jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengnan Zhao | Demian van Straten | Marike L. D. Broekman | Véronique Préat | Raymond M. Schiffelers
Source: Theranostics (review, 2020-01-01)
Field: Materials Science — Biomaterials

Nanocarrier-based drug combination therapy for glioblastoma The current achievements in treating glioblastoma (GBM) patients are not sufficient because many challenges exist, such as tumor heterogeneity, the blood brain barrier, glioma stem cells, drug efflux pumps and DNA damage repair mechanisms. Drug combination therapies have shown increasing benefits against those challenges. With the help of nanocarriers, enhancement of the efficacy and safety could be gained using synergistic combinations of different therapeutic agents. In this review, we will discuss the major issues for GBM treatment, the rationales of drug combinations with or without nanocarriers and the principle of enhanced permeability and retention effect involved in nanomedicine-based tumor targeting and promising nanodiagnostics or -therapeutics. We will also summarize the recent progress and discuss the clinical perspectives of nanocarrier-based combination therapies. The goal of this article was to provide better understanding and key considerations to develop new nanomedicine combinations and nanotheranostics options to fight against GBM. Nanocarriers ; Nanomedicine ; Glioblastoma ; Medicine ; Drug ; Glioma ; Pharmacology ; Drug delivery ; Cancer research ; Nanotechnology ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Baicheng Weng | Zhilong Song | Rilong Zhu | Qingyu Yan | Qingde Sun | Corey G. Grice | Yanfa Yan | Wan‐Jian Yin
Source: Nature Communications (article, 2020-07-14)
Field: Materials Science — Materials Chemistry

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts Abstract Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ / t , where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs 0.4 La 0.6 Mn 0.25 Co 0.75 O 3 , Cs 0.3 La 0.7 NiO 3 , SrNi 0.75 Co 0.25 O 3 , and Sr 0.25 Ba 0.75 NiO 3 , are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties. Perovskite (structure) ; Catalysis ; Simple (philosophy) ; Oxide ; Octahedron ; Computer science ; Symbolic regression ; Regression ; Work (physics) ; Materials science ; Biological system ; Chemistry ; Artificial intelligence ; Mathematics ; Physics ; Thermodynamics ; Crystallography ; Statistics ; Crystal structure ; Biology ; Metallurgy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Noel H. Wan | Tsung-Ju Lu | Kevin C. Chen | Michael P. Walsh | Matthew E. Trusheim | Lorenzo De Santis | Eric A. Bersin | Isaac B. Harris | Sara L. Mouradian | Ian R. Christen | Edward S. Bielejec | Dirk Englund
Source: Nature (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

Large-scale integration of artificial atoms in hybrid photonic circuits Laser linewidth ; Photonics ; Qubit ; Photonic integrated circuit ; Coherence (philosophical gambling strategy) ; Quantum entanglement ; Quantum ; Quantum information ; Quantum network Diamond and Carbon-based Materials Research ; Quantum optics and atomic interactions ; Mechanical and Optical Resonators
Authors: Jean-Christophe Brachet | Elodie Rouesne | J. Ribis | Thomas Guilbert | Stéphane Urvoy | G. Nony | Caroline Toffolon-Masclet | Matthieu Le Saux | Nihed Chaâbane | H. Palancher | Arnaud David | Jérémy Bischoff | Julien Augereau | Édouard Pouillier
Source: Corrosion Science (article, 2020-02-14)
Field: Materials Science — Materials Chemistry

High temperature steam oxidation of chromium-coated zirconium-based alloys: Kinetics and process Chromia ; Zirconium ; Chromium ; Materials science ; Cubic zirconia ; Eutectic system ; Intermetallic ; Metallurgy ; Diffusion ; Kinetics ; Metal ; Hydrogen ; Zirconium alloy ; Chemical engineering ; Alloy ; Chemistry ; Thermodynamics ; Ceramic Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Leopold Talirz | Snehal Kumbhar | Elsa Passaro | Aliaksandr V. Yakutovich | Valeria Granata | Fernando Gargiulo | Marco Borelli | Martin Uhrin | Sebastiaan P. Huber | Spyros Zoupanos | Carl S. Adorf | Casper Welzel Andersen | Ole Schütt | Carlo A. Pignedoli | Daniele Passerone | Joost VandeVondele | Thomas C. Schulthess | Berend Smit | Giovanni Pizzi | Nicola Marzari
Source: Scientific Data (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Materials Cloud, a platform for open computational science Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and dissemination services for raw and curated data, together with their provenance graph, (2) modelling services and virtual machines, (3) tools for data analytics, and pre-/post-processing, and (4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow retracing and reproducing any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain. Cloud computing ; Download ; Data sharing ; Raw data ; Metadata ; Pipeline transport ; Open source ; e-Science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: An Chen | Xu Zhang | Zhen Zhou
Source: InfoMat (article, 2020-02-23)
Field: Materials Science — Materials Chemistry

Machine learning: Accelerating materials development for energy storage and conversion Abstract With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery. In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energy‐related fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science. image Renewable energy ; Computer science ; Development (topology) ; Scale (ratio) ; Energy storage ; Lead (geology) ; Solar energy ; Risk analysis (engineering) ; Systems engineering ; Data science ; Biochemical engineering ; Nanotechnology ; Process engineering ; Engineering ; Materials science ; Power (physics) ; Electrical engineering ; Business Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Photocatalysis Techniques
Authors: Mihail Bogojeski | Leslie Vogt-Maranto | Mark E. Tuckerman | Klaus‐Robert Müller | Kieron Burke
Source: Nature Communications (article, 2020-10-16)
Field: Materials Science — Materials Chemistry

Quantum chemical accuracy from density functional approximations via machine learning Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol<sup>-1</sup> with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol<sup>-1</sup>) on test data. Moreover, density-based Δ-learning (learning only the correction to a standard DFT calculation, termed Δ-DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ-DFT is highlighted by correcting "on the fly" DFT-based molecular dynamics (MD) simulations of resorcinol (C<sub>6</sub>H<sub>4</sub>(OH)<sub>2</sub>) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ-DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails. Computer science ; Quantum chemical ; Density functional theory ; Machine learning ; Artificial intelligence ; Quantum ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Liuwei Wang | Yong Sik Ok | Daniel C.W. Tsang | Daniel S. Alessi | Jörg Rinklebe | Hailong Wang | Ondřej Mašek | Renjie Hou | David O’Connor | Deyi Hou
Source: Soil Use and Management (article, 2020-04-11)
Field: Materials Science — Biomaterials

New trends in biochar pyrolysis and modification strategies: feedstock, pyrolysis conditions, sustainability concerns and implications for soil amendment Abstract As a waste‐derived soil amendment with a long history, biochar has received extensive attention for its capability to improve soil fertility/health; remove or immobilize contaminants in soil, water and air; and mitigate climate change. With the aim of producing engineered biochars with excellent performances, new trends in biochar pyrolytic production and modification strategies have emerged. This review critically summarizes novel pyrolysis methods (e.g., microwave‐assisted pyrolysis, co‐pyrolysis and wet pyrolysis) and modification approaches (e.g., mineral modification, photocatalytic modification, electrochemical modification) with a focus on (a) the mechanisms involved in environmental remediation processes including soil immobilization, contaminant adsorption and catalytic oxidation; (b) effects of feedstock and pyrolysis conditions on physicochemical properties; (c) sustainability considerations in novel modification and pyrolysis strategies; and (d) the feasibility of extrapolating the results from wastewater treatment to soil remediation. It is argued that in order to achieve the maximum net environmental benefits, ‘greener’ modification methods are warranted, and the risks associated with pyrolysis of contaminated feedstock in soil amendment and contaminant sorption can be minimized through various novel approaches (e.g., co‐pyrolysis). Furthermore, novel pyrolysis methods can be combined with emerging modification strategies to synthesize more ‘effective’ biochars. Considering the similar aims of modification (e.g., increase surface area, introduce oxygen‐containing functional groups, increase aromaticity), the applicability of several novel approaches could in future can be expanded from contaminant adsorption/degradation in aqueous media to soil remediation/fertility improvement. Biochar ; Pyrolysis ; Amendment ; Environmental remediation ; Slash-and-char ; Environmental science ; Waste management ; Raw material ; Environmental chemistry ; Chemistry ; Contamination ; Organic chemistry Clay minerals and soil interactions ; Adsorption and biosorption for pollutant removal ; Electrokinetic Soil Remediation Techniques
Authors: Sergei Manzhos | Tucker Carrington
Source: Chemical Reviews (review, 2020-10-06)
Field: Materials Science — Materials Chemistry

Neural Network Potential Energy Surfaces for Small Molecules and Reactions We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational spectroscopy. The main focus is on methods for building molecular potential energy surfaces (PES) in internal coordinates that explicitly include all many-body contributions, even though some of the methods we review limit the degree of coupling, due either to a desire to limit computational cost or to limited data. Explicit and direct treatment of all many-body contributions is only practical for sufficiently small molecules, which are therefore our primary focus. This includes small molecules on surfaces. We consider direct, single NN PES fitting as well as more complex methods that impose structure (such as a multibody representation) on the PES function, either through the architecture of one NN or by using multiple NNs. We show how NNs are effective in building representations with low-dimensional functions including dimensionality reduction. We consider NN-based approaches to build PESs in the sums-of-product form important for quantum dynamics, ways to treat symmetry, and issues related to sampling data distributions and the relation between PES errors and errors in observables. We highlight combinations of NNs with other ideas such as permutationally invariant polynomials or sums of environment-dependent atomic contributions, which have recently emerged as powerful tools for building highly accurate PESs for relatively large molecular and reactive systems. Curse of dimensionality ; Observable ; Invariant (physics) ; Quantum ; Chemistry ; Statistical physics ; Potential energy ; Limit (mathematics) ; Focus (optics) ; Ab initio ; Function (biology) ; Potential energy surface ; Computer science ; Computational chemistry ; Quantum mechanics ; Physics ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Eric Bailey | Karen I. Winey
Source: Progress in Polymer Science (review, 2020-04-14)
Field: Materials Science — Polymers and Plastics

Dynamics of polymer segments, polymer chains, and nanoparticles in polymer nanocomposite melts: A review Polymer ; Polymer nanocomposite ; Nanocomposite ; Nanoparticle ; Molecular dynamics ; Materials science ; Chemical physics ; Dynamics (music) ; Nanotechnology ; Multiscale modeling ; Statistical physics ; Chemistry ; Composite material ; Physics ; Computational chemistry Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Material Dynamics and Properties
Authors: Marc A. Little | Andrew I. Cooper
Source: Advanced Functional Materials (article, 2020-02-17)
Field: Chemistry — Inorganic Chemistry

The Chemistry of Porous Organic Molecular Materials Abstract Porous organic molecular materials are a subclass of porous solids that are defined by their modular, molecular structures, and the absence of extended covalent or coordination bonding in the solid‐state. As a result, porous molecular materials are soluble and they can be processed into different forms, such as mixed matrix membranes. The structure of the porous modules can be fine‐tuned for specific applications, such as gas isotope separations, and in some cases the solid‐state properties of these materials can be defined by the structure of the porous molecule as viewed in isolation. In this review, the authors focus on the design of porous organic molecular materials and how their properties can be tuned for specific applications by using crystal engineering techniques. The authors distinguish between strategies where porosity is defined largely by the molecule itself, for example, in porous organic cages, and cases where porosity is generated by the solid‐state crystalline assembly. They emphasize the importance of computational techniques in the de novo design of functional, porous organic molecular materials, and how molecular modeling is applied to understand the properties of these materials. Porosity ; Materials science ; Porous medium ; Molecule ; Nanotechnology ; Crystal engineering ; Chemical engineering ; Organic chemistry ; Supramolecular chemistry ; Chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes
Authors: Alexander Leslie Anwyl-Irvine | Edwin S. Dalmaijer | Nick Hodges | Jo Evershed
Source: Behavior Research Methods (article, 2020-11-02)
Field: Chemistry — Physical and Theoretical Chemistry

Realistic precision and accuracy of online experiment platforms, web browsers, and devices Due to increasing ease of use and ability to quickly collect large samples, online behavioural research is currently booming. With this popularity, it is important that researchers are aware of who online participants are, and what devices and software they use to access experiments. While it is somewhat obvious that these factors can impact data quality, the magnitude of the problem remains unclear. To understand how these characteristics impact experiment presentation and data quality, we performed a battery of automated tests on a number of realistic set-ups. We investigated how different web-building platforms (Gorilla v.20190828, jsPsych v6.0.5, Lab.js v19.1.0, and psychoJS/PsychoPy3 v3.1.5), browsers (Chrome, Edge, Firefox, and Safari), and operating systems (macOS and Windows 10) impact display time across 30 different frame durations for each software combination. We then employed a robot actuator in realistic set-ups to measure response recording across the aforementioned platforms, and between different keyboard types (desktop and integrated laptop). Finally, we analysed data from over 200,000 participants on their demographics, technology, and software to provide context to our findings. We found that modern web platforms provide reasonable accuracy and precision for display duration and manual response time, and that no single platform stands out as the best in all features and conditions. In addition, our online participant analysis shows what equipment they are likely to use. Computer science ; Web browser ; World Wide Web ; The Internet ; Human–computer interaction ; Multimedia Various Chemistry Research Topics
Authors: Rhys E. A. Goodall | Alpha A. Lee
Source: Nature Communications (article, 2020-12-08)
Field: Materials Science — Materials Chemistry

Predicting materials properties without crystal structure: deep representation learning from stoichiometry Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use descriptors constructed from knowledge of either the full crystal structure - therefore only applicable to materials with already characterised structures - or structure-agnostic fixed-length representations hand-engineered from the stoichiometry. We develop a machine learning approach that takes only the stoichiometry as input and automatically learns appropriate and systematically improvable descriptors from data. Our key insight is to treat the stoichiometric formula as a dense weighted graph between elements. Compared to the state of the art for structure-agnostic methods, our approach achieves lower errors with less data. Computer science ; Stoichiometry ; Representation (politics) ; Block (permutation group theory) ; Key (lock) ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Data mining ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Chaobo Liang | Kunpeng Ruan | Yali Zhang | Junwei Gu
Source: ACS Applied Materials & Interfaces (article, 2020-03-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional Flexible Electromagnetic Interference Shielding Silver Nanowires/Cellulose Films with Excellent Thermal Management and Joule Heating Performances Flexible electromagnetic interference (EMI) shielding materials with excellent thermal conductivities and Joule heating performances are of urgent demand in the communication industry, artificial intelligence, and wearable electronics. In this work, highly conductive silver nanowires (AgNWs) were prepared using the polyol method. Cellulose sheets were then prepared by dissolving natural cotton in a green and efficient NaOH/urea aqueous solution. Finally, multifunctional flexible EMI shielding AgNWs/cellulose films were fabricated based on vacuum-assisted filtration and hot-pressing. AgNWs are evenly embedded in the inner cellulose matrix and overlap with each other to form a 3D network. AgNWs/cellulose films, with a thickness of 44.5 μm, obtain the superior EMI shielding effectiveness of 101 dB, which is the highest value ever reported for shielding materials with the same thickness. In addition, AgNWs/cellulose films present excellent tensile strength (60.7 MPa) and tensile modulus (3.35 GPa), ultrahigh electrical conductivity (σ, 5571 S/cm), and excellent in-plane thermal conductivity coefficient (λ<sub>∥</sub>, 10.55 W/mK), which can effectively dissipate the heat accumulation. Interestingly, AgNWs/cellulose films also show outstanding Joule heating performances, good stability, and sensitive temperature response at driving voltages, absolutely safe for the human body. Therefore, our fabricated multifunctional flexible AgNWs/cellulose films have broad prospects in the fields of EMI shielding and protection of outdoor large-scale power transformers and wearable electronics. Materials science ; Composite material ; Electromagnetic shielding ; Joule heating ; Flexible electronics ; Cellulose ; Thermal conductivity ; Ultimate tensile strength ; Optoelectronics ; Chemical engineering Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators