more opportunities) to implement these quantum-engineered devices, compared to the traditional electronic materials system. Here, the progress in quantum-engineered devices is reviewed and the opportunities/challenges of exploiting 2D materials are analyzed to highlight their unique quantum properties that enable novel energy-efficient devices, and useful insights to quantum device engineers and 2D-material scientists are provided. Materials science ; Nanotechnology ; Materials processing ; Information storage ; Systems engineering ; Engineering physics ; Process engineering ; Computer science ; Engineering ; Information retrieval 2D Materials and Applications ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Ergys Pahija | Christopher Panaritis | Sergey Gusarov | Jalil Shadbahr | Farid Bensebaa | Gregory S. Patience | Daria C. Boffito
Source: ACS Catalysis (review, 2022-05-26)
Field: Materials Science — Materials Chemistry

Experimental and Computational Synergistic Design of Cu and Fe Catalysts for the Reverse Water–Gas Shift: A Review Strategies to capture and sequester ever-increasing anthropogenic CO2 emissions include adsorbing CO2 onto inorganic substrates and then storing it in reservoirs, changing land use to promote forestry, and converting CO2 to chemicals and fuels. The reverse water–gas shift (RWGS) reaction is a conversion strategy for producing CO from CO2 that provides the highest technology readiness level. Cu and alkali metals promote CO2 adsorption, Fe improves the thermal stability, and reducible supports like CeO2 accelerate the reaction rate. Density functional theory (DFT) is a practical modeling tool for evaluating the catalytic properties of materials at the atomic scale. The active phases of the Cu- and Fe-based catalysts, the effect of bimetallic compositions, the presence of promotors, and the influence of the support material are evaluated using observations from DFT simulations and experimental data. An optimal RWGS catalyst favors (1) CO2 adsorption, (2) the dissociation of CO2 or intermediate carbonate species to CO, and (3) CO desorption. Typically, a single-component catalytic plane is unfavorable for all these criteria, thus necessitating the design of an optimal multicomponent RWGS catalyst. Future DFT research is directed toward multifacet catalytic systems to understand the structural configuration of a highly active RWGS system. Experimental and characterization results complement DFT studies in the design of the optimal RWGS catalyst. Machine learning trained by literature data provides an automated approach for the inverse design of high-performance, stable, and economic catalysts for the RWGS reaction. This review encompasses experimental and computational approaches to understand the activity of RWGS catalysts. Catalysis ; Bimetallic strip ; Water-gas shift reaction ; Density functional theory ; Adsorption ; Chemical engineering ; Dissociation (chemistry) ; Materials science ; Chemistry ; Process engineering ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Oluwasegun Chijioke Adekoya | Gbolahan Joseph Adekoya | Emmanuel Rotimi Sadiku | Yskandar Hamam | Suprakas Sinha Ray
Source: Pharmaceutics (review, 2022-09-19)
Field: Materials Science — Biomaterials

Application of DFT Calculations in Designing Polymer-Based Drug Delivery Systems: An Overview Drug delivery systems transfer medications to target locations throughout the body. These systems are often made up of biodegradable and bioabsorbable polymers acting as delivery components. The introduction of density functional theory (DFT) has tremendously aided the application of computational material science in the design and development of drug delivery materials. The use of DFT and other computational approaches avoids time-consuming empirical processes. Therefore, this review explored how the DFT computation may be utilized to explain some of the features of polymer-based drug delivery systems. First, we went through the key aspects of DFT and provided some context. Then we looked at the essential characteristics of a polymer-based drug delivery system that DFT simulations could predict. We observed that the Gaussian software had been extensively employed by researchers, particularly with the B3LYP functional and 6-31G(d, p) basic sets for polymer-based drug delivery systems. However, to give researchers a choice of basis set for modelling complicated organic systems, such as polymer-drug complexes, we then offered possible resources and presented the future trend. Drug delivery ; Computer science ; Context (archaeology) ; Density functional theory ; Nanotechnology ; Materials science ; Chemistry ; Computational chemistry Nanoparticle-Based Drug Delivery ; Fullerene Chemistry and Applications ; Polymer Nanocomposite Synthesis and Irradiation