jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nathan C. Frey | Arkamita Bandyopadhyay | Arun Kumar | Babak Anasori | Yury Gogotsi | Vivek B. Shenoy
Source: ACS Nano (article, 2019-01-17)
Field: Materials Science — Materials Chemistry

Surface-Engineered MXenes: Electric Field Control of Magnetism and Enhanced Magnetic Anisotropy Controlling magnetism in two-dimensional (2D) materials via electric fields and doping enables robust long-range order by providing an external mechanism to modulate magnetic exchange interactions and anisotropy. In this report, we predict that transition metal carbide and nitride MXenes are promising candidates for controllable magnetic 2D materials. The surface terminations introduced during synthesis act as chemical dopants that influence the electronic structure, enabling controllable magnetic order. We show ground-state magnetic ordering in Janus M<sub>2</sub>XO <sub>x</sub>F<sub>2- x</sub> (M is an early transition metal, X is carbon or nitrogen, and x = 0.5, 1, or 1.5) with asymmetric surface functionalization, where local structural and chemical disorder induces magnetic ordering in some systems that are nonmagnetic or weakly magnetic in their pristine form. The resulting magnetic states of these noncentrosymmetric structures can be robustly switched and stabilized by tuning the interlayer exchange couplings with small applied electric fields. Furthermore, bond directionality is enhanced by Janus functionalization, resulting in improved magnetic anisotropy, which is essential to stable 2D magnetic ordering. The mixed termination-induced anisotropy leads to robust Ising ferromagnetism with an out-of-plane easy axis over the full range of relevant termination compositions for Janus Mn<sub>2</sub>N. Janus Cr<sub>2</sub>C, V<sub>2</sub>C, and Ti<sub>2</sub>C were found to be robustly antiferromagnetic. Our results provide a strategy for exploiting asymmetric surface functionalization to achieve room-temperature nanoscale magnetism under ambient conditions in MXenes with currently available synthesis techniques. MXenes ; Magnetism ; Materials science ; Condensed matter physics ; Magnetic anisotropy ; Ferromagnetism ; Antiferromagnetism ; Spintronics ; Surface modification ; Exchange bias ; Nanotechnology ; Magnetic field ; Magnetization ; Chemistry ; Physics MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Jennifer N. Wei | David Belanger | Ryan P. Adams | D. Sculley
Source: ACS Central Science (article, 2019-03-19)
Field: Chemistry — Spectroscopy

Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library’s coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction. Artificial neural network ; Computer science ; Fragmentation (computing) ; Ionization ; Mass spectrum ; Mass spectrometry ; Electron ionization ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Data mining ; Chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Qi Sun | Briana Aguila | Pui Ching Lan | Shengqian Ma
Source: Advanced Materials (article, 2019-03-12)
Field: Materials Science — Materials Chemistry

Tuning Pore Heterogeneity in Covalent Organic Frameworks for Enhanced Enzyme Accessibility and Resistance against Denaturants Achieving high-performance biocomposites requires knowledge of the compatability between the immobilized enzyme and its host material. The modular nature of covalent organic frameworks (COFs), as a host, allows their pore geometries and chemical functionalities to be fine-tuned independently, permitting comparative studies between the individual parameters and the performances of the resultant biocomposites. This research demonstrates that dual pores in COFs have profound consequences on the catalytic activity and denaturation of infiltrated enzymes. This approach enforces a constant pore environment by rational building-block design, which enables it to be unequivocally determined that pore heterogeneity is responsible for rate enhancements of up to threefold per enzyme molecule. More so, the enzyme is more tolerant to detrimental by-products when occupying the larger pore in a dual-pore COF compared to a corresponding uniform porous COF. Kinetic studies highlight that pore heterogeneity facilitates mass transfer of both reagents and products. This unparalleled versatility of these materials allows many different aspects to be designed on demand, lending credence to their prospect as next-generation host materials for various enzyme biocomposites catalysts. Materials science ; Covalent bond ; Porosity ; Immobilized enzyme ; Nanotechnology ; Rational design ; Catalysis ; Chemical engineering ; Enzyme catalysis ; Reagent ; Enzyme ; Organic chemistry ; Chemistry ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Porphyrin and Phthalocyanine Chemistry
Authors: Lede Xian | Dante M. Kennes | Nicolas Tancogne-Dejean | M. Altarelli | Ángel Rubio
Source: Nano Letters (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Multiflat Bands and Strong Correlations in Twisted Bilayer Boron Nitride: Doping-Induced Correlated Insulator and Superconductor Two-dimensional materials, obtained by van der Waals stacking of layers, are fascinating objects of contemporary condensed matter research, exhibiting a variety of new physics. Inspired by the breakthroughs of twisted bilayer graphene (TBG), we demonstrate that twisted bilayer boron nitride (TBBN) is an even more exciting novel system that turns out to be an excellent platform to realize new correlated phases and phenomena; exploration of its electronic properties shows that in contrast to TBG in TBBN multiple families of 2,4, and 6-fold degenerate flat bands emerge without the need to fine tune close to a "magic angle", resulting in dramatic and tunable changes in optical properties and exciton physics, and providing an additional platform to study strong correlations. Upon doping, unforeseen new correlated phases of matter (insulating and superconducting) emerge. TBBN could thus provide a promising experimental platform, insensitive to small deviations in the twist angle, to study novel exciton condensate and spatial confinement physics, and correlations in two dimensions. Superconductivity ; Condensed matter physics ; Bilayer graphene ; Magic angle ; Physics ; Twist ; Boron nitride ; Doping ; Materials science ; Mott insulator ; Graphene ; Nanotechnology ; Engineering physics ; Geometry ; Solid-state Graphene research and applications ; Quantum and electron transport phenomena ; Diamond and Carbon-based Materials Research
Authors: Chris A. Brosey | John A. Tainer
Source: Current Opinion in Structural Biology (review, 2019-06-13)
Field: Materials Science — Materials Chemistry

Evolving SAXS versatility: solution X-ray scattering for macromolecular architecture, functional landscapes, and integrative structural biology Small-angle X-ray scattering (SAXS) has emerged as an enabling integrative technique for comprehensive analyses of macromolecular structures and interactions in solution. Over the past two decades, SAXS has become a mainstay of the structural biologist's toolbox, supplying multiplexed measurements of molecular shape and dynamics that unveil biological function. Here, we discuss evolving SAXS theory, methods, and applications that extend the field of small-angle scattering beyond simple shape characterization. SAXS, coupled with size-exclusion chromatography (SEC-SAXS) and time-resolved (TR-SAXS) methods, is now providing high-resolution insight into macromolecular flexibility and ensembles, delineating biophysical landscapes, and facilitating high-throughput library screening to assess macromolecular properties and to create opportunities for drug discovery. Looking forward, we consider SAXS in the integrative era of hybrid structural biology methods, its potential for illuminating cellular supramolecular and mesoscale structures, and its capacity to complement high-throughput bioinformatics sequencing data. As advances in the field continue, we look forward to proliferating uses of SAXS based upon its abilities to robustly produce mechanistic insights for biology and medicine. Small-angle X-ray scattering ; Scattering ; Computational biology ; Nanotechnology ; Biological system ; Computer science ; Biology ; Materials science ; Physics ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Molecular Biology Techniques and Applications
Authors: Rajendra P. Joshi | Jesse Eickholt | Liling Li | Marco Fornari | Verónica Barone | Juan E. Peralta
Source: ACS Applied Materials & Interfaces (article, 2019-04-29)
Field: Materials Science — Materials Chemistry

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries Machine-learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool ( http://se.cmich.edu/batteries ) based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5000 candidate electrode materials for Na- and K-ion batteries. We also make available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal ions. These results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications. Materials science ; Electrode ; Ion ; Voltage ; Nanotechnology ; Optoelectronics ; Engineering physics ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Memory and Neural Computing
Authors: Brian L. DeCost | Bo Lei | Toby Francis | Elizabeth A. Holm
Source: Microscopy and Microanalysis (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov. Cementite ; Microstructure ; Segmentation ; Metallography ; Convolutional neural network ; Grain boundary ; Grain size ; Image segmentation Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Henry Chan | Mathew J. Cherukara | Badri Narayanan | Troy D. Loeffler | Chris J. Benmore | Stephen K. Gray | Subramanian K. R. S. Sankaranarayanan
Source: Nature Communications (article, 2019-01-22)
Field: Materials Science — Materials Chemistry

Machine learning coarse grained models for water An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP<sub>dih</sub>, and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10's of milliseconds of overall trajectories). Our ML BOP models predict both the correct experimental melting point of ice and the temperature of maximum density of liquid water that remained elusive to-date. Our ML workflow navigates efficiently through the high-dimensional parameter space to even improve upon existing high-quality CG models (e.g. mW model). Mesoscopic physics ; Water model ; Computer science ; Parameter space ; Molecular dynamics ; Set (abstract data type) ; Statistical physics ; Physics ; Chemistry ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Theoretical and Computational Physics ; nanoparticles nucleation surface interactions
Authors: Lucia Salvioni | Maria Antonietta Rizzuto | Jessica Bertolini | Laura Pandolfi | Miriam Colombo | Davide Prosperi
Source: Cancers (review, 2019-11-25)
Field: Materials Science — Biomaterials

Thirty Years of Cancer Nanomedicine: Success, Frustration, and Hope Starting with the enhanced permeability and retention (EPR) effect discovery, nanomedicine has gained a crucial role in cancer treatment. The advances in the field have led to the approval of nanodrugs with improved safety profile and still inspire the ongoing investigations. However, several restrictions, such as high manufacturing costs, technical challenges, and effectiveness below expectations, raised skeptical opinions within the scientific community about the clinical relevance of nanomedicine. In this review, we aim to give an overall vision of the current hurdles encountered by nanotherapeutics along with their design, development, and translation, and we offer a prospective view on possible strategies to overcome such limitations. Nanomedicine ; Engineering ethics ; Skepticism ; Relevance (law) ; Nanotechnology ; Medicine ; Risk analysis (engineering) ; Engineering ; Political science ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Jesús M. Fernández‐García | Paul J. Evans | Salvatore Filippone | M. Ángeles Herranz | Nazario Martı́n
Source: Accounts of Chemical Research (article, 2019-06-04)
Field: Chemistry — Organic Chemistry

Chiral Molecular Carbon Nanostructures Chirality is a fascinating property present in naturally occurring and artificial molecules and materials, observable as chiroptical behavior. The emerging area of carbon nanostructures has undergone tremendous development, with a wide variety of carbon nanoforms reported over the last two decades. However, despite interest in merging chirality and nanocarbons, this has been successfully achieved only in empty fullerenes, whereas in other kinds of fullerenes or carbon nanostructures such as carbon nanotubes, graphene, and graphene quantum dots (GQDs), to name the most popular systems, it is almost unknown. Therefore, controlling chirality in carbon nanostructures currently represents a major challenge for the chemical community. In this Account, we show our progress in the synthesis of chiral molecular carbon nanostructures, namely, metallofullerenes, endohedral fullerenes, GQDs, and curved molecular nanographenes, by using asymmetric catalysis and both top-down and bottom-up chemical approaches. Furthermore, we bring in a new family of lesser-known molecular chiral bilayer nanographenes, where chirality is introduced from the starting helicene moiety and a single enantiomer of the nanographene is synthesized. Some important landmarks in the development of chiral molecular carbon nanostructures shown in this Account are the application of synthesis-tailored, enantiomerically pure metallofullerenes as catalysts for hydrogen transfer reactions and the use of endohedral fullerenes to determine the effect of the incarcerated molecule in the carbon cage on the cis-trans stereoisomerization of optically active pendent moieties. Furthermore, the first top-down synthesis of chiral GQDs by functionalization with chiral alcohols is also presented. An emerging alternative to GQDs, when the desire for purity and atomistic control outweighs the cost of multistep synthesis, is the bottom-up approach, in which molecular nanographenes are formed in precise sizes and shapes and enantiomeric control is feasible. In this regard, a singular and amazing example is given by our synthesis of a single enantiomer of the first chiral bilayer nanographene, which formally represents a new family of molecular nanographenes with chirality controlled and maintained throughout their syntheses. The aforementioned synthetic chiral nanostructures represent groundbreaking nanocarbon systems where chirality is a further dimension of structural control, paving the way to a new scenario for carbon nanoforms in which chirality selection determines the properties of these novel carbon-based materials. Fine-tuning of such properties is envisioned to impact biomedical and materials science applications. Nanostructure ; Nanotechnology ; Carbon fibers ; Chemistry ; Materials science ; Composite number Fullerene Chemistry and Applications ; Synthesis and Properties of Aromatic Compounds ; Porphyrin and Phthalocyanine Chemistry
Authors: Angela Falciatore | Marianne Jaubert | Jean‐Pierre Bouly | Benjamin Bailleul | Thomas Möck
Source: The Plant Cell (review, 2019-12-18)
Field: Materials Science — Biomaterials

Diatom Molecular Research Comes of Age: Model Species for Studying Phytoplankton Biology and Diversity Diatoms are the world's most diverse group of algae, comprising at least 100,000 species. Contributing ∼20% of annual global carbon fixation, they underpin major aquatic food webs and drive global biogeochemical cycles. Over the past two decades, <i>Thalassiosira pseudonana</i> and <i>Phaeodactylum tricornutum</i> have become the most important model systems for diatom molecular research, ranging from cell biology to ecophysiology, due to their rapid growth rates, small genomes, and the cumulative wealth of associated genetic resources. To explore the evolutionary divergence of diatoms, additional model species are emerging, such as <i>Fragilariopsis cylindrus</i> and <i>Pseudo-nitzschia multistriata</i> Here, we describe how functional genomics and reverse genetics have contributed to our understanding of this important class of microalgae in the context of evolution, cell biology, and metabolic adaptations. Our review will also highlight promising areas of investigation into the diversity of these photosynthetic organisms, including the discovery of new molecular pathways governing the life of secondary plastid-bearing organisms in aquatic environments. Biology ; Diatom ; Diversity (politics) ; Phytoplankton ; Ecology ; Evolutionary biology ; Anthropology ; Nutrient Diatoms and Algae Research ; Algal biology and biofuel production ; Marine and coastal ecosystems
Authors: Oleksandr Voznyy | Larissa Levina | James Z. Fan | Mikhail Askerka | Ankit Jain | Min‐Jae Choi | Olivier Ouellette | Petar Todorović́ | Laxmi Kishore Sagar | Edward H. Sargent
Source: ACS Nano (article, 2019-09-20)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis Colloidal quantum dots (CQDs) allow broad tuning of the bandgap across the visible and near-infrared spectral regions. Recent advances in applying CQDs in light sensing, photovoltaics, and light emission have heightened interest in achieving further synthetic improvements. In particular, improving monodispersity remains a key priority in order to improve solar cells' open-circuit voltage, decrease lasing thresholds, and improve photodetectors' noise-equivalent power. Here we utilize machine-learning-in-the-loop to learn from available experimental data, propose experimental parameters to try, and, ultimately, point to regions of synthetic parameter space that will enable record-monodispersity PbS quantum dots. The resultant studies reveal that adding a growth-slowing precursor (oleylamine) allows nucleation to prevail over growth, a strategy that enables record-large-bandgap (611 nm exciton) PbS nanoparticles with a well-defined excitonic absorption peak (half-width at half-maximum (hwhm) of 145 meV). At longer wavelengths, we also achieve improved monodispersity, with an hwhm of 55 meV at 950 nm and 24 meV at 1500 nm, compared to the best published to date values of 75 and 26 meV, respectively. Quantum dot ; Nanotechnology ; Colloid ; Materials science ; Quantum ; Physics ; Chemistry ; Quantum mechanics ; Physical chemistry Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques