jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mariela Toriello | Morteza Afsari | Ho Kyong Shon | Leonard D. Tijing
Source: Membranes (review, 2020-08-28)
Field: Materials Science — Biomaterials

Progress on the Fabrication and Application of Electrospun Nanofiber Composites Nanofibers are one of the most attractive materials in various applications due to their unique properties and promising characteristics for the next generation of materials in the fields of energy, environment, and health. Among the many fabrication methods, electrospinning is one of the most efficient technologies which has brought about remarkable progress in the fabrication of nanofibers with high surface area, high aspect ratio, and porosity features. However, neat nanofibers generally have low mechanical strength, thermal instability, and limited functionalities. Therefore, composite and modified structures of electrospun nanofibers have been developed to improve the advantages of nanofibers and overcome their drawbacks. The combination of electrospinning technology and high-quality nanomaterials via materials science advances as well as new modification techniques have led to the fabrication of composite and modified nanofibers with desired properties for different applications. In this review, we present the recent progress on the fabrication and applications of electrospun nanofiber composites to sketch a progress line for advancements in various categories. Firstly, the different methods for fabrication of composite and modified nanofibers have been investigated. Then, the current innovations of composite nanofibers in environmental, healthcare, and energy fields have been described, and the improvements in each field are explained in detail. The continued growth of composite and modified nanofiber technology reveals its versatile properties that offer alternatives for many of current industrial and domestic issues and applications. Nanofiber ; Electrospinning ; Fabrication ; Materials science ; Nanotechnology ; Composite number ; Composite material ; Polymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Malthe Kjær Bisbo | Bjørk Hammer
Source: Physical Review Letters (article, 2020-02-27)
Field: Materials Science — Materials Chemistry

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model We propose a scheme for global optimization with first-principles energy expressions of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a number of local relaxations (exploitation) and further structural searches (exploration). Assuming Gaussian processes, deploying two separate kernel widths to better capture rough features of the energy landscape while retaining a good resolution of local minima, an acquisition function is used to decide on which of the resulting structures is the more promising and should be treated at the first-principles level. The method is demonstrated to outperform by 2 orders of magnitude a well established first-principles based evolutionary algorithm in finding surface reconstructions. Finally, global optimization with first-principles energy expressions is utilized to identify initial stages of the edge oxidation and oxygen intercalation of graphene sheets on the Ir(111) surface. Surrogate model ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Xiaoxia Zhang | Songcheng Xu | Lirui Shen | Guoying Li
Source: Journal of Leather Science and Engineering (article, 2020-08-05)
Field: Materials Science — Biomaterials

Factors affecting thermal stability of collagen from the aspects of extraction, processing and modification Abstract Collagen, as a thermal-sensitive protein, is the most abundant structural protein in animals. Native collagen has been widely applied in various fields due to its specific physicochemical and biological properties. The beneficial properties would disappear with the collapse of the unique triple helical structure during heating. Understanding thermal stability of collagen is of great significance for practical applications. Previous studies have shown the thermal stability would be affected by the different sources, extraction methods, solvent systems in vitro and modified methods. Accordingly, the factors affecting thermal stability of collagen are discussed in detail in this review. Graphical abstract Thermal stability ; Extraction (chemistry) ; Thermal ; Materials science ; Chemistry ; Biophysics ; Chemical engineering ; Chromatography ; Thermodynamics ; Biology ; Engineering ; Organic chemistry ; Physics Collagen: Extraction and Characterization ; Protein Hydrolysis and Bioactive Peptides ; Proteins in Food Systems
Authors: Kevin Kaufmann | Chaoyi Zhu | Alexander S. Rosengarten | Daniel Maryanovsky | Tyler Harrington | Eduardo Marin | Kenneth S. Vecchio
Source: Science (article, 2020-01-31)
Field: Materials Science — Materials Chemistry

Crystal symmetry determination in electron diffraction using machine learning Electron backscatter diffraction (EBSD) is one of the primary tools for crystal structure determination. However, this method requires human input to select potential phases for Hough-based or dictionary pattern matching and is not well suited for phase identification. Automated phase identification is the first step in making EBSD into a high-throughput technique. We used a machine learning-based approach and developed a general methodology for rapid and autonomous identification of the crystal symmetry from EBSD patterns. We evaluated our algorithm with diffraction patterns from materials outside the training set. The neural network assigned importance to the same symmetry features that a crystallographer would use for structure identification. Electron backscatter diffraction ; Bravais lattice ; Diffraction ; Electron diffraction ; Artificial neural network ; Computer science ; Artificial intelligence ; Electron crystallography ; Convolutional neural network ; Materials science ; Synchrotron ; Crystallography ; Crystal structure ; Optics ; Physics ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Johannes Gasteiger | Shankari Giri | Johannes T. Margraf | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2020-11-28)
Field: Materials Science — Materials Chemistry

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules Many important tasks in chemistry revolve around molecules during reactions. This requires predictions far from the equilibrium, while most recent work in machine learning for molecules has been focused on equilibrium or near-equilibrium states. In this paper we aim to extend this scope in three ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more accurate than the original DimeNet on the QM9 benchmark of equilibrium molecules. Second, we validate DimeNet++ on highly reactive molecules by developing the challenging COLL dataset, which contains distorted configurations of small molecules during collisions. Finally, we investigate ensembling and mean-variance estimation for uncertainty quantification with the goal of accelerating the exploration of the vast space of non-equilibrium structures. Our DimeNet++ implementation as well as the COLL dataset are available online. Benchmark (surveying) ; Scope (computer science) ; Computer science ; Variance (accounting) ; Molecule ; Work (physics) ; Chemistry ; Physics ; Thermodynamics ; Economics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Kevin McCloskey | Eric A. Sigel | Steven Kearnes | Ling Xue | Tian Xia | Dennis Moccia | Diana Gikunju | Sana Bazzaz | Betty Chan | Matthew Clark | John W. Cuozzo | Marie-Aude Guié | John P. Guilinger | Christelle Huguet | Christopher D. Hupp | Anthony D. Keefe | Christopher J. Mulhern | Ying Zhang | Patrick Riley
Source: Journal of Medicinal Chemistry (article, 2020-06-11)
Field: Materials Science — Materials Chemistry

Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from large libraries of commercial and easily synthesizable compounds. We train models using only DEL selection data and apply automated or automatable filters to the predictions. We perform a large prospective study (∼2000 compounds) across three diverse protein targets: sEH (a hydrolase), ERα (a nuclear receptor), and c-KIT (a kinase). The approach is effective, with an overall hit rate of ∼30% at 30 μM and discovery of potent compounds (IC<sub>50</sub> < 10 nM) for every target. The system makes useful predictions even for molecules dissimilar to the original DEL, and the compounds identified are diverse, predominantly drug-like, and different from known ligands. This work demonstrates a powerful new approach to hit-finding. Chemistry ; DNA ; Computational biology ; Cognitive science ; Artificial intelligence ; Biochemistry ; Computer science ; Psychology Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Advanced Proteomics Techniques and Applications
Authors: Jack D. Evans | Volodymyr Bon | Irena Senkovska | Hui‐Chun Lee | Stefan Kaskel
Source: Nature Communications (review, 2020-06-01)
Field: Chemistry — Inorganic Chemistry

Four-dimensional metal-organic frameworks Recognising timescale as an adjustable dimension in porous solids provides a new perspective to develop novel four-dimensional framework materials. The deliberate design of three-dimensional porous framework architectures is a developed field; however, the understanding of dynamics in open frameworks leaves a number of key questions unanswered: What factors determine the spatiotemporal evolution of deformable networks? Can we deliberately engineer the response of dynamic materials along a time-axis? How can we engineer energy barriers for the selective recognition of molecules? Answering these questions will require significant methodological development to understand structural dynamics across a range of time and length scales. Computer science ; Perspective (graphical) ; Dimension (graph theory) ; Key (lock) ; Field (mathematics) ; Data science ; Range (aeronautics) ; Metal-organic framework ; Porous medium ; Nanotechnology ; Porosity ; Artificial intelligence ; Materials science ; Aerospace engineering ; Engineering ; Mathematics ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Self-Assembly in Materials ; Machine Learning in Materials Science
Authors: Indranil Bhattacharjee | Shuzo Hirata
Source: Advanced Materials (article, 2020-06-28)
Field: Materials Science — Materials Chemistry

Highly Efficient Persistent Room‐Temperature Phosphorescence from Heavy Atom‐Free Molecules Triggered by Hidden Long Phosphorescent Antenna Persistent (lifetime > 100 ms) room-temperature phosphorescence (pRTP) is important for state-of-the-art security and bioimaging applications. An unclear relationship between chromophores and physical parameters relating to pRTP has prevented obtaining an RTP yield of over 50% and a lifetime over 1 s. Here highly efficient pRTP is reported under ambient conditions from heavy atom-free chromophores. A heavy atom-free aromatic core substituted with a long-conjugated amino group considerably accelerates the phosphorescence rate independent of the intramolecular vibration-based nonradiative rate from the lowest excited triplet state. One of the designed heavy atom-free dopant chromophores presents an RTP yield of 50% with a lifetime of 1 s under ambient conditions. The afterglow brightness under strong excitation is at least 10<sup>4</sup> times stronger than that of conventional long-persistent luminescence emitters. Here it is shown that highly efficient pRTP materials allow for high-resolution gated emission with a size of the diffraction limit using small-scale and low-cost photodetectors. Phosphorescence ; Materials science ; Atom (system on chip) ; Persistent luminescence ; Photochemistry ; Molecule ; Optoelectronics ; Luminescence ; Optics ; Fluorescence ; Organic chemistry ; Physics ; Chemistry Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Advanced biosensing and bioanalysis techniques
Authors: Zhenbin Wang | Xingyu Guo | Joseph H. Montoya | Jens K. Nørskov
Source: npj Computational Materials (article, 2020-10-21)
Field: Materials Science — Materials Chemistry

Predicting aqueous stability of solid with computed Pourbaix diagram using SCAN functional Abstract In this work, using the SCAN functional, we develop a simple method on top of the Materials Project (MP) Pourbaix diagram framework to accurately predict the aqueous stability of solids. We extensively evaluate the SCAN functional’s performance in computed formation enthalpies for a broad range of oxides and develop Hubbard U corrections for transition-metal oxides where the standard SCAN functional exhibits large deviations. The performance of the calculated Pourbaix diagram using the SCAN functional is validated with comparison to the experimental and the MP PBE Pourbaix diagrams for representative examples. Benchmarks indicate the SCAN Pourbaix diagram systematically outperforms the MP PBE in aqueous stability prediction. We further show applications of this method in accurately predicting the dissolution potentials of the state-of-the-art catalysts for oxygen evolution reaction in acidic media. Pourbaix diagram ; Stability (learning theory) ; Diagram ; Aqueous solution ; Mathematics ; Computer science ; Chemistry ; Statistics ; Physical chemistry ; Machine learning ; Electrochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Martha M. Flores‐Leonar | L.M. Mejía-Mendoza | Andrés Aguilar‐Granda | Benjamín Sánchez-Lengeling | Hermann Tribukait | Carlos Amador‐Bedolla | Alán Aspuru‐Guzik
Source: Current Opinion in Green and Sustainable Chemistry (article, 2020-06-18)
Field: Materials Science — Materials Chemistry

Materials Acceleration Platforms: On the way to autonomous experimentation Computer science ; Field (mathematics) ; Autonomy ; Acceleration ; Work (physics) ; Systems engineering ; Data science ; Human–computer interaction ; Artificial intelligence ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Chongxiong Duan | Yi Yu | Jing Xiao | Xuelian Zhang | Libo Li | Pengfei Yang | Junliang Wu | Hongxia Xi
Source: Science China Materials (review, 2020-03-27)
Field: Chemistry — Inorganic Chemistry

Water-based routes for synthesis of metal-organic frameworks: A review Commercialization ; Metal-organic framework ; Nanotechnology ; Aqueous medium ; Biochemical engineering ; Sustainable development ; Materials science ; Business ; Aqueous solution ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions ; Dendrimers and Hyperbranched Polymers
Authors: Colin A. Grambow | Lagnajit Pattanaik | William H. Green
Source: The Journal of Physical Chemistry Letters (article, 2020-03-27)
Field: Materials Science — Materials Chemistry

Deep Learning of Activation Energies Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict the activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and the development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future. Leverage (statistics) ; Computer science ; Reactivity (psychology) ; Reaction mechanism ; Deep learning ; Set (abstract data type) ; Biochemical engineering ; Artificial intelligence ; Chemistry ; Biological system ; Organic chemistry ; Catalysis ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Malik Wagih | Peter Mahler Larsen | Christopher A. Schuh
Source: Nature Communications (article, 2020-12-11)
Field: Materials Science — Materials Chemistry

Learning grain boundary segregation energy spectra in polycrystals The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency-quantified by the segregation enthalpy spectrum-of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation. Grain boundary ; Embrittlement ; Materials science ; Alloy ; Spectral line ; Space (punctuation) ; Microstructure ; Range (aeronautics) ; Chemical physics ; Computer science ; Metallurgy ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Alexander Howarth | Kristaps Ermanis | Jonathan M. Goodman
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Spectroscopy

DP4-AI automated NMR data analysis: straight from spectrometer to structure A robust system for automatic processing and assignment of raw <sup>13</sup>C and <sup>1</sup>H NMR data DP4-AI has been developed and integrated into our computational organic molecule structure elucidation workflow. Starting from a molecular structure with undefined stereochemistry or other structural uncertainty, this system allows for completely automated structure elucidation. Methods for NMR peak picking using objective model selection and algorithms for matching the calculated <sup>13</sup>C and <sup>1</sup>H NMR shifts to peaks in noisy experimental NMR data were developed. DP4-AI achieved a 60-fold increase in processing speed, and near-elimination of the need for scientist time, when rigorously evaluated using a challenging test set of molecules. DP4-AI represents a leap forward in NMR structure elucidation and a step-change in the functionality of DP4. It enables high-throughput analyses of databases and large sets of molecules, which were previously impossible, and paves the way for the discovery of new structural information through machine learning. This new functionality has been coupled with an intuitive GUI and is available as open-source software at https://github.com/KristapsE/DP4-AI. Spectrometer ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Physics ; Optics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Advanced NMR Techniques and Applications
Authors: Bo Wen | Wen‐Feng Zeng | Yuxing Liao | Zhiao Shi | Sara R. Savage | Wen Jiang | Bing Zhang
Source: PROTEOMICS (review, 2020-09-16)
Field: Chemistry — Spectroscopy

Deep Learning in Proteomics Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data. Proteomics ; Computer science ; Deep learning ; Computational biology ; Proteogenomics ; Artificial intelligence ; Data science ; Genomics ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Hong Ma | Yiqing Zhao | Wuzhe Huang | Lizhan Zhang | Fuqing Wu | Jianwen Ye | Guo‐Qiang Chen
Source: Nature Communications (article, 2020-07-03)
Field: Materials Science — Biomaterials

Rational flux-tuning of Halomonas bluephagenesis for co-production of bioplastic PHB and ectoine Ectoine, a compatible solute synthesized by many halophiles for hypersalinity resistance, has been successfully produced by metabolically engineered Halomonas bluephagenesis, which is a bioplastic poly(3-hydroxybutyrate) producer allowing open unsterile and continuous conditions. Here we report a de novo synthesis pathway for ectoine constructed into the chromosome of H. bluephagenesis utilizing two inducible systems, which serve to fine-tune the transcription levels of three clusters related to ectoine synthesis, including ectABC, lysC and asd based on a GFP-mediated transcriptional tuning approach. Combined with bypasses deletion, the resulting recombinant H. bluephagenesis TD-ADEL-58 is able to produce 28 g L<sup>-1</sup> ectoine during a 28 h fed-batch growth process. Co-production of ectoine and PHB is achieved to 8 g L<sup>-1</sup> ectoine and 32 g L<sup>-1</sup> dry cell mass containing 75% PHB after a 44 h growth. H. bluephagenesis demonstrates to be a suitable co-production chassis for polyhydroxyalkanoates and non-polymer chemicals such as ectoine. Ectoine ; Halomonas ; Polyhydroxyalkanoates ; Bioplastic ; Polyhydroxybutyrate ; Halophile ; Biochemistry ; Chemistry ; Biology ; Osmoprotectant ; Bacteria ; Genetics ; Amino acid biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Microbial Metabolic Engineering and Bioproduction