jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bohdan Andreiuk | Fay Nicolson | Louise Clark | Sajanlal R. Panikkanvalappil | Kenry Kenry | Mohammad Rashidian | Stefan Harmsen | Moritz F. Kircher
Source: Nanotheranostics (review, 2021-10-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and synthesis of gold nanostars-based SERS nanotags for bioimaging applications Surface-enhanced Raman spectroscopy (SERS) nanotags hold a unique place among bioimaging contrast agents due to their fingerprint-like spectra, which provide one of the highest degrees of detection specificity. However, in order to achieve a sufficiently high signal intensity, targeting capabilities, and biocompatibility, all components of nanotags must be rationally designed and tailored to a specific application. Design parameters include fine-tuning the properties of the plasmonic core as well as optimizing the choice of Raman reporter molecule, surface coating, and targeting moieties for the intended application. This review introduces readers to the principles of SERS nanotag design and discusses both established and emerging protocols of their synthesis, with a specific focus on the construction of SERS nanotags in the context of bioimaging and theranostics. Nanotechnology ; Context (archaeology) ; Biocompatibility ; Surface-enhanced Raman spectroscopy ; Materials science ; Raman spectroscopy ; Computer science ; Raman scattering ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: You‐Quan Zou | Dawei Zhang | Tanya K. Ronson | Andrew Tarzia | Zifei Lu | Kim E. Jelfs | Jonathan R. Nitschke
Source: Journal of the American Chemical Society (article, 2021-06-14)
Field: Chemistry — Organic Chemistry

Sterics and Hydrogen Bonding Control Stereochemistry and Self-Sorting in BINOL-Based Assemblies Here we demonstrate how the hydrogen-bonding ability of a BINOL-based dialdehyde subcomponent dictated the stereochemical outcome of its subsequent self-assembly into one diastereomeric helicate form when bearing free hydroxy groups, and another in the case of its methylated congener. The presence of methyl groups also altered the self-sorting behavior when mixed with another, short linear dialdehyde subcomponent, switching the outcome of the system from narcissistic to integrative self-sorting. In all cases, the axial chirality of the BINOL building block also dictated helicate metal center handedness during stereospecific self-assembly. A new family of stereochemically pure heteroleptic helicates were thus prepared using the new knowledge gained. We also found that switching from Fe<sup>II</sup> to Zn<sup>II</sup>, or the incorporation of a longer linear ligand, favored heteroleptic structure formation. Chemistry ; Steric effects ; Diastereomer ; Hydrogen bond ; Stereochemistry ; Chirality (physics) ; Supramolecular chemistry ; Stereospecificity ; Ligand (biochemistry) ; Sorting ; Receptor ; Molecule ; Catalysis ; Organic chemistry ; Biochemistry Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Molecular Sensors and Ion Detection
Authors: Vaibhav Singh | Priyanka | Priyesh V. More | Eva Hemmer | Yogendra Kumar Mishra | Pawan K. Khanna
Source: Materials Advances (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Magic-sized CdSe nanoclusters: a review on synthesis, properties and white light potential Magic-sized CdSe nanoclusters have gained tremendous attention due to their white-light emitting properties and biological applications. The article discusses the challenges in synthesis to achieve their challenging optical properties. Nanoclusters ; MAGIC (telescope) ; Materials science ; White light ; Nanotechnology ; White (mutation) ; Chemistry ; Optoelectronics ; Physics ; Astronomy Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Chalcogenide Semiconductor Thin Films
Authors: David Egea-Benavente | Jesús G. Ovejero | M. P. Morales | Domingo F. Barber
Source: Cancers (review, 2021-09-12)
Field: Materials Science — Biomaterials

Understanding MNPs Behaviour in Response to AMF in Biological Milieus and the Effects at the Cellular Level: Implications for a Rational Design That Drives Magnetic Hyperthermia Therapy toward Clinical Implementation Hyperthermia has emerged as a promising alternative to conventional cancer therapies and in fact, traditional hyperthermia is now commonly used in combination with chemotherapy or surgery during cancer treatment. Nevertheless, non-specific application of hyperthermia generates various undesirable side-effects, such that nano-magnetic hyperthermia has arisen a possible solution to this problem. This technique to induce hyperthermia is based on the intrinsic capacity of magnetic nanoparticles to accumulate in a given target area and to respond to alternating magnetic fields (AMFs) by releasing heat, based on different principles of physics. Unfortunately, the clinical implementation of nano-magnetic hyperthermia has not been fluid and few clinical trials have been carried out. In this review, we want to demonstrate the need for more systematic and basic research in this area, as many of the sub-cellular and molecular mechanisms associated with this approach remain unclear. As such, we shall consider here the biological effects that occur and why this theoretically well-designed nano-system fails in physiological conditions. Moreover, we will offer some guidelines that may help establish successful strategies through the rational design of magnetic nanoparticles for magnetic hyperthermia. Hyperthermia ; Magnetic hyperthermia ; Rational design ; Magnetic nanoparticles ; Cancer therapy ; Clinical trial ; Nanotechnology ; Hyperthermia therapy ; Cancer treatment ; Computer science ; Medicine ; Cancer ; Materials science ; Nanoparticle ; Internal medicine Nanoparticle-Based Drug Delivery ; Ultrasound and Hyperthermia Applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: Yujia Xu | Michele Kirchner
Source: Bioengineering (review, 2021-01-05)
Field: Materials Science — Biomaterials

Collagen Mimetic Peptides Since their first synthesis in the late 1960s, collagen mimetic peptides (CMPs) have been used as a molecular tool to study collagen, and as an approach to develop novel collagen mimetic biomaterials. Collagen, a major extracellular matrix (ECM) protein, plays vital roles in many physiological and pathogenic processes. Applications of CMPs have advanced our understanding of the structure and molecular properties of a collagen triple helix-the building block of collagen-and the interactions of collagen with important molecular ligands. The accumulating knowledge is also paving the way for developing novel CMPs for biomedical applications. Indeed, for the past 50 years, CMP research has been a fast-growing, far-reaching interdisciplinary field. The major development and achievement of CMPs were documented in a few detailed reviews around 2010. Here, we provided a brief overview of what we have learned about CMPs-their potential and their limitations. We focused on more recent developments in producing heterotrimeric CMPs, and CMPs that can form collagen-like higher order molecular assemblies. We also expanded the traditional view of CMPs to include larger designed peptides produced using recombinant systems. Studies using recombinant peptides have provided new insights on collagens and promoted progress in the development of collagen mimetic fibrillar self-assemblies. Extracellular matrix ; Triple helix ; Heterotrimeric G protein ; Nanotechnology ; Chemistry ; Collagen helix ; Computational biology ; Cell biology ; Materials science ; Biochemistry ; Biology ; Signal transduction ; Stereochemistry Collagen: Extraction and Characterization ; Chemical Synthesis and Analysis ; Protein Hydrolysis and Bioactive Peptides
Authors: Valentin Stanev | Kamal Choudhary | A. Gilad Kusne | Johnpierre Paglione | Ichiro Takeuchi
Source: Communications Materials (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

Artificial intelligence for search and discovery of quantum materials Abstract Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum materials, the rise of new experimental and computational techniques has increased the volume and the speed with which data are collected, and artificial intelligence is poised to impact the exploration of new materials such as superconductors, spin liquids, and topological insulators. This review outlines how the use of data-driven approaches is changing the landscape of quantum materials research. From rapid construction and analysis of computational and experimental databases to implementing physical models as pathfinding guidelines for autonomous experiments, we show that artificial intelligence is already well on its way to becoming the lynchpin in the search and discovery of quantum materials. Quantum ; Computer science ; Data science ; Quantum computer ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Rishi G. Agarwal | Hyun‐Jo Kim | James M. Mayer
Source: Journal of the American Chemical Society (article, 2021-02-10)
Field: Materials Science — Materials Chemistry

Nanoparticle O–H Bond Dissociation Free Energies from Equilibrium Measurements of Cerium Oxide Colloids A novel equilibrium strategy for measuring the hydrogen atom affinity of colloidal metal oxide nanoparticles is presented. Reactions between oleate-capped cerium oxide nanoparticle colloids (nanoceria) and organic proton-coupled electron transfer (PCET) reagents are used as a model system. Nanoceria redox changes, or hydrogen loadings, and overall reaction stoichiometries were followed by both <sup>1</sup>H NMR and X-ray absorption near-edge spectroscopies. These investigations revealed that, in many cases, reactions between nanoceria and PCET reagents reach equilibrium states with good mass balance. Each equilibrium state is a direct measure of the bond strength, or bond dissociation free energy (BDFE), between nanoceria and hydrogen. Further studies, including those with larger nanoceria, indicated that the relevant bond is a surface O-H. Thus, we have measured surface O-H BDFEs for nanoceria-the first experimental BDFEs for any nanoscale metal oxide. Remarkably, the measured CeO-H BDFEs span 13 kcal mol<sup>-1</sup> (0.56 eV) with changes in the average redox state of the nanoceria colloid. Possible chemical models for this strong dependence are discussed. We propose that the tunability of ceria BDFEs may be important in explaining its effectiveness in catalysis. More generally, metal oxide BDFEs have been used as predictors of catalyst efficacy that, traditionally, have only been accessible by computational methods. These results provide important experimental benchmarks for metal oxide BDFEs and demonstrate that the concepts of molecular bond strength thermochemistry can be applied to nanoscale materials. Chemistry ; Cerium oxide ; Dissociation (chemistry) ; Nanoparticle ; Bond-dissociation energy ; Cerium ; Colloid ; Oxide ; Physical chemistry ; Inorganic chemistry ; Nanotechnology ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Radioactive element chemistry and processing
Authors: Robert W. Epps | Amanda A. Volk | Kristofer G. Reyes | Milad Abolhasani
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Accelerated AI development for autonomous materials synthesis in flow Autonomous robotic experimentation strategies are rapidly rising in use because, without the need for user intervention, they can efficiently and precisely converge onto optimal intrinsic and extrinsic synthesis conditions for a wide range of emerging materials. However, as the material syntheses become more complex, the meta-decisions of artificial intelligence (AI)-guided decision-making algorithms used in autonomous platforms become more important. In this work, a surrogate model is developed using data from over 1000 in-house conducted syntheses of metal halide perovskite quantum dots in a self-driven modular microfluidic material synthesizer. The model is designed to represent the global failure rate, unfeasible regions of the synthesis space, synthesis ground truth, and sampling noise of a real robotic material synthesis system with multiple output parameters (peak emission, emission linewidth, and quantum yield). With this model, over 150 AI-guided decision-making strategies within a single-period horizon reinforcement learning framework are automatically explored across more than 600 000 simulated experiments - the equivalent of 7.5 years of continuous robotic operation and 400 L of reagents - to identify the most effective methods for accelerated materials development with multiple objectives. Specifically, the structure and meta-decisions of an ensemble neural network-based material development strategy are investigated, which offers a favorable technique for intelligently and efficiently navigating a complex material synthesis space with multiple targets. The developed ensemble neural network-based decision-making algorithm enables more efficient material formulation optimization in a no prior information environment than well-established algorithms. Reinforcement learning ; Computer science ; Modular design ; Artificial neural network ; Artificial intelligence ; Surrogate model ; Machine learning Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Dipendra Jha | Vishu Gupta | Logan Ward | Zijiang Yang | Christopher Wolverton | Ian Foster | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Scientific Reports (article, 2021-02-19)
Field: Materials Science — Materials Chemistry

Enabling deeper learning on big data for materials informatics applications The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data. Big data ; Computer science ; Deep learning ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Artificial neural network ; Representation (politics) ; Deep neural networks ; Residual ; Materials informatics ; Data science ; Informatics ; Labeled data ; Data mining ; Health informatics ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Julia Westermayr | Reinhard J. Maurer
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Physically inspired deep learning of molecular excitations and photoemission spectra Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput screening is bound to fail without efficient methods to predict molecular excited-state properties across chemical space. In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations. The model exploits the fundamental underlying physics of molecular resonances as eigenvalues of a latent Hamiltonian matrix and is thus able to accurately describe multiple resonances simultaneously. The performance of this model is demonstrated for a range of organic molecules across chemical composition space and configuration space. We further showcase the model capabilities by predicting photoemission spectra at the level of the GW approximation for previously unseen conjugated molecules. Hamiltonian (control theory) ; Quasiparticle ; Chemical space ; Eigenvalues and eigenvectors ; Molecule ; Perturbation theory (quantum mechanics) ; Hamiltonian matrix ; Spectral line ; Electronic structure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Di Chen | Yiwei Bai | Sebastian Ament | Wenting Zhao | Dan Guevarra | Lan Zhou | Bart Selman | R. B. van Dover | John M. Gregoire | Carla P. Gomes
Source: Nature Machine Intelligence (article, 2021-09-16)
Field: Materials Science — Materials Chemistry

Automating crystal-structure phase mapping by combining deep learning with constraint reasoning Computer science ; Bottleneck ; Phase (matter) ; Artificial intelligence ; Constraint (computer-aided design) ; Deep learning ; Machine learning ; Chemistry ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Zhantao Chen | Nina Andrejevic | Tess Smidt | Zhiwei Ding | Qian Xu | Yen‐Ting Chi | Quynh T. Nguyen | Ahmet Alatas | Jing Kong | Mingda Li
Source: Advanced Science (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Direct Prediction of Phonon Density of States With Euclidean Neural Networks Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density-of-states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>≈</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mn>3</mml:mn></mml:msup></mml:mrow></mml:math> examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors. Phonon ; Artificial neural network ; Euclidean geometry ; Work (physics) ; Crystal structure prediction ; Thermal ; Inversion (geology) ; Property (philosophy) ; Symmetry (geometry) Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Thermal properties of materials
Authors: Wei Zhou | Li Yang | Xiao Wang | Wenling Zhao | Jun-Xia Yang | Dong Zhai | Lei Sun | Wei Deng
Source: JACS Au (article, 2021-07-22)
Field: Materials Science — Materials Chemistry

In Silico Design of Covalent Organic Framework-Based Electrocatalysts Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M-N <sub><i>x</i></sub> O <sub><i>y</i></sub> (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M-N <sub><i>x</i></sub> O <sub><i>y</i></sub> were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni-N<sub>2</sub>O<sub>2</sub> unit is subsequently employed to synthesize the corresponding Ni-COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni-N<sub>2</sub>O<sub>2</sub> coordination environment. The studies of electrocatalytic activities confirm that Ni-COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm<sup>-2</sup> at a low overpotential of 335 mV. Furthermore, Ni-COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts. Overpotential ; Extended X-ray absorption fine structure ; XANES ; Oxygen evolution ; Density functional theory ; Catalysis ; Materials science ; Electrocatalyst ; Covalent organic framework ; Metal-organic framework ; Electrochemistry ; Absorption (acoustics) ; Porosity ; Transition metal ; Chemical engineering ; Nanotechnology ; Absorption spectroscopy ; Chemistry ; Physical chemistry ; Spectroscopy ; Computational chemistry ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion