jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Bím | Anastassia N. Alexandrova
Source: ACS Catalysis (article, 2021-05-19)
Field: Chemistry — Inorganic Chemistry

Local Electric Fields As a Natural Switch of Heme-Iron Protein Reactivity Heme-iron oxidoreductases operating through the high-valent Fe<sup>IV</sup>O intermediates perform crucial and complicated transformations, such as oxidations of unreactive saturated hydrocarbons. These enzymes share the same Fe coordination, only differing by the axial ligation, e.g., Cys in P450 oxygenases, Tyr in catalases, and His in peroxidases. By examining ~200 heme-iron proteins, we show that the protein hosts exert highly specific intramolecular electric fields on the active sites, and there is a strong correlation between the direction and magnitude of this field and the protein function. In all heme proteins, the field is preferentially aligned with the Fe-O bond ( <b><i>F<sub>z</sub></i> </b> ). The Cys-ligated P450 oxygenases have the highest average <b><i>F<sub>z</sub></i> </b> of 28.5 MV cm<sup>-1</sup>, i.e., most enhancing the oxyl-radical character of the oxo group, and consistent with the ability of these proteins to activate strong C-H bonds. In contrast, in Tyr-ligated proteins, the average <b><i>F<sub>z</sub></i> </b> is only 3.0 MV cm<sup>-1</sup>, apparently suppressing single-electron off-pathway oxidations, and in His-ligated proteins, <b><i>F<sub>z</sub></i> </b> is -8.7 MV cm<sup>-1</sup>. The operational field range is given by the trade-off between the low reactivity of the Fe<sup>IV</sup>O Compound I at the more negative <b><i>F<sub>z</sub></i> </b> , and the low selectivity at the more positive <b><i>F<sub>z</sub></i> </b> . Consequently, a heme-iron site placed in the field characteristic of another heme-iron protein class loses its canonical function, and gains an adverse one. Thus, electric fields produced by the protein scaffolds, together with the nature of the axial ligand, control all heme-iron chemistry. Heme ; Chemistry ; Reactivity (psychology) ; Hemeprotein ; Ligand (biochemistry) ; Intramolecular force ; Active site ; Heme oxygenase ; Peroxidase ; Stereochemistry ; Microsome ; Enzyme ; Biochemistry ; Receptor Metal-Catalyzed Oxygenation Mechanisms ; Hemoglobin structure and function ; Heme Oxygenase-1 and Carbon Monoxide
Authors: Yuqi Song | Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Jianjun Hu
Source: ACS Applied Materials & Interfaces (article, 2021-05-14)
Field: Materials Science — Materials Chemistry

Computational Discovery of New 2D Materials Using Deep Learning Generative Models Two-dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. Although several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains challenging. Herein, we propose a deep learning generative model for composition generation combined with a random forest-based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template-based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267 489 new potential 2D materials compositions, where 1485 probability scores are more then 0.95. Among them, we have predicted 101 crystal structures and confirmed 92 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery. Generative grammar ; Crystal structure prediction ; Materials science ; Photovoltaics ; Stability (learning theory) ; Chemical space ; Artificial intelligence ; Random forest ; Computer science ; Machine learning ; Nanotechnology ; Crystal structure ; Drug discovery ; Photovoltaic system Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials
Authors: Schwalbe-Koda, Daniel | Tan, Aik Rui | Gómez-Bombarelli, Rafael
Source: Open MIND (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks Abstract Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification methods can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-molecule interactions, and can be extended to any NN potential architecture and materials system. Computer science ; Sampling (signal processing) ; Metric (unit) ; Uncertainty quantification ; Exploit ; Matching (statistics) ; Artificial neural network ; Differentiable function ; Domain (mathematical analysis) ; Molecular dynamics ; Artificial intelligence ; Data mining ; Machine learning ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Peter G. Bolhuis | David W. Swenson
Source: Advanced Theory and Simulations (article, 2021-03-09)
Field: Chemistry — Spectroscopy

Transition Path Sampling as Markov Chain Monte Carlo of Trajectories: Recent Algorithms, Software, Applications, and Future Outlook Abstract The development of enhanced sampling methods to investigate rare but important events has always been a focal point in the molecular simulation field. Such methods often rely on prior knowledge of the reaction coordinate. However, the search for this reaction coordinate is at the heart of the rare event problem. Transition path sampling (TPS) circumvents this problem by generating an ensemble of dynamical trajectories undergoing the activated event. The reaction coordinate is extracted from the resulting path ensemble using variants of machine learning, making it an output of the method instead of an input. Over the last 20 years, since its inception, many extensions of TPS have been developed. Perhaps surprisingly, large‐scale TPS simulations on complex molecular systems have become possible only recently. Other important developments include the transition interface sampling (TIS) methodology to compute rate constants, the application to multiple states, and adaptive path sampling. The development of OpenPathSampling and PyRETIS has enabled easy and flexible use and implementation of these and other novel path sampling algorithms. In this progress report, a brief overview of recent developments, novel algorithms, and software is given. In addition, several application areas are discussed, and a future outlook for the next decade is given. Computer science ; Markov chain Monte Carlo ; Sampling (signal processing) ; Rare events ; Path (computing) ; Algorithm ; Markov chain ; Event (particle physics) ; Adaptive sampling ; Gibbs sampling ; Importance sampling ; Software ; Monte Carlo method ; Machine learning ; Artificial intelligence ; Mathematics ; Bayesian probability Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Si Zhou | Wei Pei | Yanyan Zhao | Xiaowei Yang | Nanshu Liu | Jijun Zhao
Source: npj Computational Materials (article, 2021-11-16)
Field: Materials Science — Materials Chemistry

Low-dimensional non-metal catalysts: principles for regulating p-orbital-dominated reactivity Abstract Activation of p -block elements to replace the rare and precious transition metals for renewable energy applications is highly desirable. In this review, we go over recent experimental and theoretical progress on the low-dimensional non-metal materials for clean energy production, including carbon, silicon, oxide, boron, and phosphorus-based nanostructures, with the p -block elements serving as active sites. We aim to elucidate the mechanism for triggering activity in different kinds of non-metal systems, and extract general principles for controlling the p -orbital-mediated reactivity from a theoretical point of view. The perspectives and challenges for developing high-efficiency non-metal catalysts are provided in the end. Reactivity (psychology) ; Catalysis ; Oxide ; Renewable energy ; Silicon ; Metal ; Boron ; Nanotechnology ; Materials science ; Transition metal ; Block (permutation group theory) ; Carbon fibers ; Main group element ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry ; Electrical engineering MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Wei Chen | Guangchao Li | Xianfeng Yi | Sarah J. Day | Karolina A. Tarach | Zhiqiang Liu | Shang-Bin Liu | Shik Chi Edman Tsang | Kinga Góra‐Marek | Anmin Zheng
Source: Journal of the American Chemical Society (article, 2021-09-03)
Field: Chemistry — Inorganic Chemistry

Molecular Understanding of the Catalytic Consequence of Ketene Intermediates under Confinement Neutral ketene is a crucial intermediate during zeolite carbonylation reactions. In this work, the roles of ketene and its derivates (viz., acylium ion and surface acetyl) associated with direct C-C bond coupling during the carbonylation reaction have been theoretically investigated under realistic reaction conditions and further validated by synchrotron radiation X-ray diffraction (SR-XRD) and Fourier transformed infrared (FT-IR) studies. It has been demonstrated that the zeolite confinement effect has significant influence on the formation, stability, and further transformation of ketene. Thus, the evolution and the role of reactive and inhibitive intermediates depend strongly on the framework structure and pore architecture of the zeolite catalysts. Inside side pockets of mordenite (MOR), rapid protonation of ketene occurs to form a metastable acylium ion exclusively, which is favorable toward methyl acetate (MA) and acetic acid (AcOH) formation. By contrast, in 12MR channels of MOR, a relatively longer lifetime was observed for ketene, which tends to accelerate deactivation of zeolite due to coke formation by the dimerization of ketene and further dissociation to diene and alkyne. Thus, we resolve, for the first time, a long-standing debate regarding the genuine role of ketene in zeolite catalysis. It is a paradigm to demonstrate the confinement effect on the formation, fate, and catalytic consequence of the active intermediates in zeolite catalysis. Ketene ; Chemistry ; Photochemistry ; Catalysis ; Zeolite ; Reaction intermediate ; Reactive intermediate ; Carbonylation ; Protonation ; Reductive elimination ; Medicinal chemistry ; Organic chemistry ; Ion Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Fabrizio Torricelli | Ivano Alessandri | Eleonora Macchia | Irene Vassalini | Marina Maddaloni | Luisa Torsi
Source: Advanced Materials Technologies (article, 2021-07-28)
Field: Materials Science — Polymers and Plastics

Green Materials and Technologies for Sustainable Organic Transistors Abstract Intelligent objects, autonomous factories and humans browsing the real world with artificial technology‐based capabilities is not the movie set of a new saga but what is going to happen with next‐generation electronics and bioelectronics fabricated with emerging materials, technologies, and devices. The emergence of (bio)electronics as a ubiquitous feature of an advanced modern society is posing the challenge of managing an ever‐increasing amount of e‐waste, also making the recovery more and more difficult. Thus, new design approaches are required considering that a possibly small but significant fraction of mass‐scale e‐products is inherently impossible to recover. In this perspective, organic materials and technologies can provide important solutions. This review presents and analyses green materials and technologies underpinning the development of sustainable organic transistors, which promise to become key components for Earth‐safe wide‐spread electronics and bioelectronics. Bioelectronics ; Electronics ; Underpinning ; Nanotechnology ; Emerging technologies ; Electronic materials ; Organic electronics ; Engineering ; Transistor ; Computer science ; Systems engineering ; Electrical engineering ; Materials science ; Civil engineering Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Shashank Shekhar | Shreya Sharma | Amit Kumar | Anjali Taneja | Bhasha Sharma
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

The framework of nanopesticides: a paradigm in biodiversity Nanotechnology has imparted excellent substitutes in the area of agriculture for the management of insect pests without deteriorating the surrounding biota and thereby allows a more verdant environment. Biodiversity ; Biology ; Ecology Nanoparticles: synthesis and applications ; Polymer-Based Agricultural Enhancements ; Quantum Dots Synthesis And Properties
Authors: Hannah M. Britt | Tristan Cragnolini | Konstantinos Thalassinos
Source: Chemical Reviews (review, 2021-09-10)
Field: Chemistry — Spectroscopy

Integration of Mass Spectrometry Data for Structural Biology Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies. Chemistry ; Structural biology ; Mass spectrometry ; Hydrogen–deuterium exchange ; Intrinsically disordered proteins ; Footprinting ; Proteomics ; Computational biology ; Nanotechnology ; Biological system ; Chromatography ; Biochemistry ; DNA Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Advanced Proteomics Techniques and Applications
Authors: Ranaweera K.R. Ranaweera | Dimitra L. Capone | Susan E.P. Bastian | Daniel Cozzolino | David W. Jeffery
Source: Molecules (review, 2021-07-17)
Field: Chemistry — Analytical Chemistry

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication. Chemometrics ; Wine ; Context (archaeology) ; Authentication (law) ; Computer science ; Biochemical engineering ; Instrumentation (computer programming) ; Data science ; Engineering ; Chemistry ; Machine learning ; Computer security ; Geography ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Ya Zhuo | Jakoah Brgoch
Source: The Journal of Physical Chemistry Letters (article, 2021-01-10)
Field: Materials Science — Materials Chemistry

Opportunities for Next-Generation Luminescent Materials through Artificial Intelligence Luminescent materials are continually sought for application in solid-state LED-based lighting and display applications. This has traditionally required extensive experimental effort. More recently, the employment of data-driven approaches in materials science has provided an alternative avenue to accelerate the discovery and development of luminescent materials. In this Perspective, we give an overview of luminescent materials used for lighting and display applications with a specific focus on inorganic phosphors, quantum dots, and organic light-emitting diodes. We discuss recent progress using data-driven approaches to discover new compounds, predict optical properties, and optimize synthesis, among other topics for each type of material. We then highlight future research directions focusing on using artificial intelligence (AI) to advance these fields and address some cross-cutting challenges limiting the current application of AI techniques in luminescence-related research. Limiting ; Nanotechnology ; Luminescence ; Computer science ; Phosphor ; Materials science ; Data science ; Systems engineering ; Engineering ; Optoelectronics ; Mechanical engineering Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications
Authors: Tom A. Young | Tristan Johnston-Wood | Volker L. Deringer | Fernanda Duarte
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A transferable active-learning strategy for reactive molecular force fields Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but doing so typically requires considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models can be developed for diverse chemical systems in an autonomous manner, requiring only hundreds to a few thousand energy and gradient evaluations on a reference potential-energy surface. The approach uses separate intra- and inter-molecular fits and employs a prospective error metric to assess the accuracy of the potentials. We demonstrate applications to a range of molecular systems with relevance to computational organic chemistry: ranging from bulk solvents, a solvated metal ion and a metallocage onwards to chemical reactivity, including a bifurcating Diels-Alder reaction in the gas phase and non-equilibrium dynamics (a model S<sub>N</sub>2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for reactive molecular systems. Chemistry ; Nanotechnology ; Computer science ; Biochemical engineering ; Materials science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Eunji Kim | Dongseon Lee | Youngchun Kwon | Min‐Sik Park | Youn-Suk Choi
Source: Journal of Chemical Information and Modeling (article, 2021-01-07)
Field: Materials Science — Materials Chemistry

Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables Retrosynthesis is an essential task in organic chemistry for identifying the synthesis pathways of newly discovered materials, and with the recent advances in deep learning, there have been growing attempts to solve the retrosynthesis problem through transformer models, which are the state-of-the-art in neural machine translation, by converting the problem into a machine translation problem. However, the pure transformer provides unsatisfactory results that lack grammatical validity, chemical plausibility, and diversity in reactant candidates. In this study, we develop tied two-way transformers with latent modeling to solve those problems using cycle consistency checks, parameter sharing, and multinomial latent variables. Experimental results obtained using public and in-house datasets demonstrate that the proposed model improves the retrosynthesis accuracy, grammatical error, and diversity, and qualitative evaluation results verify its ability to suggest valid and plausible results. Retrosynthetic analysis ; Transformer ; Artificial intelligence ; Computer science ; Machine translation ; Machine learning ; Latent variable ; Natural language processing ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics