jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kai Guo | Zhenze Yang | Chi‐Hua Yu | Markus J. Buehler
Source: Materials Horizons (review, 2020-12-17)
Field: Materials Science — Materials Chemistry

Artificial intelligence and machine learning in design of mechanical materials Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field. Mechanical design ; Materials science ; Machine design ; Artificial intelligence ; Nanotechnology ; Mechanical engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced machining processes and optimization
Authors: Annemie Bogaerts | Xin Tu | J. Christopher Whitehead | Gabriele Centi | Leon Lefferts | Olivier Guaitella | Federico Azzolina-Jury | Hyun‐Ha Kim | Anthony B. Murphy | William F. Schneider | Tomohiro Nozaki | Jason C. Hicks | Antoine Rousseau | Frédéric Thévenet | Ahmed Khacef | Maria L. Carreon
Source: Journal of Physics D Applied Physics (article, 2020-08-17)
Field: Materials Science — Materials Chemistry

The 2020 plasma catalysis roadmap Abstract Plasma catalysis is gaining increasing interest for various gas conversion applications, such as CO 2 conversion into value-added chemicals and fuels, CH 4 activation into hydrogen, higher hydrocarbons or oxygenates, and NH 3 synthesis. Other applications are already more established, such as for air pollution control, e.g. volatile organic compound remediation, particulate matter and NO x removal. In addition, plasma is also very promising for catalyst synthesis and treatment. Plasma catalysis clearly has benefits over ‘conventional’ catalysis, as outlined in the Introduction. However, a better insight into the underlying physical and chemical processes is crucial. This can be obtained by experiments applying diagnostics, studying both the chemical processes at the catalyst surface and the physicochemical mechanisms of plasma-catalyst interactions, as well as by computer modeling. The key challenge is to design cost-effective, highly active and stable catalysts tailored to the plasma environment. Therefore, insight from thermal catalysis as well as electro- and photocatalysis is crucial. All these aspects are covered in this Roadmap paper, written by specialists in their field, presenting the state-of-the-art, the current and future challenges, as well as the advances in science and technology needed to meet these challenges. Catalysis ; Oxygenate ; Nanotechnology ; Nonthermal plasma ; Chemistry ; Environmental remediation ; Biochemical engineering ; Plasma ; Materials science ; Organic chemistry ; Engineering ; Contamination ; Physics Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Ammonia Synthesis and Nitrogen Reduction
Authors: Hui Shao | Yih‐Chyng Wu | Zifeng Lin | Pierre‐Louis Taberna | Patrice Simon
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanoporous carbon for electrochemical capacitive energy storage The urgent need for efficient energy storage devices has stimulated a great deal of research on electrochemical double layer capacitors (EDLCs). This review aims at summarizing the recent progress in nanoporous carbons, as the most commonly used EDLC electrode materials in the field of capacitive energy storage, from the viewpoint of materials science and characterization techniques. We discuss the key advances in the fundamental understanding of the charge storage mechanism in nanoporous carbon-based electrodes, including the double layer formation in confined nanopores. Special attention will be also paid to the important development of advanced in situ analytical techniques as well as theoretical studies to better understand the carbon pore structure, electrolyte ion environment and ion fluxes in these confined pores. We also highlight the recent progress in advanced electrolytes for EDLCs. The better understanding of the charge storage mechanism of nanoporous carbon-based electrodes and the rational design of electrolytes should shed light on developing the next-generation of EDLCs. Nanoporous ; Energy storage ; Electrolyte ; Nanotechnology ; Supercapacitor ; Materials science ; Nanopore ; Electrochemistry ; Capacitor ; Capacitive sensing ; Electrode ; Carbon fibers ; Electrochemical energy storage ; Computer science ; Voltage ; Chemistry ; Electrical engineering ; Engineering ; Composite material ; Power (physics) ; Physics Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Chiyui Ahn
Source: npj 2D Materials and Applications (article, 2020-06-18)
Field: Materials Science — Materials Chemistry

2D materials for spintronic devices Abstract 2D materials are attractive for nanoelectronics due to their ultimate thickness dimension and unique physical properties. A wide variety of emerging spintronic device concepts will greatly benefit from the use of 2D materials, leading a better way to manipulating spin. In this review, we discuss various 2D materials, including graphene and other inorganic 2D semiconductors, in the context of scientific and technological advances in spintronic devices. Applications of 2D materials in spin logic switches, spin valves, and spin transistors are specifically investigated. We also introduce the spin-orbit and spin-valley coupled properties of 2D materials to explore their potential to address the crucial issues of contemporary electronics. Finally, we highlight major challenges in integrating 2D materials into spintronic devices and provide a future perspective on 2D materials for spin logic devices. Spintronics ; Nanoelectronics ; Nanotechnology ; Context (archaeology) ; Electronics ; Materials science ; Spin (aerodynamics) ; Graphene ; Engineering physics ; Computer science ; Engineering ; Electrical engineering ; Physics ; Ferromagnetism ; Condensed matter physics ; Mechanical engineering 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Christoph B. Messner | Vadim Demichev | Daniel Wendisch | Laura Michalick | Matthew White | Anja Freiwald | Kathrin Textoris‐Taube | Spyros I. Vernardis | Anna-Sophia Egger | Marco Kreidl | Daniela Ludwig | Christiane Kilian | Federica Agostini | Aleksej Zelezniak | Charlotte Thibeault | Moritz Pfeiffer | Stefan Hippenstiel | Andreas C. Hocke | Christof von Kalle | Archie Campbell | Caroline Hayward | David J. Porteous | Riccardo E. Marioni | Claudia Langenberg | Kathryn S. Lilley | Wolfgang M. Kuebler | Michael Mülleder | Christian Drosten | Norbert Suttorp | Martin Witzenrath | Florian Kurth | Leif Erik Sander | Markus Ralser
Source: Cell Systems (article, 2020-06-01)
Field: Chemistry — Spectroscopy

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets. Coronavirus disease 2019 (COVID-19) ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Throughput ; Pandemic ; Proteomics ; Standardization ; Workflow ; Computer science ; Point of care ; 2019-20 coronavirus outbreak ; Computational biology ; Medicine ; Bioinformatics ; Biology ; Virology ; Internal medicine ; Pathology ; Disease Advanced Proteomics Techniques and Applications ; SARS-CoV-2 detection and testing ; COVID-19 Clinical Research Studies
Authors: Bohayra Mortazavi | Brahmanandam Javvaji | Fazel Shojaei | Timon Rabczuk | Alexander V. Shapeev | Xiaoying Zhuang
Source: Nano Energy (article, 2020-12-29)
Field: Materials Science — Materials Chemistry

Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles Chemical vapor deposition has been most recently employed to fabricate centimeter-scale high-quality single-layer MoSi 2 N 4 ( Science; 2020;369; 670 ). Motivated by this exciting experimental advance, herein we conduct extensive first-principles based simulations to explore the stability, mechanical properties, lattice thermal conductivity , piezoelectric and flexoelectric response, and photocatalytic and electronic features of MA 2 Z 4 (M = Cr, Mo, W; A = Si, Ge; Z = N, P) monolayers. The considered nanosheets are found to exhibit dynamical stability and remarkably high mechanical properties. Moreover, they show diverse electronic properties from antiferromagnetic metal to half metal and to semiconductors with band gaps ranging from 0.31 to 2.57 eV. Among the studied nanosheets , the MoSi 2 N 4 and WSi 2 N 4 monolayers yield appropriate band edge positions, high electron and hole mobilities, and strong visible light absorption , highly promising for applications in optoelectronics and photocatalytic water splitting. The MoSi 2 N 4 and WSi 2 N 4 monolayers are also predicted to show outstandingly high lattice thermal conductivity of 440 and 500 W/mK, respectively. For the first time we show that machine learning interatomic potentials trained over small supercells can be employed to examine the flexoelectric and piezoelectric properties of complex structures. As the most exciting finding, WSi 2 N 4 , CrSi 2 N 4 and MoSi 2 N 4 are found to exhibit the highest piezoelectric coefficients, outperforming all other-known 2D materials . Our results highlight that MA 2 Z 4 nanosheets not only undoubtedly outperform the transition metal dichalcogenides family but also can compete with graphene for applications in nanoelectronics, optoelectronic, energy storage/conversion and thermal management systems. • The intrinsic properties of MA 2 Z 4 (M = Cr, Mo, W; A = Si, Ge; Z = N, P) monolayers are explored. • An accurate methodology for the examination of flexoelectric properties is proposed using machine learning interatomic potentials. • WSi 2 N 4 , CrSi 2 N 4 and MoSi 2 N 4 monolayers exhibit the highest piezoelectric coefficients among all 2D materials . • MoSi 2 N 4 and WSi 2 N 4 monolayers show outstandingly high lattice thermal conductivity and mechanical strength. • MoSi 2 N 4 and WSi 2 N 4 nanosheets are promising for the applications in optoelectronics and photocatalytic water splitting. Materials science ; Monolayer ; Semiconductor ; Piezoelectricity ; Photocatalysis ; Condensed matter physics ; Chemical vapor deposition ; Graphene ; Thermal stability ; MXenes ; Optoelectronics ; Nanotechnology ; Composite material ; Chemical engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Marc Guerre | Christian Taplan | Johan M. Winne | Filip Du Prez
Source: Chemical Science (review, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Vitrimers: directing chemical reactivity to control material properties The development of more sustainable materials with a prolonged useful lifetime is a key requirement for a transition towards a more circular economy. However, polymer materials that are long-lasting and highly durable also tend to have a limited application potential for re-use. This is because such materials derive their durable properties from a high degree of chemical connectivity, resulting in rigid meshes or networks of polymer chains with a high intrinsic resistance to deformation. Once such polymers are fully synthesised, thermal (re)processing becomes hard (or impossible) to achieve without damaging the degree of chemical connectivity, and most recycling options quickly lead to a drop or even loss of material properties. In this context, both academic and industrial researchers have taken a keen interest in materials design that combines high degrees of chemical connectivity with an improved thermal (re)processability, mediated through a dynamic exchange reaction of covalent bonds. In particular vitrimer materials offer a promising concept because they completely maintain their degree of chemical connectivity at all times, yet can show a clear thermally driven plasticity and liquid behavior, enabled through rapid bond rearrangement reactions within the network. In the past decade, many suitable dynamic covalent chemistries were developed to create vitrimer materials, and are now applicable to a wide range of polymer matrices. The material properties of vitrimers, however, do not solely rely on the chemical structure of the polymer matrix, but also on the chemical reactivity of the dynamic bonds. Thus, chemical reactivity considerations become an integral part of material design, which has to take into account for example catalytic and cross-reactivity effects. This mini-review will aim to provide an overview of recent efforts aimed at understanding and controlling dynamic cross-linking reactions within vitrimers, and how directing this chemical reactivity can be used as a handle to steer material properties. Hence, it is shown how a focus on a fundamental chemical understanding can pave the way towards new sustainable materials and applications. Reactivity (psychology) ; Control (management) ; Chemistry ; Computer science ; Artificial intelligence ; Medicine Polymer composites and self-healing ; Photochromic and Fluorescence Chemistry ; Advanced Polymer Synthesis and Characterization
Authors: Mario Krenn | Florian Häse | AkshatKumar Nigam | Pascal Friederich | Alan Aspuru-Guzik
Source: Machine Learning Science and Technology (article, 2020-07-27)
Field: Materials Science — Materials Chemistry

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation Abstract The discovery of novel materials and functional molecules can help to solve some of society’s most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering–generally denoted as inverse design–was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce S ELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100% robust. Every S ELFIES string corresponds to a valid molecule, and S ELFIES can represent every molecule. S ELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model’s internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models. String (physics) ; Intuition ; Representation (politics) ; Interpretation (philosophy) ; Task (project management) ; Generative model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Seyed Mohamad Moosavi | Aditya Nandy | Kevin Maik Jablonka | Daniele Ongari | Jon Paul Janet | Peter G. Boyd | Yongjin Lee | Berend Smit | Heather J. Kulik
Source: Nature Communications (article, 2020-08-13)
Field: Chemistry — Inorganic Chemistry

Understanding the diversity of the metal-organic framework ecosystem Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures. Chemical space ; Formalism (music) ; Computer science ; Metal-organic framework ; Diversity (politics) ; Biochemical engineering ; Nanotechnology ; Chemistry ; Bioinformatics ; Materials science ; Biology ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jaísa Oliveira Chaves | Mariana C. Souza | Laíse Capelasso da Silva | Daniel Lachos‐Perez | Paulo César Torres-Mayanga | Ana Paula da Fonseca Machado | Tânia Forster‐Carneiro | Mercedes Vázquez-Espinosa | Ana V. González-de-Peredo | Gerardo F. Barbero | Maurı́cio A. Rostagno
Source: Frontiers in Chemistry (review, 2020-09-25)
Field: Chemistry — Analytical Chemistry

Extraction of Flavonoids From Natural Sources Using Modern Techniques Flavonoids are one of the main groups of polyphenols found in natural products. Traditional flavonoid extraction techniques are being replaced by advanced techniques to reduce energy and solvent consumption, increase efficiency and selectivity, to meet increased market demand and environmental regulations. Advanced technologies, such as microwaves, ultrasound, pressurized liquids, supercritical fluids, and electric fields, are alternatives currently being used. These modern techniques are generally faster, more environmentally friendly, and with higher automation levels compared to conventional extraction techniques. This review will discuss the different methods available for flavonoid extraction from natural sources and the main parameters involved (temperature, solvent, sample quantity, extraction time, among others). Recent trends and their industrial importance are also discussed in detail, providing insight into their potential. Thus, this paper seeks to review the innovations of compound extraction techniques, presenting in each of them their advantages and disadvantages, trying to offer a broader scope in the understanding of flavonoid extraction from different plant matrices. Extraction (chemistry) ; Natural (archaeology) ; Chromatography ; Chemistry ; Biochemical engineering ; Traditional medicine ; Engineering ; Geography ; Medicine Chromatography in Natural Products ; Natural product bioactivities and synthesis ; Medicinal Plant Pharmacodynamics Research
Authors: Alexis Goulet‐Hanssens | Fabian Eisenreich | Stefan Hecht
Source: Advanced Materials (review, 2020-01-24)
Field: Materials Science — Materials Chemistry

Enlightening Materials with Photoswitches Incorporating molecular photoswitches into various materials provides unique opportunities for controlling their properties and functions with high spatiotemporal resolution using remote optical stimuli. The great and largely still untapped potential of these photoresponsive systems has not yet been fully exploited due to the fundamental challenges in harnessing geometrical and electronic changes on the molecular level to modulate macroscopic and bulk material properties. Herein, progress made during the past decade in the field of photoswitchable materials is highlighted. After pointing to some general design principles, materials with an increasing order of the integrated photoswitchable units are discussed, spanning the range from amorphous settings over surfaces/interfaces and supramolecular ensembles, to liquid crystalline and crystalline phases. Finally, some potential future directions are pointed out in the conclusion. In view of the exciting recent achievements in the field, the future emergence and further development of light-driven and optically programmable (inter)active materials and systems are eagerly anticipated. Materials science ; Nanotechnology Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Polydiacetylene-based materials and applications