jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kristof T. Schütt | Oliver T. Unke | Michael Gastegger
Source: arXiv (Cornell University) (preprint, 2021-02-05)
Field: Materials Science — Materials Chemistry

Equivariant message passing for the prediction of tensorial properties and molecular spectra Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies. While they readily scale to large training data sets, previous approaches have proven to be less data efficient than kernel methods. We identify limitations of invariant representations as a major reason and extend the message passing formulation to rotationally equivariant representations. On this basis, we propose the polarizable atom interaction neural network (PaiNN) and improve on common molecule benchmarks over previous networks, while reducing model size and inference time. We leverage the equivariant atomwise representations obtained by PaiNN for the prediction of tensorial properties. Finally, we apply this to the simulation of molecular spectra, achieving speedups of 4-5 orders of magnitude compared to the electronic structure reference. Leverage (statistics) ; Equivariant map ; Message passing ; Computer science ; Invariant (physics) ; Artificial neural network ; Kernel (algebra) ; Inference ; Theoretical computer science ; Basis (linear algebra) ; Algorithm ; Artificial intelligence ; Mathematics ; Distributed computing ; Physics ; Discrete mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tong Bian | Andrea Gardin | Julius Gemen | Lothar Houben | Claudio Perego | Byeongdu Lee | Nadav Elad | Zonglin Chu | Giovanni M. Pavan | Rafał Klajn
Source: Nature Chemistry (article, 2021-09-06)
Field: Materials Science — Materials Chemistry

Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures Nanoparticle ; Chemistry ; Molecule ; Nanotechnology ; Electrostatics ; Self-assembly ; Fabrication ; Colloid ; Charged particle ; Electrostatic interaction ; Static electricity ; Chemical physics ; Materials science ; Ion ; Organic chemistry ; Physics Pickering emulsions and particle stabilization ; Gold and Silver Nanoparticles Synthesis and Applications ; Electrostatics and Colloid Interactions
Authors: Ernesto Nakayasu | Marina Gritsenko | Paul Piehowski | Yuqian Gao | Danny Orton | Athena Schepmoes | Thomas Fillmore | Brigitte I. Frohnert | Marian Rewers | Jeffrey P. Krischer | Charles Ansong | Astrid M. Suchy‐Dicey | Carmella Evans‐Molina | Weijun Qian | Bobbie‐Jo Webb‐Robertson | Thomas Metz
Source: Nature Protocols (review, 2021-07-09)
Field: Chemistry — Spectroscopy

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation Biomarker discovery ; Computer science ; Biomarker ; Blinding ; Sample size determination ; Data science ; Data collection ; Data quality ; Sample (material) ; Data mining ; Proteomics ; Computational biology ; Bioinformatics ; Clinical trial ; Chemistry ; Chromatography ; Statistics ; Engineering ; Biology ; Mathematics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Pablo R. Salgado | Luciana Di Giorgio | Yanina S. Musso | Adriana N. Mauri
Source: Frontiers in Sustainable Food Systems (article, 2021-04-29)
Field: Materials Science — Biomaterials

Recent Developments in Smart Food Packaging Focused on Biobased and Biodegradable Polymers Food packaging has a crucial function in the modern food industry. New food packaging technologies seek to meet consumers and industrial's demands. Changes related to food production, sale practices and consumers' lifestyles, along with environmental awareness and the advance in new areas of knowledge (such as nanotechnology or biotechnology), act as driving forces to develop smart packages that can extend food shelf-life, keeping and supervising their innocuousness and quality and also taking care of the environment. This review describes the main concepts and types of active and intelligent food packaging, focusing on recent progress and new trends using biodegradable and biobased polymers. Numerous studies show the great possibilities of these materials. Future research needs to focus on some important aspects such as possibilities to scale-up, costs, regulatory aspects, and consumers' acceptance, to make these systems commercially viable. Food packaging ; Active packaging ; Business ; Food industry ; Function (biology) ; Scale (ratio) ; Engineering ; Food science Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Pavel Sinitcyn | Hamid Hamzeiy | Favio Salinas Soto | Daniel N. Itzhak | Frank McCarthy | Christoph Wichmann | Martin Steger | Uli Ohmayer | Ute Distler | Stephanie Kaspar‐Schoenefeld | Nikita A. Prianichnikov | Şule Yılmaz | Jan Daniel Rudolph | Stefan Tenzer | Yasset Pérez‐Riverol | Nagarjuna Nagaraj | Sean J. Humphrey | Jürgen Cox
Source: Nature Biotechnology (article, 2021-07-08)
Field: Chemistry — Spectroscopy

MaxDIA enables library-based and library-free data-independent acquisition proteomics MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification. Workflow ; Proteome ; Computer science ; False discovery rate ; Software ; Matching (statistics) ; Proteomics ; Biomarker discovery ; Data mining ; Computational biology ; Artificial intelligence ; Bioinformatics ; Chemistry ; Biology ; Mathematics ; Database ; Operating system ; Statistics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Mingze Liu | Pengcheng Huo | Wenqi Zhu | Cheng Zhang | Si Zhang | Maowen Song | Song Zhang | Qianwei Zhou | Lu Chen | Henri J. Lezec | Amit Agrawal | Yanqing Lu | Ting Xu
Source: Nature Communications (article, 2021-04-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband generation of perfect Poincaré beams via dielectric spin-multiplexed metasurface The term Poincaré beam, which describes the space-variant polarization of a light beam carrying spin angular momentum (SAM) and orbital angular momentum (OAM), plays an important role in various optical applications. Since the radius of a Poincaré beam conventionally depends on the topological charge number, it is difficult to generate a stable and high-quality Poincaré beam by two optical vortices with different topological charge numbers, as the Poincaré beam formed in this way collapses upon propagation. Here, based on an all-dielectric metasurface platform, we experimentally demonstrate broadband generation of a generalized perfect Poincaré beam (PPB), whose radius is independent of the topological charge number. By utilizing a phase-only modulation approach, a single-layer spin-multiplexed metasurface is shown to achieve all the states of PPBs on the hybrid-order Poincaré Sphere for visible light. Furthermore, as a proof-of-concept demonstration, a metasurface encoding multidimensional SAM and OAM states in the parallel channels of elliptical and circular PPBs is implemented for optical information encryption. We envision that this work will provide a compact and efficient platform for generation of PPBs for visible light, and may promote their applications in optical communications, information encryption, optical data storage and quantum information sciences. Angular momentum ; Physics ; Multiplexing ; Optics ; Optical vortex ; Light beam ; Broadband ; Optical communication ; Topological quantum number ; Beam (structure) ; Polarization (electrochemistry) ; Topology (electrical circuits) ; Quantum mechanics ; Telecommunications ; Computer science ; Mathematics Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Junwei Gu | Kunpeng Ruan
Source: Nano-Micro Letters (article, 2021-04-13)
Field: Materials Science — Materials Chemistry

Breaking Through Bottlenecks for Thermally Conductive Polymer Composites: A Perspective for Intrinsic Thermal Conductivity, Interfacial Thermal Resistance and Theoretics Rapid development of energy, electrical and electronic technologies has put forward higher requirements for the thermal conductivities of polymers and their composites. However, the thermal conductivity coefficient (λ) values of prepared thermally conductive polymer composites are still difficult to achieve expectations, which has become the bottleneck in the fields of thermally conductive polymer composites. Aimed at that, based on the accumulation of the previous research works by related researchers and our research group, this paper proposes three possible directions for breaking through the bottlenecks: (1) preparing and synthesizing intrinsically thermally conductive polymers, (2) reducing the interfacial thermal resistance in thermally conductive polymer composites, and (3) establishing suitable thermal conduction models and studying inner thermal conduction mechanism to guide experimental optimization. Also, the future development trends of the three above-mentioned directions are foreseen, hoping to provide certain basis and guidance for the preparation, researches and development of thermally conductive polymers and their composites. Materials science ; Electrical conductor ; Composite material ; Thermal conductivity ; Thermal conduction ; Conductive polymer ; Polymer ; Thermal ; Thermal resistance ; Thermodynamics Thermal properties of materials ; Carbon Nanotubes in Composites ; Composite Material Mechanics
Authors: David Bialas | Eva Kirchner | Merle I. S. Röhr | Frank Würthner
Source: Journal of the American Chemical Society (article, 2021-03-15)
Field: Chemistry — Physical and Theoretical Chemistry

Perspectives in Dye Chemistry: A Rational Approach toward Functional Materials by Understanding the Aggregate State The past 20 years have witnessed a renaissance of dye chemistry, moving from traditional colorant research toward functional materials. Different from traditional colorant research, the properties of functional materials are governed extensively by intermolecular interactions, thereby entailing significant limitations to the classical approach based on molecular structure-molecular property (color, emission, redox properties, etc.) relationships for the respective dye molecules. However, as discussed in this Perspective, such an approach can be pursued for dye aggregates, and in many cases already well-tailored dimers are sufficient to understand the influence of supramolecular organization on the functional properties of ground and photoexcited states. Illustrative examples will be given for exciton coupling and charge-transfer coupling and how these properties relate to desirable functions such as fluorescence, symmetry-breaking charge separation, and singlet fission in molecular aggregates. While the progress in this research so far mostly originated from studies on well-defined folded and self-assembled structures composed of only two dye molecules, future work will have to advance toward larger oligomers of specific size and geometry. Furthermore, future experimental studies should be guided to a larger extent by theoretical predictions that may be supported by machine learning algorithms and new concepts from artificial intelligence. Beyond already pursued calculations of potential energy landscapes, we suggest the development of theoretical approaches that identify the most desirable dye aggregate structures for a particular property on functional energy landscapes. Chemistry ; Intermolecular force ; Supramolecular chemistry ; Chemical physics ; Density functional theory ; Molecule ; Intramolecular force ; Nanotechnology ; Computational chemistry ; Organic chemistry Photochemistry and Electron Transfer Studies ; Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials
Authors: Julien Peyrton | Luc Avérous
Source: Materials Science and Engineering R Reports (article, 2021-03-10)
Field: Materials Science — Polymers and Plastics

Structure-properties relationships of cellular materials from biobased polyurethane foams Raw material ; Polyurethane ; Ultimate tensile strength ; Thermal insulation ; Materials science ; Composite material ; Chemistry ; Organic chemistry Polymer composites and self-healing ; Polymer Foaming and Composites ; biodegradable polymer synthesis and properties
Authors: Yang Woo Kwon | Han-Seul Jo | Sungwon Bae | Youngsuk Seo | Parkyong Song | Minseok Song | Jong Hyuk Yoon
Source: Frontiers in Medicine (review, 2021-09-22)
Field: Chemistry — Spectroscopy

Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen. Proteomics ; Proteome ; Computational biology ; Identification (biology) ; Proteogenomics ; Biomarker discovery ; Bioinformatics ; Data science ; Computer science ; Biology ; Genomics ; Genome Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Bioinformatics
Authors: Haile Liu | Yonghui Li | Si Sun | Qi Xin | Shuhu Liu | Xiaoyu Mu | Xun Yuan | Ke Chen | Hao Wang | Kalman Varga | Wenbo Mi | Jiang Yang | Xiao-Dong Zhang
Source: Nature Communications (article, 2021-01-07)
Field: Materials Science — Materials Chemistry

Catalytically potent and selective clusterzymes for modulation of neuroinflammation through single-atom substitutions Emerging artificial enzymes with reprogrammed and augmented catalytic activity and substrate selectivity have long been pursued with sustained efforts. The majority of current candidates have rather poor catalytic activity compared with natural molecules. To tackle this limitation, we design artificial enzymes based on a structurally well-defined Au<sub>25</sub> cluster, namely clusterzymes, which are endowed with intrinsic high catalytic activity and selectivity driven by single-atom substitutions with modulated bond lengths. Au<sub>24</sub>Cu<sub>1</sub> and Au<sub>24</sub>Cd<sub>1</sub> clusterzymes exhibit 137 and 160 times higher antioxidant capacities than natural trolox, respectively. Meanwhile, the clusterzymes demonstrate preferential enzyme-mimicking catalytic activities, with Au<sub>25</sub>, Au<sub>24</sub>Cu<sub>1</sub> and Au<sub>24</sub>Cd<sub>1</sub> displaying compelling selectivity in glutathione peroxidase-like (GPx-like), catalase-like (CAT-like) and superoxide dismutase-like (SOD-like) activities, respectively. Au<sub>24</sub>Cu<sub>1</sub> decreases peroxide in injured brain via catalytic reactions, while Au<sub>24</sub>Cd<sub>1</sub> preferentially uses superoxide and nitrogenous signal molecules as substrates, and significantly decreases inflammation factors, indicative of an important role in mitigating neuroinflammation. Selectivity ; Superoxide ; Catalysis ; Substrate (aquarium) ; Enzyme ; Antioxidant ; Neuroinflammation ; Glutathione ; Small molecule Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Naozumi Hiranuma | Hahnbeom Park | Minkyung Baek | Ivan Anishchenko | Justas Dauparas | David Baker
Source: Nature Communications (article, 2021-02-26)
Field: Materials Science — Materials Chemistry

Improved protein structure refinement guided by deep learning based accuracy estimation We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules. Computer science ; Maxima and minima ; Protein structure prediction ; Protein structure ; Network structure ; Algorithm ; Deep learning ; Artificial intelligence ; Data mining ; Machine learning ; Mathematics ; Chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies
Authors: Philippe Schwaller | Alain C. Vaucher | Teodoro Laino | Jean‐Louis Reymond
Source: Machine Learning Science and Technology (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Prediction of chemical reaction yields using deep learning Abstract Artificial intelligence is driving one of the most important revolutions in organic chemistry. Multiple platforms, including tools for reaction prediction and synthesis planning based on machine learning, have successfully become part of the organic chemists’ daily laboratory, assisting in domain-specific synthetic problems. Unlike reaction prediction and retrosynthetic models, the prediction of reaction yields has received less attention in spite of the enormous potential of accurately predicting reaction conversion rates. Reaction yields models, describing the percentage of the reactants converted to the desired products, could guide chemists and help them select high-yielding reactions and score synthesis routes, reducing the number of attempts. So far, yield predictions have been predominantly performed for high-throughput experiments using a categorical (one-hot) encoding of reactants, concatenated molecular fingerprints, or computed chemical descriptors. Here, we extend the application of natural language processing architectures to predict reaction properties given a text-based representation of the reaction, using an encoder transformer model combined with a regression layer. We demonstrate outstanding prediction performance on two high-throughput experiment reactions sets. An analysis of the yields reported in the open-source USPTO data set shows that their distribution differs depending on the mass scale, limiting the data set applicability in reaction yields predictions. Computer science ; Categorical variable ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Ruichao Zhu | Tianshuo Qiu | Jiafu Wang | Sai Sui | Chenglong Hao | Tonghao Liu | Yongfeng Li | Mingde Feng | Anxue Zhang | Cheng‐Wei Qiu | Shaobo Qu
Source: Nature Communications (article, 2021-05-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning Metasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 2<sup>8×8</sup> meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span. Realization (probability) ; Computer science ; Inverse ; Phase (matter) ; Reflection (computer programming) ; Degrees of freedom (physics and chemistry) ; Inverse problem ; Electronic engineering ; Physics ; Mathematics ; Engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Energy Harvesting in Wireless Networks