jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julia Westermayr | Philipp Marquetand
Source: Machine Learning Science and Technology (article, 2020-06-12)
Field: Materials Science — Materials Chemistry

Machine learning and excited-state molecular dynamics Abstract Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes. Field (mathematics) ; Dynamics (music) ; Excited state ; Quantum ; Molecular dynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular spectroscopy and chirality
Authors: Christoph Scherer | René Scheid | Denis Andrienko | Tristan Bereau
Source: Journal of Chemical Theory and Computation (article, 2020-04-13)
Field: Materials Science — Materials Chemistry

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids Current machine learning (ML) models aimed at learning force fields are plagued by their high computational cost at every integration time step. We describe a number of practical and computationally efficient strategies to parametrize traditional force fields for molecular liquids from ML: the particle decomposition ansatz to two- and three-body force fields, the use of kernel-based ML models that incorporate physical symmetries, the incorporation of switching functions close to the cutoff, and the use of covariant meshing to boost the training set size. Results are presented for model molecular liquids: pairwise Lennard-Jones, three-body Stillinger-Weber, and bottom-up coarse-graining of water. Here, covariant meshing proves to be an efficient strategy to learn canonically averaged instantaneous forces. We show that molecular dynamics simulations with tabulated two- and three-body ML potentials are computationally efficient and recover two- and three-body distribution functions. Many-body representations, decomposition, and kernel regression schemes are all implemented in the open-source software package VOTCA. Covariant transformation ; Kernel (algebra) ; Ansatz ; Computer science ; Molecular dynamics ; Pairwise comparison ; Decomposition ; Computational science ; Set (abstract data type) ; Force field (fiction) ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Computational chemistry ; Mathematics ; Geometry ; Chemistry ; Pure mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Phyu Hnin Thike | Zhaoyang Zhao | Peng Shi | Ying Jin
Source: Bulletin of Materials Science (article, 2020-08-13)
Field: Materials Science — Materials Chemistry

Significance of artificial neural network analytical models in materials’ performance prediction Artificial neural network ; Context (archaeology) ; Generalization ; Computer science ; Predictive modelling ; Ranking (information retrieval) ; Noise (video) ; Machine learning ; Artificial intelligence ; Data mining ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Musen Zhou | Anthony Vassallo | Jianzhong Wu
Source: ACS Applied Energy Materials (article, 2020-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Approach to Understanding the <i>In-Operando</i> Performance of Heteroatom-Doped Carbon Electrodes Doping with heteroatoms such as nitrogen and oxygen has been widely practiced to improve the capacitance of carbon electrodes for supercapacitor. However, the role of different heteroatoms and their local atomic configurations on the supercapacitor performance remains elusive, which hampers the rational design of carbon electrodes to achieve high energy density and power density. In this work, machine-learning models are applied to interpret how the surface chemistry affects the in-operando behavior of various carbon electrodes with different structural features such as the specific surface areas of micro- and mesopores. Quantitative descriptions have been established to predict how the configurations of nitrogen-doping and oxygen-doping influence the capacitance and retention rate. The machine-learning models provide insights into the design and possible routes to the synthesis of nitrogen and oxygen co-doped carbon electrodes that maximize the energy storage capacity. Heteroatom ; Supercapacitor ; Carbon fibers ; Capacitance ; Electrode ; Doping ; Materials science ; Nanotechnology ; Energy storage ; Oxygen ; Chemical engineering ; Optoelectronics ; Chemistry ; Power (physics) ; Organic chemistry ; Composite material ; Thermodynamics ; Physical chemistry Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Conducting polymers and applications
Authors: Magnus Röding | Zheng Ma | Salvatore Torquato
Source: Scientific Reports (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Predicting permeability via statistical learning on higher-order microstructural information Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods. Tortuosity ; Artificial neural network ; Geodesic ; Permeability (electromagnetism) ; Lattice Boltzmann methods ; Linear regression ; Correlation ; Porosity ; Porous medium Machine Learning in Materials Science ; Lattice Boltzmann Simulation Studies ; Generative Adversarial Networks and Image Synthesis
Authors: Paul W. M. Blom
Source: Advanced Materials Technologies (article, 2020-05-12)
Field: Materials Science — Polymers and Plastics

Polymer Electronics: To Be or Not to Be? Abstract The realization that polymers can be used as active material in opto‐electronic applications initiated substantial effort in the scientific community to explore new materials. Polymers can be made strong, flexible, lightweight, and can be mass produced. Furthermore, polymers can be processed at low temperatures, typically below 150 °C, creating the opportunity to use a range of plastic substrates instead of glass. Many polymers are soluble in organic solvents, making it possible to create electronically active “inks” that allow for solution‐processed electronic components as light‐emitting diodes and transistors. Examples of innovative new products based on semiconducting polymers are inkjet‐printed displays, non‐contact radio frequency identification tags, and sensors. However, the realization of polymeric displays is hindered by the relatively low efficiency of polymer‐based light‐emitting diodes. Major problems are the inability to realize multi‐layers from solution, insufficient harvesting of triplet excitons and the presence of defects. Printed circuits of organic transistors are still hampered by stability issues and relatively low charge carrier mobility of the organic semiconductors. More recently, organic electrochemical transistors have been employed as biosensors. Herein, the fundamentals and recent progress of polymer‐based light‐emitting devices and transistors are being discussed. Materials science ; Transistor ; Polymer ; Electronics ; Organic electronics ; Nanotechnology ; OLED ; Organic semiconductor ; Printed electronics ; Realization (probability) ; Optoelectronics ; Diode ; Electrical engineering ; Voltage Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Organic Light-Emitting Diodes Research
Authors: Cong Liu | Hao Gao | Andreas Hermann | Yong Wang | Maosheng Miao | Chris J. Pickard | R. J. Needs | Hui‐Tian Wang | Dingyu Xing | Jian Sun
Source: Physical Review X (article, 2020-04-09)
Field: Chemistry — Inorganic Chemistry

Plastic and Superionic Helium Ammonia Compounds under High Pressure and High Temperature Both helium and ammonia are main components of icy giant planets. While ammonia is very reactive, helium is the most inert element in the universe. It is of great interest whether ammonia and helium can react with each other under planetary conditions, and if so, what kinds of structures and states of matter can form. Here, using crystal structure prediction methods and first-principles calculations, we report three new stable stoichiometries and eight new stable phases of He-NH 3 compounds under pressures up to 500 GPa. These structures may exhibit perovskitelike structures for HeNH 3 and He 2 NH 3 , and a host-guest crystal structure for HeNH 3 2 . Superionic states are found in all these He-NH 3 compounds under high pressures and temperatures in which the hydrogen atoms are diffusive while the nitrogen and helium atoms remain fixed. Such dynamical behavior in helium ammonia compounds is quite different from that in helium water compounds, where weakly interacting helium is more diffusive than stronger bound hydrogen. The lowdensity host-guest phase of space group I4cm is found to be stable at very low pressures (about 3 GPa) and it enters into a plastic state, characterized by freely rotating ammonia molecules. The present results suggest that plastic or superionic helium ammonia compounds may exist under planetary conditions, and helium contributes crucially to the exotic physics and chemistry observed under extreme conditions. Helium ; Chemical physics ; Ammonia ; Plastic crystal ; Hydrogen ; Stoichiometry ; Chemistry ; Atomic physics ; Materials science ; Thermodynamics ; Physics ; Physical chemistry ; Phase transition ; Organic chemistry Inorganic Fluorides and Related Compounds ; High-pressure geophysics and materials ; Advanced Chemical Physics Studies
Authors: Reza Darvishi Kamachali
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A model for grain boundary thermodynamics Systematic microstructure design requires reliable thermodynamic descriptions of each and all microstructure elements. While such descriptions are well established for most bulk phases, thermodynamic assessment of microstructure defects is challenging because of their individualistic nature. In this paper, a model is devised for assessing grain boundary thermodynamics based on available bulk thermodynamic data. We propose a continuous relative atomic density field and its spatial gradients to describe the grain boundary region with reference to the homogeneous bulk and derive the grain boundary Gibbs free energy functional. The grain boundary segregation isotherm and phase diagram are computed for a regular binary solid solution, and qualitatively benchmarked for the Pt-Au system. The relationships between the grain boundary's atomic density, excess free volume, and misorientation angle are discussed. Combining the current density-based model with available bulk thermodynamic databases enables constructing databases, phase diagrams, and segregation isotherms for grain boundaries, opening possibilities for studying and designing heterogeneous microstructures. Misorientation ; Grain boundary ; Microstructure ; Gibbs free energy ; Phase diagram ; Boundary (topology) ; Phase field models ; Work (physics) ; Thermodynamic state Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Microstructure and Mechanical Properties of Steels
Authors: Yuan Dong | Dawei Li | Chi Zhang | Chuhan Wu | Hong Wang | Ming Xin | Jianlin Cheng | Jian Lin
Source: Carbon (article, 2020-07-22)
Field: Materials Science — Materials Chemistry

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN Inverse ; Graphene ; Materials science ; Mathematics ; Internal medicine ; Medicine ; Nanotechnology ; Geometry Machine Learning in Materials Science ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Lisa Claeys | Vincent Cnockaert | Tom Depover | Iris De Graeve | Kim Verbeken
Source: Acta Materialia (article, 2020-01-06)
Field: Materials Science — Metals and Alloys

Critical assessment of the evaluation of thermal desorption spectroscopy data for duplex stainless steels: A combined experimental and numerical approach Materials science ; Desorption ; Austenite ; Activation energy ; Thermal desorption spectroscopy ; Thermal desorption ; Hydrogen ; Diffusion ; Analytical Chemistry (journal) ; Ferrite (magnet) ; Microstructure ; Duplex (building) ; Thermodynamics ; Metallurgy ; Composite material ; Physical chemistry ; Adsorption ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Welding Techniques and Residual Stresses
Authors: Pranvera Breznica | Rozafa Koliqi | A. Daka
Source: Medicine and Pharmacy Reports (review, 2020-09-25)
Field: Materials Science — Biomaterials

A review of the current understanding of nanoparticles protein corona composition Upon entering into the biological environments, the surface of the nanoparticles is immediately coated with proteins and form the so-called a protein corona due to which a nanoparticle changes its "synthetic" identity to a new "biological" identity. Different types of nanoparticles have different protein binding profiles, which is why they have different protein corona composition and therefore it cannot be said that there is a universal protein corona. The composition and amount of protein in the corona depends on the physical and chemical characteristics of the nanoparticles, the type of biological medium and the exposure time. Protein corona increases the diameter but also changes the composition of the surface of the nanoparticles and these changes affect biodistribution, efficacy, and toxicity of the nanoparticles. Nanoparticle ; Corona (planetary geology) ; Composition (language) ; Biodistribution ; Chemistry ; Biophysics ; Nanotechnology ; Materials science ; Biochemistry ; Biology Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Bone Tissue Engineering Materials
Authors: Huey Wen Ooi | Jordy M M Kocken | Francis L. C. Morgan | Afonso Malheiro | Bram Zoetebier | Marcel Karperien | Paul Wieringa | Pieter J. Dijkstra | Lorenzo Moroni | Matthew B. Baker
Source: Biomacromolecules (article, 2020-04-03)
Field: Chemistry — Organic Chemistry

Multivalency Enables Dynamic Supramolecular Host–Guest Hydrogel Formation Supramolecular and dynamic biomaterials hold promise to recapitulate the time-dependent properties and stimuli-responsiveness of the native extracellular matrix (ECM). Host-guest chemistry is one of the most widely studied supramolecular bonds, yet the binding characteristics of host-guest complexes (β-CD/adamantane) in relevant biomaterials have mostly focused on singular host-guest interactions or nondiscrete multivalent pendent polymers. The stepwise synergistic effect of multivalent host-guest interactions for the formation of dynamic biomaterials remains relatively unreported. In this work, we study how a series of multivalent adamantane (guest) cross-linkers affect the overall binding affinity and ability to form supramolecular networks with alginate-CD (Alg-CD). These binding constants of the multivalent cross-linkers were determined via NMR titrations and showed increases in binding constants occurring with multivalent constructs. The higher multivalent cross-linkers enabled hydrogel formation; furthermore, an increase in binding and gelation was observed with the inclusion of a phenyl spacer to the cross-linker. A preliminary screen shows that only cross-linking Alg-CD with an 8-arm-multivalent guest results in robust gel formation. These cytocompatible hydrogels highlight the importance of multivalent design for dynamically cross-linked hydrogels. These materials hold promise for development toward cell- and small molecule-delivery platforms and allow discrete and fine-tuning of network properties. Supramolecular chemistry ; Self-healing hydrogels ; Adamantane ; Chemistry ; Molecule ; Biophysics ; Nanotechnology ; Polymer chemistry ; Materials science ; Organic chemistry Supramolecular Chemistry and Complexes ; Hydrogels: synthesis, properties, applications ; Supramolecular Self-Assembly in Materials
Authors: Hyunsoo Lim | Dabum Kim | Goomin Kwon | Hyun-Jong Kim | Jungmok You | Jeonghun Kim | Miharu Eguchi | Ashok Kumar Nanjundan | Jongbeom Na | Yusuke Yamauchi
Source: The Journal of Physical Chemistry C (article, 2020-10-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis of Uniformly Sized Mesoporous Silver Films and Their SERS Application Nanostructured metal films and particles have been recently exploited as substrates for surface-enhanced Raman spectroscopy (SERS) studies because their intrinsic properties lead to substantial Raman scattering enhancement. Among metals, silver (Ag) is one of the efficient elements for SERS because of its potential for high Raman scattering enhancement and low molecular detection level. Substantial electric field enhancement takes place around the center of uniform mesopores as well as on the walls between the pores, leading to enhanced light scattering as well as SERS. However, aggregation and high surface energy, which are intrinsic to Ag structures, hamper the formation of mesoporous structures. In this study, we have successfully achieved the first synthesis of uniformly sized mesoporous silver films (MAgFs) using self-assembled polymeric micelles under controlled conditions. The synthetic conditions such as temperatures, applied potentials, and deposition times were fine-tuned to obtain the optimized mesoporous structure. The MAgF demonstrated a 10−9 M level of detection and 107 to 108 enhancement factor without any surface modification. The simple synthetic process, the potential of high performance, and a large detecting area of MAgFs enable us to realize the fabrication of a suitable SERS substrate. Mesoporous material ; Raman scattering ; Materials science ; Nanotechnology ; Raman spectroscopy ; Substrate (aquarium) ; Fabrication ; Mesoporous silica ; Deposition (geology) ; Scattering ; Chemical engineering ; Optics ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Mei‐Yan Gao | Bai‐Qiao Song | Debobroto Sensharma | Michael J. Zaworotko
Source: SmartMat (article, 2020-12-29)
Field: Chemistry — Inorganic Chemistry

Crystal engineering of porous coordination networks for C3 hydrocarbon separation Abstract C3 hydrocarbons (HCs), especially propylene and propane, are high‐volume products of the chemical industry as they are utilized for the production of fuels, polymers, and chemical commodities. Demand for C3 HCs as chemical building blocks is increasing but obtaining them in sufficient purity (&gt;99.95%) for polymer and chemical processes requires economically and energetically costly methods such as cryogenic distillation. Adsorptive separations using porous coordination networks (PCNs) could offer an energy‐efficient alternative to current technologies for C3 HC purification because of the lower energy footprint of sorbent separations for recycling versus alternatives such as distillation, solvent extraction, and chemical transformation. In this review, we address how the structural modularity of porous PCNs makes them amenable to crystal engineering that in turn enables control over pore size, shape, and chemistry. We detail how control over pore structure has enabled PCN sorbents to offer benchmark performance for C3 separations thanks to several distinct mechanisms, each of which is highlighted. We also discuss the major challenges and opportunities that remain to be addressed before the commercial development of PCNs as advanced sorbents for C3 separation becomes viable. Process engineering ; Porosity ; Distillation ; Sorbent ; Chemical engineering ; Polymer ; Materials science ; Nanotechnology ; Extraction (chemistry) ; Chemistry ; Organic chemistry ; Engineering ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Philippe Schwaller | Daniel Probst | Alain C. Vaucher | Vishnu H Nair | David Kreutter | Teodoro Laino | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2020-08-07)
Field: Materials Science — Materials Chemistry

Mapping the Space of Chemical Reactions using Attention-Based Neural Networks Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and e cient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple textbased representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained dierences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching. Computer science ; Atlas (anatomy) ; Cluster analysis ; Chemical space ; Artificial intelligence ; Bioinformatics ; Biology Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Machine Learning in Bioinformatics