jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Janis Timoshenko | Anatoly I. Frenkel
Source: ACS Catalysis (article, 2019-09-27)
Field: Materials Science — Materials Chemistry

“Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors The rapid growth of methods emerging in the past decade for synthesis of “designer” catalysts—ranging from the size and shape-selected nanoparticles to mass-selected clusters, to precisely engineered bimetallic surfaces, to single site and pair site catalysts—has opened opportunities for tailoring the catalyst structure for the desired activity and selectivity. It has also sharpened the need for developing approaches to the operando characterization, ones that identify the catalytic active sites and follow their evolutions in reaction conditions. Commonly used methods for determination of the activity descriptors in the nanocatalysts, based on the correlation between the changes in catalyst performance and evolution of its structural and electronic properties, are hampered by the paucity of experimental techniques that can detect such properties with high accuracy and in reaction conditions. Out of many such techniques, X-ray absorption spectroscopy (XAS) stands out as an element-specific method that is very sensitive to the local geometric and electronic properties of the metal atoms and their surroundings and, therefore, is able to track catalyst structure modifications in operando conditions. Despite the vast amount of structure-specific information (such as, e.g., the charge states and radial distribution function of neighbors of selected atomic species) stored in the XAS data of catalysts, extracting it from the spectra is challenging, especially in the conditions of low metal weight loading, nanoscale dimensions, heterogeneous size and composition distributions, and harsh reaction environment. In this Perspective, we discuss the recent developments in XAS data analysis achieved by employing supervised and unsupervised machine learning (ML) methods for structural characterization of catalysts. By benefiting from the sensitivity of ML methods to subtle variations in experimental data, a previously “hidden” relationship between the X-ray absorption spectrum and descriptors of material’s structure and/or composition can be found, as illustrated on representative examples of mono-, hetero-, and nonmetallic catalysts. In the case of supervised ML, the experimental spectra can be rapidly “inverted”, and the structure of the catalyst can be tracked in real time and in reaction conditions. Emerging opportunities for catalysis research that the ML methods enable, such as high-throughput data analysis, and their applications to other experimental probes of catalyst structure are discussed. X-ray absorption spectroscopy ; Nanomaterial-based catalyst ; Catalysis ; Bimetallic strip ; Absorption spectroscopy ; Characterization (materials science) ; Materials science ; Nanotechnology ; Chemistry ; Chemical physics ; Physics ; Optics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Kamal Choudhary | Francesca Tavazza
Source: Computational Materials Science (article, 2019-02-16)
Field: Materials Science — Materials Chemistry

Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations Pseudopotential ; Convergence (economics) ; Plane wave ; Cutoff ; Density functional theory ; Algorithm ; Atom (system on chip) ; Plane (geometry) ; Computer science ; Computational physics ; Physics ; Mathematics ; Geometry ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Yao Zhang | Alpha A. Lee
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning Predicting bioactivity and physical properties of small molecules is a central challenge in drug discovery. Deep learning is becoming the method of choice but studies to date focus on mean accuracy as the main metric. However, to replace costly and mission-critical experiments by models, a high mean accuracy is not enough: outliers can derail a discovery campaign, thus models need to reliably predict when it will fail, even when the training data is biased; experiments are expensive, thus models need to be data-efficient and suggest informative training sets using active learning. We show that uncertainty quantification and active learning can be achieved by Bayesian semi-supervised graph convolutional neural networks. The Bayesian approach estimates uncertainty in a statistically principled way through sampling from the posterior distribution. Semi-supervised learning disentangles representation learning and regression, keeping uncertainty estimates accurate in the low data limit and allowing the model to start active learning from a small initial pool of training data. Our study highlights the promise of Bayesian deep learning for chemistry. Machine learning ; Artificial intelligence ; Active learning (machine learning) ; Bayesian probability ; Computer science ; Uncertainty quantification ; Bayesian inference Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kübra Karayağız | Luke Johnson | Raiyan Seede | Vahid Attari | Bing Zhang | Xueqin Huang | Supriyo Ghosh | Thien Duong | İbrahim Karaman | Alaa Elwany | Raymundo Arróyave
Source: Acta Materialia (article, 2019-12-04)
Field: Materials Science — Materials Chemistry

Finite interface dissipation phase field modeling of Ni–Nb under additive manufacturing conditions Materials science ; Dissipation ; Interface (matter) ; Field (mathematics) ; Phase (matter) ; Metallurgy ; Phase field models ; Composite material ; Thermodynamics Solidification and crystal growth phenomena ; High Temperature Alloys and Creep ; Intermetallics and Advanced Alloy Properties
Authors: Xuan Lin | Zhe Quan | Zhi-Jie Wang | Huang Huang | Xiangxiang Zeng
Source: Briefings in Bioinformatics (article, 2019-09-11)
Field: Materials Science — Materials Chemistry

A novel molecular representation with BiGRU neural networks for learning atom Molecular representations play critical roles in researching drug design and properties, and effective methods are beneficial to assisting in the calculation of molecules and solving related problem in drug discovery. In previous years, most of the traditional molecular representations are based on hand-crafted features and rely heavily on biological experimentations, which are often costly and time consuming. However, recent researches achieve promising results using machine learning on various domains. In this article, we present a novel method named Smi2Vec-BiGRU that is designed for learning atoms and solving the single- and multitask binary classification problems in the field of drug discovery, which are the basic and also key problems in this field. Specifically, our approach transforms the molecule data in the SMILES format into a set of sample vectors and then feeds them into the bidirectional gated recurrent unit neural networks for training, which learns low-dimensional vector representations for molecular drug. We conduct extensive experiments on several widely used benchmarks including Tox21, SIDER and ClinTox. The experimental results show that our approach can achieve state-of-the-art performance on these benchmarking datasets, demonstrating the feasibility and competitiveness of our proposed approach. Representation (politics) ; Computer science ; Artificial intelligence ; Artificial neural network ; Atom (system on chip) ; Pattern recognition (psychology) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Sicong Ma | Sida Huang | Zhi‐Pan Liu
Source: Nature Catalysis (article, 2019-06-17)
Field: Materials Science — Materials Chemistry

Dynamic coordination of cations and catalytic selectivity on zinc–chromium oxide alloys during syngas conversion Syngas ; Spinel ; Selectivity ; Catalysis ; Oxide ; Zinc ; Chromium ; Ternary operation ; Inorganic chemistry ; Metal ; Materials science ; Alloy ; Chemical engineering ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Hana Dureckova | Mykhaylo Krykunov | Mohammad Zein Aghaji | Tom K. Woo
Source: The Journal of Physical Chemistry C (article, 2019-01-18)
Field: Chemistry — Inorganic Chemistry

Robust Machine Learning Models for Predicting High CO<sub>2</sub> Working Capacity and CO<sub>2</sub>/H<sub>2</sub> Selectivity of Gas Adsorption in Metal Organic Frameworks for Precombustion Carbon Capture This work is devoted to the development of quantitative structure–property relationship (QSPR) models using machine learning to predict CO2 working capacity and CO2/H2 selectivity for precombustion carbon capture using a topologically diverse database of hypothetical metal–organic framework (MOF) structures (358 400 MOFs, 1166 network topologies). Such a diversity of the networks topology is much higher than previously used (<20 network topologies) for rapid and accurate recognition of high-performing MOFs for other gas-separation applications. The gradient boosted trees regression method allowed us to use 80% of the database as a training set, while the rest was used for the validation and test set. The QSPR models are first built using purely geometric descriptors of MOFs such as gravimetric surface area and void fraction. Additional models which account for chemical features of MOFs are constructed using atomic property weighted radial distribution functions (AP-RDFs) with a novel normalization to accommodate the size diversity of the MOF database. It is shown that the best models for CO2 working capacities (R2 = 0.944) and CO2/H2 selectivities (R2 = 0.872) are built from a combination of six geometric descriptors and three AP-RDF descriptors. However, more important is that our QSPR models can identify top 1000 high-performing MOFs in just top 3000 or 5000 MOFs. This work shows that QSPR modeling can account for the topological diversity of MOFs and accelerate the screening for top-performing MOFs for precombustion carbon capture. Quantitative structure–activity relationship ; Metal-organic framework ; Molecular descriptor ; Test set ; Computer science ; Network topology ; Machine learning ; Normalization (sociology) ; Adsorption ; Artificial intelligence ; Data mining ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Jérôme Chevalier | Aléthea Liens | Helen Reverón | Fei Zhang | Pascal Reynaud | Thierry Douillard | Laura Preiss | Valter Sergo | Vanni Lughi | Michael Swain | Nicolas Courtois
Source: Journal of the American Ceramic Society (article, 2019-11-11)
Field: Materials Science — Ceramics and Composites

Forty years after the promise of «ceramic steel?»: Zirconia‐based composites with a metal‐like mechanical behavior Abstract Forty years ago, Garvie and his Australian co‐workers reported that the stress‐induced transformation of metastable tetragonal zirconia grains to the monoclinic symmetry could give rise to a powerful toughening mechanism. Their results even led them to consider zirconia systems as analogues of certain steels. This seminal paper generated extraordinary excitement in the ceramic community and it is still the subject of extensive research. Transformation toughening is widely used in zirconia materials and results in an increase in strength and toughness when compared to nontransformable ceramics, but the implementation into strong, tough, and sufficiently stable materials has not been fully reached. Zirconia ceramics generally fail at low strains with a much larger scatter in the strength values than metals and require statistical approaches to failure. Here we describe in detail the mechanical behavior laws of ceria‐doped zirconia composites exhibiting a high degree of stress‐induced transformation. They present, to some extent, mechanical behavior analogous to a metal, displaying, (a) significant amount of transformation‐induced plasticity without damage, (b) very high flaw tolerance and (c) almost no dispersion in strength data. They potentially open new application avenues in situations where the advantages of ceramics were dampened by their brittle failure behavior. In particular, the consequences of such behavior for dental implants and additive‐manufactured structures are highlighted. Materials science ; Cubic zirconia ; Ceramic ; Toughening ; Brittleness ; Composite material ; Toughness ; Fracture toughness ; Tetragonal crystal system ; Stress (linguistics) ; Deformation (meteorology) ; Plasticity ; Phase (matter) Advanced ceramic materials synthesis ; Bone Tissue Engineering Materials ; Advanced materials and composites
Authors: Lihua Chen | Tran Doan Huan | Rohit Batra | Chiho Kim | Rampi Ramprasad
Source: Computational Materials Science (article, 2019-08-02)
Field: Materials Science — Materials Chemistry

Machine learning models for the lattice thermal conductivity prediction of inorganic materials Thermal conductivity ; Empirical modelling ; Computer science ; Thermoelectric materials ; Lattice (music) ; Thermal ; Formalism (music) ; Gaussian process ; Materials science ; Machine learning ; Semiconductor ; Statistical physics ; Artificial intelligence ; Gaussian ; Algorithm ; Thermodynamics ; Physics ; Simulation ; Chemistry ; Computational chemistry ; Composite material Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Zebin Su | Chih‐Hao Hsu | Zihao Gong | Xueyan Feng | Jiahao Huang | Ruimeng Zhang | Yu Wang | Jialin Mao | Chrys Wesdemiotis | Tao Li | Söenke Seifert | Wei Zhang | Takuzo Aida | Mingjun Huang | Stephen Z. D. Cheng
Source: Nature Chemistry (article, 2019-09-23)
Field: Materials Science — Biomaterials

Identification of a Frank–Kasper Z phase from shape amphiphile self-assembly Chemistry ; Silsesquioxane ; Phase (matter) ; Crystallography ; Soft matter ; Self-assembly ; Amphiphile ; Chemical physics ; Nanotechnology ; Colloid ; Copolymer ; Materials science ; Polymer ; Organic chemistry Supramolecular Self-Assembly in Materials ; Mesoporous Materials and Catalysis ; Pickering emulsions and particle stabilization
Authors: Yuan Liu | Yanli Zhao | Xiaohong Chen
Source: Theranostics (review, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Bioengineering of Metal-organic Frameworks for Nanomedicine Controlled structure, tunable porosity, and readily chemical functionalizability make metal-organic frameworks (MOFs) a powerful biomedical tool. Nanoscale MOF particles have been increasingly studied as drug carriers, bioimaging agents, and therapeutic agents due to their excellent physiochemical properties. In this review, we start with MOF as a nanocarrier for drug delivery, covering therapeutic MOF agents followed by a comprehensive discussion of surface bioengineering of MOF for improved biostability, biocompatibility, and targeted delivery. Finally, we detail the challenges and prospects of the future of MOF research for biomedical applications. Nanomedicine ; Nanocarriers ; Nanotechnology ; Drug delivery ; Biocompatibility ; Metal-organic framework ; Materials science ; Nanoparticle ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics
Authors: Konstantinos Konstantinou | Felix C. Mocanu | Tae-Hoon Lee | Stephen R. Elliott
Source: Nature Communications (article, 2019-07-11)
Field: Materials Science — Materials Chemistry

Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5 Understanding the relation between the time-dependent resistance drift in the amorphous state of phase-change materials and the localised states in the band gap of the glass is crucial for the development of memory devices with increased storage density. Here a machine-learned interatomic potential is utilised to generate an ensemble of glass models of the prototypical phase-change alloy, Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>, to obtain reliable statistics. Hybrid density-functional theory is used to identify and characterise the geometric and electronic structures of the mid-gap states. 5-coordinated Ge atoms are the local defective bonding environments mainly responsible for these electronic states. The structural motif for the localisation of the mid-gap states is a crystalline-like atomic environment within the amorphous network. An extra electron is trapped spontaneously by these mid-gap states, creating deep traps in the band gap. The results provide significant insights that can help to rationalise the design of multi-level-storage memory devices. Amorphous solid ; Band gap ; Materials science ; Phase-change memory ; Density of states ; Phase (matter) ; Electron ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Optoelectronics ; Physics ; Chemistry ; Crystallography ; Quantum mechanics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Studies
Authors: Rafal Sliz | Marc Lejay | James Z. Fan | Min‐Jae Choi | Sachin Kinge | Sjoerd Hoogland | Tapio Fabritius | F. Pelayo Garcı́a de Arquer | Edward H. Sargent
Source: ACS Nano (article, 2019-09-23)
Field: Materials Science — Materials Chemistry

Stable Colloidal Quantum Dot Inks Enable Inkjet-Printed High-Sensitivity Infrared Photodetectors Colloidal quantum dots (CQDs) have recently gained attention as materials for manufacturing optoelectronic devices in view of their tunable light absorption and emission properties and compatibility with low-temperature thin-film manufacture. The realization of CQD inkjet-printed infrared photodetectors has thus far been hindered by incompatibility between the chemical processes that produce state-of-the-art CQD solution-exchanged inks and the requirements of ink formulations for inkjet materials processing. To achieve inkjet-printed CQD solids with a high degree of reproducibility, as well as with the needed morphological and optoelectronic characteristics, we sought to overcome the mismatch among these processing conditions. In this study, we design CQD inks by simultaneous evaluation of requirements regarding ink colloidal stability, jetting conditions, and film morphology for different dots and solvents. The new inks remain colloidally stable, achieved through a design that suppresses the reductant properties of amines on the dots' surface. After drop ejection from the nozzle, the quantum dot material is immobilized on the substrate surface due to the rapid evaporation of the low boiling point amine-based compound. Concurrently, the high boiling point solvent allows for the formation of a thin film of high uniformity, as is required for the fabrication of high-performance IR photodetectors. We fabricate inkjet-printed photodetectors exhibiting the highest specific detectivities reported to date (above 10<sup>12</sup> Jones across the IR) in an inkjet-printed quantum dot film. As a patternable CMOS-compatible process, the work offers routes to integrated sensing devices and systems. Materials science ; Quantum dot ; Photodetector ; Fabrication ; Nanotechnology ; Inkwell ; Optoelectronics ; Thin film ; Inkjet printing ; Microfluidics ; Nanoparticle ; Composite material Quantum Dots Synthesis And Properties ; Perovskite Materials and Applications ; ZnO doping and properties
Authors: André Boltjes | Alexander Dömlingꝉ
Source: European Journal of Organic Chemistry (article, 2019-08-30)
Field: Chemistry — Organic Chemistry

The Groebke‐Blackburn‐Bienaymé Reaction Imidazo[1,2-<i>a</i>]pyridine is a well-known scaffold in many marketed drugs, such as Zolpidem, Minodronic acid, Miroprofen and DS-1 and it also serves as a broadly applied pharmacophore in drug discovery. The scaffold revoked a wave of interest when Groebke, Blackburn and Bienaymé reported independently a new three component reaction resulting in compounds with the imidazo[1,2-<i>a</i>]-heterocycles as a core structure. During the course of two decades the Groebke Blackburn Bienaymé (GBB-3CR) reaction has emerged as a very important multicomponent reaction (MCR), resulting in over a hundred patents and a great number of publications in various fields of interest. Now two compounds derived from GBB-3CR chemistry received FDA approval. To celebrate the first 20 years of GBB-chemistry, we present an overview of the chemistry of the GBB-3CR, including an analysis of each of the three starting material classes, solvents and catalysts. Additionally, a list of patents and their applications and a more in-depth summary of the biological targets that were addressed, including structural biology analysis, is given. Chemistry ; Pharmacophore ; Drug discovery ; Combinatorial chemistry ; Stereochemistry ; Biochemistry Multicomponent Synthesis of Heterocycles ; Synthesis and Reactivity of Heterocycles ; Synthesis and biological activity
Authors: Seungho Lim | Jooho Park | Man Kyu Shim | Wooram Um | Hong Yeol Yoon | Ju Hee Ryu | Dong‐Kwon Lim | Kwangmeyung Kim
Source: Theranostics (review, 2019-01-01)
Field: Materials Science — Biomaterials

Recent advances and challenges of repurposing nanoparticle-based drug delivery systems to enhance cancer immunotherapy Cancer immunotherapy is an attractive treatment option under clinical settings. However, the major challenges of immunotherapy include limited patient response, limited tumor specificity, immune-related adverse events, and immunosuppressive tumor microenvironment. Therefore, nanoparticle (NP)-based drug delivery has been used to not only increase the efficacy of immunotherapeutic agents, but it also significantly reduces the toxicity. In particular, NP-based drug delivery systems alter the pharmacokinetic (PK) profile of encapsulated or conjugated immunotherapeutic agents to targeted cancer cells or immune cells and facilitate the delivery of multiple therapeutic combinations to targeted cells using single NPs. Recently, advanced NP-based drug delivery systems were effectively utilized in cancer immunotherapy to reduce the toxic side effects and immune-related adverse events. Repurposing these NPs as delivery systems of immunotherapeutic agents may overcome the limitations of current cancer immunotherapy. In this review, we focus on recent advances in NP-based immunotherapeutic delivery systems, such as immunogenic cell death (ICD)-inducing drugs, cytokines and adjuvants for promising cancer immunotherapy. Finally, we discuss the challenges facing current NP-based drug delivery systems that need to be addressed for successful clinical application. Immunotherapy ; Cancer immunotherapy ; Medicine ; Drug delivery ; Immune system ; Cancer ; Tumor microenvironment ; Cancer research ; Pharmacology ; Immunology ; Nanotechnology ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Cancer Immunotherapy and Biomarkers