jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryther Anderson | Jacob Rodgers | Edwin Argueta | Achay Biong | Diego A. Gómez‐Gualdrón
Source: Chemistry of Materials (article, 2018-08-23)
Field: Chemistry — Inorganic Chemistry

Role of Pore Chemistry and Topology in the CO<sub>2</sub> Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning Open framework materials (OFMs) such as metal–organic frameworks (MOFs) can provide structurally and chemically tailorable nanopores. This exceptional tunability has allowed for careful positioning of optimal adsorption sites within MOF pores to enable selective CO2 physisorption, making these materials promising for energy-efficient CO2 capture. However, given the multitude of features that can be simultaneously altered within the thousands of MOFs synthesized to date, it can be daunting to elucidate the most critical features for boosting CO2 capture capabilities. Here we use a multiscale approach—density functional theory (DFT), grand canonical Monte Carlo (GCMC), and machine learning (ML)—to investigate the role of various pore chemical and topological features in the enhancement of CO2 capture metrics of MOFs. To enable a thorough “sweep” of a target region of MOF structure-space, we used computational synthesis methods to create sets of MOFs encompassing all possible combinations of 16 topologies and 13 functionalized molecular building blocks. The adsorption of pure CO2, and CO2/H2 and CO2/N2 mixtures for the resulting 31 parent MOFs and its derivatives was then simulated, and CO2 capture metrics were calculated. Functionalization with hydroxyl, thiol, cyano, amino, or nitro chemistries was found to often improve CO2 capture metrics of the parent MOFs, but the efficacy of this strategy depended strongly on the pore topology. Decision trees were trained to predict the improvement or decline of CO2 capture metrics upon functionalization of parent MOFs, whereas five additional machine learning algorithms were trained to predict absolute metrics for all MOFs. The training of these algorithms allowed us to determine, without human bias, the relative importance of various pore chemical and structural/topological factors on the CO2 capture capabilities of MOFs. Physisorption ; Chemical space ; Adsorption ; Materials science ; Nanotechnology ; Surface modification ; Topology (electrical circuits) ; Metal-organic framework ; Computer science ; Machine learning ; Chemistry ; Organic chemistry ; Mathematics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Carbon Dioxide Capture Technologies
Authors: Houliang Tang | Weilong Zhao | Jinming Yu | Yang Li | Chao Zhao
Source: Molecules (review, 2018-12-20)
Field: Materials Science — Biomaterials

Recent Development of pH-Responsive Polymers for Cancer Nanomedicine Cancer remains a leading cause of death worldwide with more than 10 million new cases every year. Tumor-targeted nanomedicines have shown substantial improvements of the therapeutic index of anticancer agents, addressing the deficiencies of conventional chemotherapy, and have had a tremendous growth over past several decades. Due to the pathophysiological characteristics that almost all tumor tissues have lower pH in comparison to normal healthy tissues, among various tumor-targeted nanomaterials, pH-responsive polymeric materials have been one of the most prevalent approaches for cancer diagnosis and treatment. In this review, we summarized the types of pH-responsive polymers, describing their chemical structures and pH-response mechanisms; we illustrated the structure-property relationships of pH-responsive polymers and introduced the approaches to regulating their pH-responsive behaviors; we also highlighted the most representative applications of pH-responsive polymers in cancer imaging and therapy. This review article aims to provide general guidelines for the rational design of more effective pH-responsive nanomaterials for cancer diagnosis and treatment. Nanomedicine ; Cancer ; Nanotechnology ; Cancer therapy ; Cancer imaging ; Nanomaterials ; Cancer treatment ; Polymer ; Cancer chemotherapy ; Medicine ; Cancer research ; Pharmacology ; Chemistry ; Materials science ; Nanoparticle ; Internal medicine ; Organic chemistry Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Nanoplatforms for cancer theranostics
Authors: Weike Ye | Chi Chen | Zhenbin Wang | Iek-Heng Chu | Shyue Ping Ong
Source: Nature Communications (article, 2018-09-12)
Field: Materials Science — Materials Chemistry

Deep neural networks for accurate predictions of crystal stability Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations remain comparatively expensive and scale poorly with system size. Here we show that deep neural networks utilizing just two descriptors-the Pauling electronegativity and ionic radii-can predict the DFT formation energies of C<sub>3</sub>A<sub>2</sub>D<sub>3</sub>O<sub>12</sub> garnets and ABO<sub>3</sub> perovskites with low mean absolute errors (MAEs) of 7-10 meV atom<sup>-1</sup> and 20-34 meV atom<sup>-1</sup>, respectively, well within the limits of DFT accuracy. Further extension to mixed garnets and perovskites with little loss in accuracy can be achieved using a binary encoding scheme, addressing a critical gap in the extension of machine-learning models from fixed stoichiometry crystals to infinite universe of mixed-species crystals. Finally, we demonstrate the potential of these models to rapidly transverse vast chemical spaces to accurately identify stable compositions, accelerating the discovery of novel materials with potentially superior properties. Electronegativity ; Density functional theory ; Binary number ; Stoichiometry ; Stability (learning theory) ; Crystal (programming language) ; Ionic bonding ; Artificial neural network ; Extension (predicate logic) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Luka Đorđević∞ | Francesca Arcudi | Alessandro D’Urso | Michele Cacioppo | Norberto Micali | Thomas Bürgi | Roberto Purrello | Maurizio Prato
Source: Nature Communications (article, 2018-08-20)
Field: Materials Science — Materials Chemistry

Design principles of chiral carbon nanodots help convey chirality from molecular to nanoscale level The chirality of (nano)structures is paramount in many phenomena, including biological processes, self-assembly, enantioselective reactions, and light or electron spin polarization. In the quest for new chiral materials, metallo-organic hybrids have been attractive candidates for exploiting the aforementioned scientific fields. Here, we show that chiral carbon nanoparticles, called carbon nanodots, can be readily prepared using hydrothermal microwave-assisted synthesis and easily purified. These particles, with a mean particle size around 3 nm, are highly soluble in water and display mirror-image profile both in the UV-Vis and in the infrared regions, as detected by electronic and vibrational circular dichroism, respectively. Finally, the nanoparticles are used as templates for the formation of chiral supramolecular porphyrin assemblies, showing that it is possible to use and transfer the chiral information. This simple (and effective) methodology opens up exciting opportunities for developing a variety of chiral composite materials and applications. Chirality (physics) ; Materials science ; Porphyrin ; Supramolecular chemistry ; Nanodot ; Nanotechnology ; Nanoparticle ; Circular dichroism ; Enantioselective synthesis ; Nanoscopic scale ; Metamaterial ; Nanomaterials ; Asymmetric carbon ; Circular polarization ; Photochemistry ; Optoelectronics ; Molecule ; Chemistry ; Optically active ; Catalysis ; Organic chemistry ; Physics ; Crystallography ; Symmetry breaking ; Optics ; Chiral symmetry breaking Carbon and Quantum Dots Applications ; Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials
Authors: Benjamin Meyer | Boodsarin Sawatlon | Stefan Heinen | O. Anatole von Lilienfeld | Clémence Corminbœuf
Source: Chemical Science (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates. Volcano ; Catalysis ; Coupling (piping) ; Computer science ; Artificial intelligence ; Chemistry ; Materials science ; Geology ; Geochemistry ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Complex Network Analysis Techniques
Authors: Matthew Welborn | Lixue Cheng | Thomas F. Miller
Source: Journal of Chemical Theory and Computation (article, 2018-07-24)
Field: Materials Science — Materials Chemistry

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input. The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular orbitals, and Gaussian process regression is used to predict these contributions from a feature set that is based on molecular orbital properties, such as Fock, Coulomb, and exchange matrix elements. With the aim of maximizing transferability across chemical systems and compactness of the feature set, we avoid the usual specification of ML features in terms of atom- or geometry-specific information, such atom/element-types, bond-types, or local molecular structure. ML predictions of MP2 and CCSD energies are presented for a range of systems, demonstrating that the method maintains accuracy while providing transferability both within and across chemical families; this includes predictions for molecules with atom-types and elements that are not included in the training set. The method holds promise both in its current form and as a proof-of-principle for the use of ML in the design of generalized density-matrix functionals. Basis set ; Atom (system on chip) ; Basis (linear algebra) ; Atomic orbital ; Molecular orbital ; Gaussian ; Feature (linguistics) ; Coulomb ; Set (abstract data type) ; Computer science ; Electronic structure ; Transferability ; Matrix (chemical analysis) ; Statistical physics ; Computational chemistry ; Density functional theory ; Chemistry ; Molecule ; Physics ; Machine learning ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Neetu Agrawal | Pradeep Mishra
Source: Medicinal Chemistry Research (review, 2018-02-27)
Field: Chemistry — Organic Chemistry

The synthetic and therapeutic expedition of isoxazole and its analogs Isoxazole ; Nitrogen atom ; Chemistry ; Oxygen atom ; Combinatorial chemistry ; Broad spectrum ; Computational biology ; Nanotechnology ; Stereochemistry ; Ring (chemistry) ; Biology ; Organic chemistry ; Molecule ; Materials science Synthesis and Biological Evaluation ; Click Chemistry and Applications ; Chemical Synthesis and Analysis
Authors: Christopher D. Spicer | E. Thomas Pashuck | Molly M. Stevens
Source: Chemical Reviews (review, 2018-07-24)
Field: Materials Science — Surfaces, Coatings and Films

Achieving Controlled Biomolecule–Biomaterial Conjugation The conjugation of biomolecules can impart materials with the bioactivity necessary to modulate specific cell behaviors. While the biological roles of particular polypeptide, oligonucleotide, and glycan structures have been extensively reviewed, along with the influence of attachment on material structure and function, the key role played by the conjugation strategy in determining activity is often overlooked. In this review, we focus on the chemistry of biomolecule conjugation and provide a comprehensive overview of the key strategies for achieving controlled biomaterial functionalization. No universal method exists to provide optimal attachment, and here we will discuss both the relative advantages and disadvantages of each technique. In doing so, we highlight the importance of carefully considering the impact and suitability of a particular technique during biomaterial design. Biomaterial ; Biomolecule ; Chemistry ; Nanotechnology ; Biological materials ; Function (biology) ; Biochemical engineering ; Biochemistry ; Materials science ; Organic chemistry ; Engineering Polymer Surface Interaction Studies ; Click Chemistry and Applications ; Silk-based biomaterials and applications
Authors: Linfeng Zhang | Jiequn Han | Handong Wang | Wissam A. Saidi | Roberto Car | E Weinan
Source: neural information processing systems (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. Ab initio ; Homogeneous space ; Symmetry (geometry) ; Differentiable function ; Computer science ; Physical system ; Statistical physics ; Variety (cybernetics) ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Dawei Wang | Ge Wang | S. Murakami | Zhongming Fan | Antonio Feteira | Di Zhou | Shi‐Kuan Sun | Quan-Liang Zhao | Ian M. Reaney
Source: Journal of Advanced Dielectrics (article, 2018-10-23)
Field: Materials Science — Materials Chemistry

BiFeO<sub>3</sub>-BaTiO<sub>3</sub>: A new generation of lead-free electroceramics Lead-based electroceramics such as Pb(Zr.Ti)O 3 (PZT) and its derivatives have excellent piezoelectric, pyroelectric and energy storage properties and can be used in a wide range of applications. Potential lead-free replacements for PZT such as potassium sodium niobate (KNN) and sodium bismuth titanate (NBT) have a much more limited range of useful properties and have been optimized primarily for piezoelectric applications. Here, we review the initial results on a new generation of lead-free electroceramics based on BiFeO 3 -BaTiO 3 (BF-BT) highlighting the essential crystal chemistry that permits a wide range of functional properties. We demonstrate that with the appropriate dopants and heat treatment, BF-BT can be used to fabricate commercially viable ceramics for applications, ranging from sensors, multilayer actuators, capacitors and high-density energy storage devices. We also assess the potential of BF-BT-based ceramics for electrocaloric and pyroelectric applications. Materials science ; Electroceramics ; Pyroelectricity ; Ferroelectricity ; Piezoelectricity ; Capacitor ; Lead zirconate titanate ; Bismuth ; Ceramic ; Nanotechnology ; Energy storage ; Isostructural ; Optoelectronics ; Dielectric ; Crystal structure ; Composite material ; Fabrication ; Voltage ; Metallurgy ; Electrical engineering ; Microfabrication ; Organic chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Dielectric materials and actuators
Authors: Yasuchika Hasegawa | Yuichi Kitagawa | Takayuki Nakanishi
Source: NPG Asia Materials (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Effective photosensitized, electrosensitized, and mechanosensitized luminescence of lanthanide complexes The 4f–4f emission of Tb(III), Eu(III), and Sm(III) complexes plays an important role in the design of monochromatic green, red, and deep-red luminescent materials for displays, lighting, and sensing devices. The 4f–4f emission of Yb(III), Nd(III), and Er(III) complexes is observed in the near-infrared (IR) region for bioimaging and security applications. However, their absorption coefficients are extremely small (ε < 10 L mol−1 cm−1). In this review, photosensitized luminescent lanthanide(III) complexes containing organic chromophores (ligands) with large absorption coefficients (ε > 10,000 L mol−1 cm−1) are introduced. Organic molecular design elements, including (1) the control of the excited triplet (T1) state, (2) the effects on the charge-transfer (CT) band, and (3) the energy transfer from metal ions for effective photosensitized luminescence, are explained. The characteristic electrosensitized luminescence (electroluminescence) and mechanoluminescence (triboluminescence) of lanthanide(III) complexes are also explained. Lanthanide(III) complexes with well-designed organic molecules are expected to open avenues of research among the fields of chemistry, physics, electronics, and material science. Lanthanide complexes can be made strongly luminescent by designing their ligands so that they act as antenna, find researchers in Japan. With their long luminescence lifetimes and narrow emission bands, lanthanide ions are attractive components for monochromatic luminescent materials. But their low light absorption hinders their widespread use. Yasuchika Hasegawa and co-workers from Hokkaido University, review how this problem can be overcome through using coordinating ligands as antennas. Such ligands absorb incident light and transfer its energy to the lanthanide ion. The researchers emphasize the importance of organic ligands’ excited triplet states and charge-transfer processes. They also discuss cases where luminescence is induced electrically or mechanically (for example, by grinding). The improved luminescence efficiency of such coordination complexes show promise for applications such as displays, solar cells and biomedical sensors. In this paper, the design strategy of trivalent lanthanide (Ln(III)) complexes for effective photo-, electric-, and tribo-sensitized luminescence are reviewed. Ln(III) complexes with well-designed organic molecules are expected to open up a frontier field of chemistry, physics, electronics and material science. Lanthanide ; Luminescence ; Materials science ; Chromophore ; Photochemistry ; Absorption (acoustics) ; Excited state ; Electroluminescence ; Phosphor ; Ion ; Optoelectronics ; Nanotechnology ; Chemistry ; Atomic physics ; Organic chemistry ; Physics Lanthanide and Transition Metal Complexes ; Organic Light-Emitting Diodes Research ; Luminescence and Fluorescent Materials
Authors: Nelly R. Hajizadeh | Daniel Franke | Cy M. Jeffries | Dmitri I. Svergun
Source: Scientific Reports (article, 2018-05-02)
Field: Materials Science — Materials Chemistry

Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data Molecular mass (MM) is one of the key structural parameters obtained by small-angle X-ray scattering (SAXS) of proteins in solution and is used to assess the sample quality, oligomeric composition and to guide subsequent structural modelling. Concentration-dependent assessment of MM relies on a number of extra quantities (partial specific volume, calibrated intensity, accurate solute concentration) and often yields limited accuracy. Concentration-independent methods forgo these requirements being based on the relationship between structural parameters, scattering invariants and particle volume obtained directly from the data. Using a comparative analysis on 165,982 unique scattering profiles calculated from high-resolution protein structures, the performance of multiple concentration-independent MM determination methods was assessed. A Bayesian inference approach was developed affording an accuracy above that of the individual methods, and reports MM estimates together with a credibility interval. This Bayesian approach can be used in combination with concentration-dependent MM methods to further validate the MM of proteins in solution, or as a reliable stand-alone tool in instances where an accurate concentration estimate is not available. Small-angle X-ray scattering ; Scattering ; Bayesian probability ; Bayesian inference ; Computer science ; Biological system ; Volume (thermodynamics) ; Inference ; Chemistry ; Materials science ; Computational physics ; Physics ; Artificial intelligence ; Thermodynamics ; Optics ; Biology Enzyme Structure and Function ; X-ray Spectroscopy and Fluorescence Analysis ; Protein Structure and Dynamics
Authors: Juntao Tang | Yang Song | Feiping Zhao | Stewart Spinney | Juliana S. Bernardes | Kam Chiu Tam
Source: Carbohydrate Polymers (article, 2018-12-26)
Field: Materials Science — Biomaterials

Compressible cellulose nanofibril (CNF) based aerogels produced via a bio-inspired strategy for heavy metal ion and dye removal A sustainable nanomaterial, cellulose nanofibril (CNF) was used to prepare aerogel sorbents to remove various contaminants in wastewater. A mussel-inspired coating strategy was used to introduce polydopamine onto the surface of CNFs, which were cross-linked with polyethylenimine (PEI) to form the aerogels. The synthetic procedure was optimized to achieve a minimal consumption of raw materials to produce a robust porous structure. The aerogels possessed a low density (25.0 mg/cm<sup>3</sup>), high porosity (98.5%) and shape recovery in air and water. Adsorption studies were conducted on two representative contaminants, Cu (II) and methyl orange (MO). The kinetic data obeyed the pseudo 2<sup>nd</sup> order kinetic model and the mechanism of adsorption could be described by the intra-particle diffusion model. The Langmuir model fitting yielded a maximum adsorption capacity of 103.5 mg/g and 265.9 mg/g for Cu (II) and MO, respectively. The effects of pH on the adsorption performance were evaluated, confirming that the aerogels can maintain a high adsorption capacity over a wide pH range. Adsorption ; Chemical engineering ; Aerogel ; Materials science ; Porosity ; Cellulose ; Nanomaterials ; Langmuir adsorption model ; Metal ions in aqueous solution ; Metal ; Chemistry ; Nanotechnology ; Composite material ; Organic chemistry ; Metallurgy Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Electrospun Nanofibers in Biomedical Applications