jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brian C. Searle | Lindsay K. Pino | Jarrett D. Egertson | Ying S. Ting | Robert Lawrence | Brendan MacLean | Judit Villén | Michael J. MacCoss
Source: Nature Communications (article, 2018-11-27)
Field: Chemistry — Spectroscopy

Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of proteomics studies. Here, we introduce an experimental workflow that uses this technique to construct chromatogram libraries that capture fragment ion chromatographic peak shape and retention time for every detectable peptide in a proteomics experiment. These coordinates calibrate protein databases or spectrum libraries to a specific mass spectrometer and chromatography setup, facilitating DIA-only pipelines and the reuse of global resource libraries. We also present EncyclopeDIA, a software tool for generating and searching chromatogram libraries, and demonstrate the performance of our workflow by quantifying proteins in human and yeast cells. We find that by exploiting calibrated retention time and fragmentation specificity in chromatogram libraries, EncyclopeDIA can detect 20-25% more peptides from DIA experiments than with data dependent acquisition-based spectrum libraries alone. Mass spectrometry ; Workflow ; Chromatography ; Proteomics ; Computer science ; Data acquisition ; Fragmentation (computing) ; Chemistry ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Liz Potterton | Jon Agirre | Charles Ballard | Kevin Cowtan | E.J. Dodson | Phil Evans | Huw T. Jenkins | Ronan M. Keegan | Eugene Krissinel | Kyle Stevenson | Andrey A. Lebedev | Stuart McNicholas | Robert A. Nicholls | M.E.M. Noble | Neesh Pannu | Christian Roth | George M. Sheldrick | Pavol Skubák | J.P. Turkenburg | Ville Uski | F. von Delft | David G. Waterman | Keith S. Wilson | Martyn Winn | Marcin Wojdyr
Source: Acta Crystallographica Section D Structural Biology (article, 2018-02-01)
Field: Materials Science — Materials Chemistry

<i>CCP</i>4<i>i</i>2: the new graphical user interface to the<i>CCP</i>4 program suite The CCP4 (Collaborative Computational Project, Number 4) software suite for macromolecular structure determination by X-ray crystallography groups brings together many programs and libraries that, by means of well established conventions, interoperate effectively without adhering to strict design guidelines. Because of this inherent flexibility, users are often presented with diverse, even divergent, choices for solving every type of problem. Recently, CCP4 introduced CCP4i2, a modern graphical interface designed to help structural biologists to navigate the process of structure determination, with an emphasis on pipelining and the streamlined presentation of results. In addition, CCP4i2 provides a framework for writing structure-solution scripts that can be built up incrementally to create increasingly automatic procedures. Suite ; Computer science ; Flexibility (engineering) ; Scripting language ; Interoperability ; Graphical user interface ; Interface (matter) ; Software ; Software engineering ; User interface ; Presentation (obstetrics) ; Process (computing) ; Session (web analytics) ; Programming language ; World Wide Web ; Operating system Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Rahul Rao | Cary L. Pint | Ahmad E. Islam | Robert S. Weatherup | Stephan Hofmann | Eric R. Meshot | Fanqi Wu | Chongwu Zhou | Nicholas T. Dee | Placidus B. Amama | Jennifer Carpena‐Núñez | Wenbo Shi | Desirée L. Plata | Evgeni S. Penev | Boris I. Yakobson | Perla B. Balbuena | Christophe Bichara | Don N. Futaba | Suguru Noda | Homin Shin | Keun Su Kim | Benoît Simard | Francesca Mirri | Matteo Pasquali | Francesco Fornasiero | Esko I. Kauppinen | Michael S. Arnold | Baratunde A. Cola | Pavel Nikolaev | Sivaram Arepalli | Hui–Ming Cheng | Dmitri N. Zakharov | Eric A. Stach | Jin Zhang | Fei Wei | Mauricio Terrones | David B. Geohegan | Benji Maruyama | Shigeo Maruyama | Yan Li | W. Wade Adams | A. John Hart
Source: ACS Nano (article, 2018-12-05)
Field: Materials Science — Materials Chemistry

Carbon Nanotubes and Related Nanomaterials: Critical Advances and Challenges for Synthesis toward Mainstream Commercial Applications Advances in the synthesis and scalable manufacturing of single-walled carbon nanotubes (SWCNTs) remain critical to realizing many important commercial applications. Here we review recent breakthroughs in the synthesis of SWCNTs and highlight key ongoing research areas and challenges. A few key applications that capitalize on the properties of SWCNTs are also reviewed with respect to the recent synthesis breakthroughs and ways in which synthesis science can enable advances in these applications. While the primary focus of this review is on the science framework of SWCNT growth, we draw connections to mechanisms underlying the synthesis of other 1D and 2D materials such as boron nitride nanotubes and graphene. Nanotechnology ; Carbon nanotube ; Boron nitride ; Materials science ; Graphene ; Nanomaterials Carbon Nanotubes in Composites ; Graphene research and applications ; Advancements in Battery Materials
Authors: Catherine Weetman | Shigeyoshi Inoue
Source: ChemCatChem (article, 2018-07-22)
Field: Chemistry — Organic Chemistry

The Road Travelled: After Main‐Group Elements as Transition Metals Abstract Since the latter quarter of the twentieth century, main group chemistry has undergone significant advances. Power's timely review in 2010 highlighted the inherent differences between the lighter and heavier main group elements, and that the heavier analogues resemble transition metals as shown by their reactivity towards small molecules. In this concept article, we present an overview of the last 10 years since Power's seminal review, and the progress made for catalytic application. This examines the use of low oxidation state and/or low coordinate group 13 and 14 complexes towards small molecule activation (oxidative addition step in a redox based cycle) and how ligand design plays a crucial role in influencing subsequent reactivity. The challenge in these redox based catalytic cycles still centres on the main group complexes’ ability to undergo reductive elimination, however considerable progress in this field has been reported via reversible oxidative addition reactions. Within the last 5 years the first examples of well‐defined low valent main group catalysts have begun to emerge, representing a bright future ahead for main group chemistry. Reactivity (psychology) ; Reductive elimination ; Redox ; Catalysis ; Chemistry ; Transition metal ; Catalytic cycle ; Main group element ; Group (periodic table) ; Oxidation state ; Molecule ; Oxidative addition ; Ligand (biochemistry) ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Coordination Chemistry and Organometallics
Authors: Xinle Li | Changlin Zhang | Song‐Liang Cai | Xiaohe Lei | Virginia Altoé | Fang Hong | Jeffrey J. Urban | Jim Ciston | Emory M. Chan | Yi Liu
Source: Nature Communications (article, 2018-07-25)
Field: Materials Science — Materials Chemistry

Facile transformation of imine covalent organic frameworks into ultrastable crystalline porous aromatic frameworks The growing interest in two-dimensional imine-based covalent organic frameworks (COFs) is inspired by their crystalline porous structures and the potential for extensive π-electron delocalization. The intrinsic reversibility and strong polarization of imine linkages, however, leads to insufficient chemical stability and optoelectronic properties. Developing COFs with improved robustness and π-delocalization is highly desirable but remains an unsettled challenge. Here we report a facile strategy that transforms imine-linked COFs into ultrastable porous aromatic frameworks by kinetically fixing the reversible imine linkage via an aza-Diels-Alder cycloaddition reaction. The as-formed, quinoline-linked COFs not only retain crystallinity and porosity, but also display dramatically enhanced chemical stability over their imine-based COF precursors, rendering them among the most robust COFs up-to-date that can withstand strong acidic, basic and redox environment. Owing to the chemical diversity of the cycloaddition reaction and structural tunability of COFs, the pores of COFs can be readily engineered to realize pre-designed surface functionality. Imine ; Covalent bond ; Covalent organic framework ; Materials science ; Cycloaddition ; Delocalized electron ; Crystallinity ; Porosity ; Chemical stability ; Chemical engineering ; Aromaticity ; Photochemistry ; Nanotechnology ; Chemistry ; Organic chemistry ; Molecule ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Kun Yao | John E. Herr | David W. Toth | Ryker Mckintyre | John Parkhill
Source: Chemical Science (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near <i>ab initio</i> accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed "TensorMol-0.1", is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry. Python (programming language) ; Molecular dynamics ; Statistical physics ; Artificial neural network ; Scalability ; van der Waals force ; Computer science ; Monte Carlo method ; Robustness (evolution) ; Computational chemistry ; Computational science ; Physics ; Chemistry ; Artificial intelligence ; Quantum mechanics ; Molecule ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jianguo Wang | Xinggui Gu | Huili Ma | Qian Peng | Xiaobo Huang | Xiaoyan Zheng | Simon H. P. Sung | Guo‐Gang Shan | Jacky W. Y. Lam | Zhigang Shuai | Ben Zhong Tang
Source: Nature Communications (article, 2018-07-23)
Field: Materials Science — Materials Chemistry

A facile strategy for realizing room temperature phosphorescence and single molecule white light emission Research on materials with pure organic room temperature phosphorescence (RTP) and their application as organic single-molecule white light emitters is a hot area and relies on the design of highly efficient pure organic RTP luminogens. Herein, a facile strategy of heavy-atom-participated anion-π<sup>+</sup> interactions is proposed to construct RTP-active organic salt compounds (1,2,3,4-tetraphenyloxazoliums with different counterions). Those compounds with heavy-atom counterions (bromide and iodide ions) exhibit outstanding RTP due to the external heavy atom effect via anion-π<sup>+</sup> interactions, evidently supported by the single-crystal X-ray diffraction analysis and theoretical calculation. Their single-molecule white light emission is realized by tuning the degree of crystallization. Such white light emission also performs well in polymer matrices and their use in 3D printing is demonstrated by white light lampshades. Phosphorescence ; Molecule ; Ion ; Materials science ; Counterion ; Atom (system on chip) ; Iodide ; Crystallization ; Phosphor ; Bromide ; Single crystal ; Photochemistry ; Chemistry ; Crystallography ; Optoelectronics ; Inorganic chemistry ; Fluorescence ; Organic chemistry ; Optics ; Physics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Molecular Sensors and Ion Detection
Authors: Shuo Feng | Huiyu Zhou | Hongbiao Dong
Source: Materials & Design (article, 2018-11-29)
Field: Materials Science — Materials Chemistry

Using deep neural network with small dataset to predict material defects Deep neural network (DNN) exhibits state-of-the-art performance in many fields including microstructure recognition where big dataset is used in training. However, DNN trained by conventional methods with small datasets commonly shows worse performance than traditional machine learning methods, e.g. shallow neural network and support vector machine. This inherent limitation prevented the wide adoption of DNN in material study because collecting and assembling big dataset in material science is a challenge. In this study, we attempted to predict solidification defects by DNN regression with a small dataset that contains 487 data points. It is found that a pre-trained and fine-tuned DNN shows better generalization performance over shallow neural network, support vector machine, and DNN trained by conventional methods. The trained DNN transforms scattered experimental data points into a map of high accuracy in high-dimensional chemistry and processing parameters space. Though DNN with big datasets is the optimal solution, DNN with small datasets and pre-training can be a reasonable choice when big datasets are unavailable in material study. Artificial neural network ; Artificial intelligence ; Generalization ; Support vector machine ; Machine learning ; Computer science ; Deep neural networks ; Big data ; Deep learning ; Small data ; Space (punctuation) ; Pattern recognition (psychology) ; Data mining ; Mathematics Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Industrial Vision Systems and Defect Detection
Authors: Dipendra Jha | Logan Ward | Arindam Paul | Wei‐keng Liao | Alok Choudhary | Chris Wolverton | Ankit Agrawal
Source: Scientific Reports (article, 2018-11-28)
Field: Materials Science — Materials Chemistry

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of ElemNet enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds. Composition (language) ; Deep learning ; Computer science ; Chemistry ; Chemical engineering ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Christopher D. Spicer | Coline Jumeaux | Bakul Gupta | Molly M. Stevens
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Biomaterials

Peptide and protein nanoparticle conjugates: versatile platforms for biomedical applications Peptide- and protein-nanoparticle conjugates have emerged as powerful tools for biomedical applications, enabling the treatment, diagnosis, and prevention of disease. In this review, we focus on the key roles played by peptides and proteins in improving, controlling, and defining the performance of nanotechnologies. Within this framework, we provide a comprehensive overview of the key sequences and structures utilised to provide biological and physical stability to nano-constructs, direct particles to their target and influence their cellular and tissue distribution, induce and control biological responses, and form polypeptide self-assembled nanoparticles. In doing so, we highlight the great advances made by the field, as well as the challenges still faced in achieving the clinical translation of peptide- and protein-functionalised nano-drug delivery vehicles, imaging species, and active therapeutics. Nanoparticle ; Conjugate ; Peptide ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Materials science ; Computer science ; Biochemistry Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; RNA Interference and Gene Delivery
Authors: Nicola De Cao | Thomas Kipf
Source: arXiv (Cornell University) (preprint, 2018-05-30)
Field: Materials Science — Materials Chemistry

MolGAN: An implicit generative model for small molecular graphs Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties. In experiments on the QM9 chemical database, we demonstrate that our model is capable of generating close to 100% valid compounds. MolGAN compares favorably both to recent proposals that use string-based (SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit being susceptible to mode collapse. Code at https://github.com/nicola-decao/MolGAN Generative grammar ; Computer science ; Generative model ; Differentiable function ; Graph ; Theoretical computer science ; Molecular graph ; Node (physics) ; String (physics) ; Chemical space ; Algorithm ; Artificial intelligence ; Machine learning ; Mathematics ; Bioinformatics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Chiho Kim | Anand Chandrasekaran | Tran Doan Huan | Deya Das | Rampi Ramprasad
Source: The Journal of Physical Chemistry C (article, 2018-07-13)
Field: Materials Science — Materials Chemistry

Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions The recent successes of the Materials Genome Initiative have opened up new opportunities for data-centric informatics approaches in several subfields of materials research, including in polymer science and engineering. Polymers, being inexpensive and possessing a broad range of tunable properties, are widespread in many technological applications. The vast chemical and morphological complexity of polymers though gives rise to challenges in the rational discovery of new materials for specific applications. The nascent field of polymer informatics seeks to provide tools and pathways for accelerated property prediction (and materials design) via surrogate machine learning models built on reliable past data. We have carefully accumulated a data set of organic polymers whose properties were obtained either computationally (bandgap, dielectric constant, refractive index, and atomization energy) or experimentally (glass transition temperature, solubility parameter, and density). A fingerprinting scheme that captures atomistic to morphological structural features was developed to numerically represent the polymers. Machine learning models were then trained by mapping the fingerprints (or features) to properties. Once developed, these models can rapidly predict properties of new polymers (within the same chemical class as the parent data set) and can also provide uncertainties underlying the predictions. Since different properties depend on different length-scale features, the prediction models were built on an optimized set of features for each individual property. Furthermore, these models are incorporated in a user-friendly online platform named Polymer Genome (www.polymergenome.org). Systematic and progressive expansion of both chemical and property spaces are planned to extend the applicability of Polymer Genome to a wide range of technological domains. Materials informatics ; Property (philosophy) ; Computer science ; Polymer ; Set (abstract data type) ; Characterization (materials science) ; Informatics ; Materials science ; Nanotechnology ; Engineering ; Health informatics ; Engineering informatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Philippe Schwaller | Théophile Gaudin | Dávid Lányi | Costas Bekas | Teodoro Laino
Source: Chemical Science (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Based on this analogy, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a tokenization, which is arbitrarily extensible with reaction information. Using an attention-based model borrowed from human language translation, we improve the state-of-the-art solutions in reaction prediction on the top-1 accuracy by achieving 80.3% without relying on auxiliary knowledge, such as reaction templates or explicit atomic features. Also, a top-1 accuracy of 65.4% is reached on a larger and noisier dataset. Sequence (biology) ; Translation (biology) ; Chemistry ; Computational biology ; Computer science ; Artificial intelligence ; Biology ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Albert P. Bartók | James R. Kermode | Noam Bernstein | Gábor Csányi
Source: Physical Review X (article, 2018-12-14)
Field: Materials Science — Materials Chemistry

Machine Learning a General-Purpose Interatomic Potential for Silicon A machine-learning based approach to computing the energy and forces of silicon atoms circumvents the need for complex electronic structure calculations, offering an efficient method for predicting material properties that can be extended to other materials. Interatomic potential ; Statistical physics ; Computer science ; Gaussian process ; Observable ; Gaussian ; Electronic structure ; Algorithm ; Machine learning ; Molecular dynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography