jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bakytzhan Yeskendir | Jean‐Philippe Dacquin | Yannick Lorgouilloux | Christian Courtois | Sébastien Royer | Jérémy Dhainaut
Source: Materials Advances (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

From metal–organic framework powders to shaped solids: recent developments and challenges This review discusses the impact of shaping techniques on the physico-chemical properties of metal–organic frameworks. Metal-organic framework ; Materials science ; Metal ; Nanotechnology ; Environmental science ; Metallurgy ; Chemistry ; Physical chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Dendrimers and Hyperbranched Polymers
Authors: Carla Verdi | Ferenc Karsai | Peitao Liu | Ryosuke Jinnouchi | Georg Kresse
Source: npj Computational Materials (article, 2021-09-30)
Field: Materials Science — Materials Chemistry

Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials Abstract Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and, thus, shape their technological applications. Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zirconia, an important transition metal oxide. This machine-learned potential accurately captures the temperature-induced phase transitions below the melting point. We further showcase the predictive power of the potential by calculating the heat transport on the basis of Green–Kubo theory, which allows to account for anharmonic effects to all orders. This study indicates that machine-learned potentials trained on the fly offer a routine solution for accurate and efficient simulations of the thermodynamic properties of a vast class of anharmonic materials. Anharmonicity ; Interatomic potential ; Phase transition ; Statistical physics ; Lattice (music) ; Thermodynamic integration ; Molecular dynamics ; Thermal conductivity ; Computer science ; Materials science ; Thermodynamics ; Physics ; Condensed matter physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Joël Gautron | Lilian Stapane | Nathalie Le Roy | Yves Y. Nys | Alejandro B. Rodríguez‐Navarro | Maxwell T. Hincke
Source: BMC Molecular and Cell Biology (review, 2021-02-12)
Field: Materials Science — Biomaterials

Avian eggshell biomineralization: an update on its structure, mineralogy and protein tool kit With this large dataset at hand, it should now be possible to determine specific motifs, domains or proteins and peptide sequences that perform a critical function during avian eggshell biomineralization. The integration of this insight with genomic data (non-synonymous single nucleotide polymorphisms) and precise phenotyping (shell biomechanical parameters) on pure selected lines will lead to consistently better-quality eggshell characteristics for improved food safety. This information will also address the question of how the evolutionary-optimized chicken eggshell matrix proteins affect and regulate calcium carbonate mineralization as a good example of biomimetic and bio-inspired material design. Biomineralization ; Eggshell ; Geology ; Mineralogy ; Chemistry ; Paleontology Calcium Carbonate Crystallization and Inhibition ; Animal Nutrition and Physiology ; Bone and Dental Protein Studies
Authors: Matteo Mori | Zhongge Zhang | Amir Banaei‐Esfahani | Jean‐Benoît Lalanne | Hiroyuki Okano | Ben C. Collins | Alexander Schmidt | Olga T. Schubert | Deok‐Sun Lee | Gene‐Wei Li | Ruedi Aebersold | Terence Hwa | Christina Ludwig
Source: Molecular Systems Biology (article, 2021-05-01)
Field: Chemistry — Spectroscopy

From coarse to fine: the absolute <i>Escherichia coli</i> proteome under diverse growth conditions Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data-independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in for , Escherichia coli > 2 000 proteins over growth conditions, including nutrient limita-> 60 tions, non-metabolic stresses, and non-planktonic states. The resulting high-quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low-abundant proteins under various metabolic limitations, the non-specificity of catabolic enzymes upregulated under carbon limitation, the lack of largescale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain-dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi-omics studies of gene regulation and metabolic control in . Biology ; Proteome ; Escherichia coli ; Computational biology ; Microbiology ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Bacterial Genetics and Biotechnology
Authors: Florian Häse | Matteo Aldeghi | Riley J. Hickman | Loïc M. Roch | Alán Aspuru-Guzik
Source: Applied Physics Reviews (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

G<scp>ryffin</scp>: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge Designing functional molecules and advanced materials requires complex design choices: tuning continuous process parameters such as temperatures or flow rates, while simultaneously selecting catalysts or solvents. To date, the development of data-driven experiment planning strategies for autonomous experimentation has largely focused on continuous process parameters, despite the urge to devise efficient strategies for the selection of categorical variables. Here, we introduce Gryffin, a general-purpose optimization framework for the autonomous selection of categorical variables driven by expert knowledge. Gryffin augments Bayesian optimization based on kernel density estimation with smooth approximations to categorical distributions. Leveraging domain knowledge in the form of physicochemical descriptors, Gryffin can significantly accelerate the search for promising molecules and materials. Gryffin can further highlight relevant correlations between the provided descriptors to inspire physical insights and foster scientific intuition. In addition to comprehensive benchmarks, we demonstrate the capabilities and performance of Gryffin on three examples in materials science and chemistry: (i) the discovery of non-fullerene acceptors for organic solar cells, (ii) the design of hybrid organic–inorganic perovskites for light-harvesting, and (iii) the identification of ligands and process parameters for Suzuki–Miyaura reactions. Our results suggest that Gryffin, in its simplest form, is competitive with state-of-the-art categorical optimization algorithms. However, when leveraging domain knowledge provided via descriptors, Gryffin outperforms other approaches while simultaneously refining this domain knowledge to promote scientific understanding. Categorical variable ; Bayesian optimization ; Domain (mathematical analysis) ; Process (computing) ; Kernel (algebra) ; Domain knowledge ; Bayesian probability ; Identification (biology) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Perovskite Materials and Applications
Authors: Marcus H. Reis | Filipp Gusev | Nicholas G. Taylor | Sang Hun Chung | Matthew Verber | Yueh Z. Lee | Olexandr Isayev | Frank A. Leibfarth
Source: Journal of the American Chemical Society (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Machine-Learning-Guided Discovery of <sup>19</sup> F MRI Agents Enabled by Automated Copolymer Synthesis Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of <sup>19</sup>F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials. Copolymer ; Context (archaeology) ; Chemistry ; Chemical space ; Polymer ; Monomer ; Characterization (materials science) ; Software ; Computer science ; Nanotechnology ; Artificial intelligence ; Algorithm ; Drug discovery ; Materials science ; Organic chemistry ; Programming language Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Rola Houhou | Thomas Bocklitz
Source: Analytical Science Advances (review, 2021-02-02)
Field: Chemistry — Analytical Chemistry

Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data Artificial intelligence-based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI-based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed. Chemometrics ; Computer science ; Artificial intelligence ; Preprocessor ; Data pre-processing ; Machine learning ; Sample (material) ; Data mining ; Pattern recognition (psychology) ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Vishu Gupta | Kamal Choudhary | Francesca Tavazza | Carelyn E. Campbell | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Nature Communications (article, 2021-11-15)
Field: Materials Science — Materials Chemistry

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science. Unavailability ; Computer science ; Transfer of learning ; Deep learning ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Scratch ; Predictive analytics ; Analytics ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Stéṕhane Gin | Jean‐Marc Delaye | Frédéric Angeli | Sophie Schuller
Source: npj Materials Degradation (article, 2021-08-13)
Field: Materials Science — Ceramics and Composites

Aqueous alteration of silicate glass: state of knowledge and perspectives Abstract The question of silicate glass chemical durability is at the heart of many industrial and environmental issues, with certain glasses, such as bioglasses, needing to transform rapidly, while others, like nuclear glasses, extremely slowly. Due to the wide diversity of the chemical composition for these types of materials and their metastability—no thermodynamic equilibrium can be reached between glass and solution—the evaluation of chemical durability remains a scientific challenge. In this article, we review the current state of knowledge on glass alteration mechanisms and kinetics, and point to some perspectives for glasses for which no direct experimental validation is currently possible. Thanks to the development of novel techniques and international collaborations, a better understanding of the mechanisms involved has been achieved. Mechanistic models have been developed at some specific scales, although holistic models still need further development to link the various scales and perform reliable predictions. Durability ; Silicate glass ; Silicate ; Metastability ; Biochemical engineering ; Aqueous solution ; Materials science ; Process engineering ; Computer science ; Chemistry ; Mineralogy ; Chemical engineering ; Engineering ; Physical chemistry ; Composite material Glass properties and applications ; Luminescence Properties of Advanced Materials ; Pigment Synthesis and Properties
Authors: Mudasir Ahmad | Junjie Chen | Ke Yang | Tariq Shah | Mehraj‐ud‐din Naik | Qiuyu Zhang | Baoliang Zhang
Source: Chemical Engineering Journal (article, 2021-03-18)
Field: Chemistry — Inorganic Chemistry

Preparation of amidoxime modified porous organic polymer flowers for selective uranium recovery from seawater Seawater ; Adsorption ; Uranium ; Chemistry ; Acrylonitrile ; Chemical engineering ; Polymer ; Inorganic chemistry ; Organic chemistry ; Materials science ; Metallurgy ; Copolymer Radioactive element chemistry and processing ; Covalent Organic Framework Applications ; Chemical Synthesis and Characterization
Authors: Jelena Čuklina | Chloe H. Lee | Evan G. Williams | Tatjana Sajic | Ben C. Collins | María Rodríguez Martínez | Varun Sharma | Fabian Wendt | Sandra Goetze | Gregory R. Keele | Bernd Wollscheid | Ruedi Aebersold | Patrick G. A. Pedrioli
Source: Molecular Systems Biology (review, 2021-08-01)
Field: Chemistry — Spectroscopy

Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Biology ; Computational biology ; Scale (ratio) ; Physics Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Advanced Biosensing Techniques and Applications
Authors: Weiqi Ji | Sili Deng
Source: The Journal of Physical Chemistry A (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network Chemical reactions occur in energy, environmental, biological, and many other natural systems, and the inference of the reaction networks is essential to understand and design the chemical processes in engineering and life sciences. Yet, revealing the reaction pathways for complex systems and processes is still challenging because of the lack of knowledge of the involved species and reactions. Here, we present a neural network approach that autonomously discovers reaction pathways from the time-resolved species concentration data. The proposed chemical reaction neural network (CRNN), by design, satisfies the fundamental physics laws, including the law of mass action and the Arrhenius law. Consequently, the CRNN is physically interpretable such that the reaction pathways can be interpreted, and the kinetic parameters can be quantified simultaneously from the weights of the neural network. The inference of the chemical pathways is accomplished by training the CRNN with species concentration data via stochastic gradient descent. We demonstrate the successful implementations and the robustness of the approach in elucidating the chemical reaction pathways of several chemical engineering and biochemical systems. The autonomous inference by the CRNN approach precludes the need for expert knowledge in proposing candidate networks and addresses the curse of dimensionality in complex systems. The physical interpretability also makes the CRNN capable of not only fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems. Interpretability ; Computer science ; Artificial neural network ; Inference ; Artificial intelligence ; Robustness (evolution) ; Biochemical engineering ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Natalie S. Eyke | Brent A. Koscher | Klavs F. Jensen
Source: Trends in Chemistry (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Toward Machine Learning-Enhanced High-Throughput Experimentation Throughput ; Computer science ; Artificial intelligence ; Machine learning ; Operating system Machine Learning in Materials Science ; Machine Learning and Data Classification ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Mohsen Fallah Vostakola | Bahman Amini Horri
Source: Energies (review, 2021-02-26)
Field: Materials Science — Materials Chemistry

Progress in Material Development for Low-Temperature Solid Oxide Fuel Cells: A Review Solid oxide fuel cells (SOFCs) have been considered as promising candidates to tackle the need for sustainable and efficient energy conversion devices. However, the current operating temperature of SOFCs poses critical challenges relating to the costs of fabrication and materials selection. To overcome these issues, many attempts have been made by the SOFC research and manufacturing communities for lowering the operating temperature to intermediate ranges (600–800 °C) and even lower temperatures (below 600 °C). Despite the interesting success and technical advantages obtained with the low-temperature SOFC, on the other hand, the cell operation at low temperature could noticeably increase the electrolyte ohmic loss and the polarization losses of the electrode that cause a decrease in the overall cell performance and energy conversion efficiency. In addition, the electrolyte ionic conductivity exponentially decreases with a decrease in operating temperature based on the Arrhenius conduction equation for semiconductors. To address these challenges, a variety of materials and fabrication methods have been developed in the past few years which are the subject of this critical review. Therefore, this paper focuses on the recent advances in the development of new low-temperature SOFCs materials, especially low-temperature electrolytes and electrodes with improved electrochemical properties, as well as summarizing the matching current collectors and sealants for the low-temperature region. Different strategies for improving the cell efficiency, the impact of operating variables on the performance of SOFCs, and the available choice of stack designs, as well as the costing factors, operational limits, and performance prospects, have been briefly summarized in this work. Solid oxide fuel cell ; Operating temperature ; Materials science ; Electrolyte ; Fabrication ; Engineering physics ; Oxide ; Ohmic contact ; Polarization (electrochemistry) ; Process engineering ; Nanotechnology ; Electrode ; Electrical engineering ; Engineering ; Chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials