jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shunning Li | Yuanji Liu | Dong Chen | Yi Jiang | Zhiwei Nie | Feng Pan
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2021-06-18)
Field: Materials Science — Materials Chemistry

Encoding the atomic structure for machine learning in materials science Abstract In recent years, we have witnessed a widespread application of machine learning techniques in the field of materials science, owing to the increased availability of research data and sophisticated algorithms. At the core of this technology lies the ability to encode material structures into descriptors that are understandable for a computer. Although significant advances have been made in this area, there is a continued need to explore efficient structure‐encoding strategies so as to maximize the predictive power of the machine learning models. Here we present a revision of the exciting progress in four representative structural features that are capable of describing the structures of diverse materials: structure graph, Coulomb matrix, topological descriptor, and diffraction fingerprint. Particular attention is given to the studies of crystalline solids, which appear more challenging to be encoded than molecules. By summarizing previous works and presenting critical appraisals of these descriptors, this review could offer some guideline for the selection of structural features and stimulate inspiration for the design of powerful descriptors suited towards different tasks. This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Data Science &gt; Artificial Intelligence/Machine Learning Computer science ; Artificial intelligence ; ENCODE ; Machine learning ; Field (mathematics) ; Graph ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yu Gan | Guanjie Wang | Jian Zhou | Zhimei Sun
Source: npj Computational Materials (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning Abstract Layered IV-V-VI semiconductors have immense potential for thermoelectric (TE) applications due to their intrinsically ultralow lattice thermal conductivity. However, it is extremely difficult to assess their TE performance via experimental trial-and-error methods. Here, we present a machine-learning-based approach to accelerate the discovery of promising thermoelectric candidates in this chalcogenide family. Based on a dataset generated from high-throughput ab initio calculations, we develop two highly accurate-and-efficient neural network models to predict the maximum ZT ( ZT max ) and corresponding doping type, respectively. The top candidate, n-type Pb 2 Sb 2 S 5 , is successfully identified, with the ZT max over 1.0 at 650 K, owing to its ultralow thermal conductivity and decent power factor. Besides, we find that n-type Te-based compounds exhibit a combination of high Seebeck coefficient and electrical conductivity, thereby leading to better TE performance under electron doping than hole doping. Whereas p-type TE performance of Se-based semiconductors is superior to n-type, resulting from large Seebeck coefficient induced by high density-of-states near valence band edges. Thermoelectric effect ; Materials science ; Seebeck coefficient ; Ab initio ; Thermoelectric materials ; Doping ; Semiconductor ; Condensed matter physics ; Chalcogenide ; Thermal conductivity ; Ab initio quantum chemistry methods ; Valence (chemistry) ; Optoelectronics ; Thermodynamics ; Physics ; Quantum mechanics ; Composite material Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Colin Bonatti | Dirk Mohr
Source: Science Advances (article, 2021-06-23)
Field: Materials Science — Materials Chemistry

One for all: Universal material model based on minimal state-space neural networks Computational models describing the mechanical behavior of materials are indispensable when optimizing the stiffness and strength of structures. The use of state-of-the-art models is often limited in engineering practice due to their mathematical complexity, with each material class requiring its own distinct formulation. Here, we develop a recurrent neural network framework for material modeling by introducing "Minimal State Cells." The framework is successfully applied to datasets representing four distinct classes of materials. It reproduces the three-dimensional stress-strain responses for arbitrary loading paths accurately and replicates the state space of conventional models. The final result is a universal model that is flexible enough to capture the mechanical behavior of any engineering material while providing an interpretable representation of their state. Computer science ; Artificial neural network ; State (computer science) ; State space ; Stress (linguistics) ; Artificial intelligence ; Mathematics ; Algorithm Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topic Modeling
Authors: Andrew J. Lew | Chi-Hua Yu | Yu-Chuan Hsu | Markus J. Buehler
Source: npj 2D Materials and Applications (article, 2021-04-30)
Field: Materials Science — Materials Chemistry

Deep learning model to predict fracture mechanisms of graphene Abstract Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials. Graphene ; Artificial intelligence ; Scalability ; Deep learning ; Fracture (geology) ; Computer science ; Nanotechnology ; Materials science ; Nanomaterials ; Fractal ; Machine learning ; Mathematics ; Composite material Graphene research and applications ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies
Authors: Yanyan Fang | Qianyu Zhang | Lifeng Cui
Source: Microporous and Mesoporous Materials (article, 2021-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress of mesoporous materials for high performance supercapacitors Supercapacitor ; Mesoporous material ; Materials science ; Nanotechnology ; Capacitance ; Energy storage ; Electrolyte ; Pseudocapacitor ; Electrode ; Oxide ; Power (physics) ; Chemistry ; Catalysis Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Polyoxometalates: Synthesis and Applications
Authors: Johannes T. Margraf | Karsten Reuter
Source: Nature Communications (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Pure non-local machine-learned density functional theory for electron correlation Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in computational chemistry, their (semi-)local treatment of electron correlation has a number of well-known pathologies, e.g. related to electron self-interaction. Here, we present a type of machine-learning (ML) based DFA (termed Kernel Density Functional Approximation, KDFA) that is pure, non-local and transferable, and can be efficiently trained with fully quantitative reference methods. The functionals retain the mean-field computational cost of common DFAs and are shown to be applicable to non-covalent, ionic and covalent interactions, as well as across different system sizes. We demonstrate their remarkable possibilities by computing the free energy surface for the protonated water dimer at hitherto unfeasible gold-standard coupled cluster quality on a single commodity workstation. Density functional theory ; Water dimer ; Computer science ; Kernel (algebra) ; Ionic bonding ; Electronic correlation ; Statistical physics ; Electron ; Computational chemistry ; Chemistry ; Molecule ; Physics ; Quantum mechanics ; Mathematics ; Ion ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: W.A.M. Kethaki Pabasara Wickramaarachchi | Manickam Minakshi | Xiangpeng Gao | Rukshima Dabare | Kok Wai Wong
Source: Chemical Engineering Journal Advances (article, 2021-07-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hierarchical porous carbon from mango seed husk for electro-chemical energy storage Bio-waste valorization of Mango Seed (MS) husk to Activated Carbon (AC) was conducted via carbonization followed by chemical activation using KOH for sustainable supercapacitor material. Various physicochemical techniques were used to characterise both MS husk and AC, while electrochemical techniques were used to assess the energy storage capability. Carbonized MS husk was activated at four different temperatures 800, 900, 1000, and 1100 °C. Optimal activation temperature was found to be 1100 °C resulting in a hierarchical porous structure, possessing the highest specific surface area 1943 m2 g−1 and average pore volume 0.397 cm3 g−1, the critical parameters for their energy storage performance. In a three-electrode configuration, the MS-AC sample synthesized at 1100 °C (MS-AC 1100) exhibited a maximum specific capacitance of 135 F g−1 at 5 mA cm−2 with the energy density of 19 Wh kg−1 at the power density of 1077 W kg−1, which is higher than the commercially available AC. The MS-AC 1100 delivered 97% of the capacitance when increasing the current densities from 2 to 10 mA cm−2. The capacitance retention is 100% at 5 mA cm−2 with 1000 cycles. Moreover, Machine Learning (ML) models are used to identify the non-linear pattern for predicting the AC capacitance using various input parameters. Out of the four ML models considered in this work, the Multi-Layer Perceptron (MLP) model outperformed the other models to better correlate the predicted data and the experimental measurements. This study demonstrates the economic perspective of repurposing MS husk bio-waste to a low-cost super-capacitor electrode material and developing high-performance carbon materials to aid the integration of renewable energy into electricity systems. Husk ; Supercapacitor ; Capacitance ; Carbonization ; Materials science ; Activated carbon ; Energy storage ; Specific energy ; Volume (thermodynamics) ; Carbon fibers ; Porosity ; Current density ; Electrode ; Chemical engineering ; Composite material ; Chemistry ; Adsorption ; Power (physics) ; Botany ; Thermodynamics ; Organic chemistry ; Physics ; Composite number Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Mahsa Mirzaei | Irini Furxhi | Finbarr Murphy | Martin Mullins
Source: Nanomaterials (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model’s validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools. Machine learning ; Cluster analysis ; Antibiotics ; Predictive modelling ; Antibacterial activity ; Computer science ; Nanoparticle ; Homogenization (climate) ; Artificial intelligence ; Antimicrobial ; Biochemical engineering ; Nanotechnology ; Bacteria ; Materials science ; Biology ; Engineering ; Microbiology Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Mohammed Al‐Fahdi | Alejandro Rodriguez | Tao Ouyang | Ming Hu
Source: Crystals (article, 2021-07-05)
Field: Materials Science — Materials Chemistry

High-Throughput Computation of New Carbon Allotropes with Diverse Hybridization and Ultrahigh Hardness The discovery of new carbon allotropes with different building blocks and crystal symmetries has long been of great interest to broad materials science fields. Herein, we report several hundred new carbon allotropes predicted by the state-of-the-art RG2 code and first-principles calculations. The types of new carbon allotropes that were identified in this work span pure sp2, hybrid sp2/sp3, and pure sp3 C–C bonding. All structures were globally optimized at the first-principles level. The thermodynamic stability of some selected carbon allotropes was further validated by computing their phonon dispersions. The predicted carbon allotropes possess a broad range of Vickers’ hardness. This wide range of Vickers’ hardness is explained in detail in terms of both atomic descriptors such as density, volume per atom, packing fraction, and local potential energy throughout the unit cell, and global descriptors such as elastic modulus, shear modulus, and bulk modulus, universal anisotropy, Pugh’s ratio, and Poisson’s ratio. For the first time, we found strong correlation between Vickers’ hardness and average local potentials in the unit cell. This work provides deep insight into the identification of novel carbon materials with high Vickers’ hardness for modern applications in which ultrahigh hardness is desired. Moreover, the local potential averaged over the entire unit cell of an atomic structure, an easy-to-evaluate atomic descriptor, could serve as a new atomic descriptor for efficient screening of the mechanical properties of unexplored structures in future high-throughput computing and artificial-intelligence-accelerated materials discovery methods. Materials science ; Vickers hardness test ; Bulk modulus ; Carbon fibers ; Shear modulus ; Poisson's ratio ; Elastic modulus ; Atom (system on chip) ; Nanotechnology ; Composite material ; Computer science ; Mathematics ; Microstructure ; Poisson distribution Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Mohamed Mohamady Ghobashy | M. Abd Elkodous | Soha Shabaka | Sherif A. Younis | Dalal Mohamed Alshangiti | Mohamed Madani | Samera Ali Al-Gahtany | Walid F. Elkhatib | Ayman Noreddin | Norhan Nady | Gharieb S. El‐Sayyad
Source: Nanotechnology Reviews (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

An overview of methods for production and detection of silver nanoparticles, with emphasis on their fate and toxicological effects on human, soil, and aquatic environment Abstract Silver nanoparticles (AgNPs) have been extensively used in various industries; however, this is accompanied by several implications to humans and the environment. This review focuses on different aspects of AgNPs including the production and detection techniques, their fate, and dynamics in response to different environmental factors. In addition, this review illustrates the toxicity mechanism and the interaction of AgNPs with different matrices, such as aquatic environment, soil, crops, and humans. Reduction measures and future research are discussed. Silver nanoparticle ; Biochemical engineering ; Aquatic environment ; Production (economics) ; Mechanism (biology) ; Environmental science ; Environmental chemistry ; Computer science ; Nanotechnology ; Chemistry ; Ecology ; Nanoparticle ; Biology ; Engineering ; Materials science Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: N Pende | Adrià Sogues | Daniela Megrian | Anna Sartori-Rupp | Patrick England | Hayk Palabikyan | Simon K.‐M. R. Rittmann | Martín Graña | Anne Marie Wehenkel | Pedro M. Alzari | Simonetta Gribaldo
Source: Nature Communications (article, 2021-06-04)
Field: Materials Science — Materials Chemistry

SepF is the FtsZ anchor in archaea, with features of an ancestral cell division system Most archaea divide by binary fission using an FtsZ-based system similar to that of bacteria, but they lack many of the divisome components described in model bacterial organisms. Notably, among the multiple factors that tether FtsZ to the membrane during bacterial cell constriction, archaea only possess SepF-like homologs. Here, we combine structural, cellular, and evolutionary analyses to demonstrate that SepF is the FtsZ anchor in the human-associated archaeon Methanobrevibacter smithii. 3D super-resolution microscopy and quantitative analysis of immunolabeled cells show that SepF transiently co-localizes with FtsZ at the septum and possibly primes the future division plane. M. smithii SepF binds to membranes and to FtsZ, inducing filament bundling. High-resolution crystal structures of archaeal SepF alone and in complex with the FtsZ C-terminal domain (FtsZ<sub>CTD</sub>) reveal that SepF forms a dimer with a homodimerization interface driving a binding mode that is different from that previously reported in bacteria. Phylogenetic analyses of SepF and FtsZ from bacteria and archaea indicate that the two proteins may date back to the Last Universal Common Ancestor (LUCA), and we speculate that the archaeal mode of SepF/FtsZ interaction might reflect an ancestral feature. Our results provide insights into the mechanisms of archaeal cell division and pave the way for a better understanding of the processes underlying the divide between the two prokaryotic domains. FtsZ ; Archaea ; Cell division ; Division (mathematics) ; Biology ; Evolutionary biology ; Genetics ; Computational biology ; Cell biology ; Cell ; Bacteria Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; RNA and protein synthesis mechanisms
Authors: Weidong Qiu | Xinyi Cai | Mengke Li | Zijian Chen | Liangying Wang | Wentao Xie | Kunkun Liu | Ming Liu | Shi‐Jian Su
Source: The Journal of Physical Chemistry Letters (article, 2021-05-10)
Field: Materials Science — Materials Chemistry

Achieving Purely Organic Room-Temperature Phosphorescence Mediated by a Host–Guest Charge Transfer State Strategies for developing purely organic materials exhibiting both high efficiency and persistent room-temperature phosphorescence (RTP) have remained ambiguous and challenging. Herein, we propose that introducing an intermediate charge transfer (CT) state into the donor-acceptor binary molecular system holds promise for accomplishing this goal. Guest materials showing gradient ionization potentials were selected to fine-tune the intermolecularly formed CT state when doped into the same host material with a large electron affiliation potential. Such a CT intermediate state accelerates the population of the triplet exciton to benefit phosphorescent emission and decreases the phosphorescence lifetime via quenching the long-lived triplet excitons. As a result, a "trade-off" between a long phosphorescence lifetime (595 ms) and a high phosphorescent quantum yield (27.5%) can be obtained by tuning the host-guest energy gap offset. This finding highlights the key role of CT in RTP emission and provides new guidance for developing novel RTP systems. Phosphorescence ; Quenching (fluorescence) ; Triplet state ; Persistent luminescence ; Acceptor ; Photochemistry ; Materials science ; Exciton ; Quantum yield ; Optoelectronics ; Chemistry ; Chemical physics ; Luminescence ; Fluorescence ; Molecule ; Physics ; Optics ; Condensed matter physics ; Organic chemistry Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Perovskite Materials and Applications
Authors: Daniil V. Kladko | Aleksandra S. Falchevskaya | Nikita Serov | Artur Y. Prilepskii
Source: International Journal of Molecular Sciences (review, 2021-05-17)
Field: Materials Science — Materials Chemistry

Nanomaterial Shape Influence on Cell Behavior Nanomaterials are proven to affect the biological activity of mammalian and microbial cells profoundly. Despite this fact, only surface chemistry, charge, and area are often linked to these phenomena. Moreover, most attention in this field is directed exclusively at nanomaterial cytotoxicity. At the same time, there is a large body of studies showing the influence of nanomaterials on cellular metabolism, proliferation, differentiation, reprogramming, gene transfer, and many other processes. Furthermore, it has been revealed that in all these cases, the shape of the nanomaterial plays a crucial role. In this paper, the mechanisms of nanomaterials shape control, approaches toward its synthesis, and the influence of nanomaterial shape on various biological activities of mammalian and microbial cells, such as proliferation, differentiation, and metabolism, as well as the prospects of this emerging field, are reviewed. Nanomaterials ; Reprogramming ; Nanotechnology ; Cellular metabolism ; Chemistry ; Cell ; Cell biology ; Biology ; Materials science ; Metabolism ; Biochemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis