jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kjell Jorner | Anna Tomberg | Christoph Bauer | Christian Sköld | Per‐Ola Norrby
Source: Nature Reviews Chemistry (review, 2021-03-16)
Field: Materials Science — Materials Chemistry

Organic reactivity from mechanism to machine learning Reactivity (psychology) ; Computer science ; Perspective (graphical) ; Field (mathematics) ; Component (thermodynamics) ; Quantum ; Machine learning ; Artificial intelligence ; Mechanism (biology) ; Mathematics ; Epistemology ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Shrey Sindhwani | Warren C. W. Chan
Source: Journal of Internal Medicine (review, 2021-01-22)
Field: Materials Science — Biomaterials

Nanotechnology for modern medicine: next step towards clinical translation The field of nanotechnology has been a significant research focus in the last thirty years. This emphasis is due to the unique optical, electrical, magnetic, chemical and biological properties of materials approximately ten thousand times smaller than the diameter of a hair strand. Researchers have developed methods to synthesize and characterize large libraries of nanomaterials and have demonstrated their preclinical utility. We have entered a new phase of nanomedicine development, where the focus is to translate these technologies to benefit patients. This review article provides an overview of nanomedicine's unique properties, the current state of the field, and discusses the challenge of clinical translation. Finally, we discuss the need to build and strengthen partnerships between engineers and clinicians to create a feedback loop between the bench and bedside. This partnership will guide fundamental studies on the nanoparticle-biological interactions, address clinical challenges and change the development and evaluation of new drug delivery systems, sensors, imaging agents and therapeutic systems. Medicine ; Translation (biology) ; Nanotechnology Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Li Li | Stephan Hoyer | Ryan Pederson | Ruoxi Sun | Ekin D. Cubuk | Patrick Riley | Kieron Burke
Source: Physical Review Letters (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics Including prior knowledge is important for effective machine learning models in physics and is usually achieved by explicitly adding loss terms or constraints on model architectures. Prior knowledge embedded in the physics computation itself rarely draws attention. We show that solving the Kohn-Sham equations when training neural networks for the exchange-correlation functional provides an implicit regularization that greatly improves generalization. Two separations suffice for learning the entire one-dimensional H_{2} dissociation curve within chemical accuracy, including the strongly correlated region. Our models also generalize to unseen types of molecules and overcome self-interaction error. Computation ; Dissociation (chemistry) ; Generalization ; Regularization (linguistics) ; Artificial neural network ; Generalization error ; Machine learning ; Computer science ; Models of neural computation ; Artificial intelligence ; Training set ; Physics ; Statistical physics ; Algorithm ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Quan Zong | Chaofeng Liu | Hui Yang | Qilong Zhang | Guozhong Cao
Source: Nano Today (article, 2021-05-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tailoring nanostructured transition metal phosphides for high-performance hybrid supercapacitors Supercapacitor ; Materials science ; Electrochemistry ; Nanotechnology ; Electrode ; Nanostructure ; Battery (electricity) ; Energy storage ; Transition metal ; Power density ; Power (physics) ; Chemistry ; Catalysis Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Nadja Leibl | Karsten Haupt | Carlo Gonzato | Luminita Duma
Source: Chemosensors (review, 2021-05-26)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymers for Chemical Sensing: A Tutorial Review The field of molecularly imprinted polymer (MIP)-based chemosensors has been experiencing constant growth for several decades. Since the beginning, their continuous development has been driven by the need for simple devices with optimum selectivity for the detection of various compounds in fields such as medical diagnosis, environmental and industrial monitoring, food and toxicological analysis, and, more recently, the detection of traces of explosives or their precursors. This review presents an overview of the main research efforts made so far for the development of MIP-based chemosensors, critically discusses the pros and cons, and gives perspectives for further developments in this field. Molecularly imprinted polymer ; Nanotechnology ; Computer science ; Molecular imprinting ; Biochemical engineering ; Process engineering ; Environmental science ; Materials science ; Chemistry ; Engineering ; Selectivity ; Organic chemistry Analytical chemistry methods development ; Advanced Chemical Sensor Technologies ; Electrochemical Analysis and Applications
Authors: Bokai Liu | N. Vu‐Bac | Timon Rabczuk
Source: Composite Structures (article, 2021-06-15)
Field: Materials Science — Materials Chemistry

A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms Thermal conductivity ; Particle swarm optimization ; Artificial neural network ; Volume fraction ; Materials science ; Nanocomposite ; Algorithm ; Composite number ; Artificial intelligence ; Machine learning ; Computer science ; Biological system ; Composite material Thermal properties of materials ; Composite Material Mechanics ; Carbon Nanotubes in Composites
Authors: Sina Kaabipour | Shohreh Hemmati
Source: Beilstein Journal of Nanotechnology (review, 2021-01-25)
Field: Materials Science — Materials Chemistry

A review on the green and sustainable synthesis of silver nanoparticles and one-dimensional silver nanostructures The significance of silver nanostructures has been growing considerably, thanks to their ubiquitous presence in numerous applications, including but not limited to renewable energy, electronics, biosensors, wastewater treatment, medicine, and clinical equipment. The properties of silver nanostructures, such as size, size distribution, and morphology, are strongly dependent on synthesis process conditions such as the process type, equipment type, reagent type, precursor concentration, temperature, process duration, and pH. Physical and chemical methods have been among the most common methods to synthesize silver nanostructures; however, they possess substantial disadvantages and short-comings, especially compared to green synthesis methods. On the contrary, the number of green synthesis techniques has been increasing during the last decade and they have emerged as alternative routes towards facile and effective synthesis of silver nanostructures with different morphologies. In this review, we have initially outlined the most common and popular chemical and physical methodologies and reviewed their advantages and disadvantages. Green synthesis methodologies are then discussed in detail and their advantages over chemical and physical methods have been noted. Recent studies are then reviewed in detail and the effects of essential reaction parameters, such as temperature, pH, precursor, and reagent concentration, on silver nanostructure size and morphology are discussed. Also, green synthesis techniques used for the synthesis of one-dimensional (1D) silver nanostructures have been reviewed, and the potential of alternative green reagents for their synthesis has been discussed. Furthermore, current challenges regarding the green synthesis of 1D silver nanostructures and future direction are outlined. To sum up, we aim to show the real potential of green nanotechnology towards the synthesis of silver nanostructures with various morphologies (especially 1D ones) and the possibility of altering current techniques towards more environmentally friendly, more energy-efficient, less hazardous, simpler, and cheaper procedures. Nanostructure ; Nanotechnology ; Silver nanoparticle ; Reagent ; Materials science ; Nanomaterials ; Green chemistry ; Nanoparticle ; Renewable energy ; Chemistry ; Catalysis ; Reaction mechanism Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Domitille Mailley | Anne Hébraud | Guy Schlatter
Source: Macromolecular Materials and Engineering (review, 2021-04-19)
Field: Materials Science — Biomaterials

A Review on the Impact of Humidity during Electrospinning: From the Nanofiber Structure Engineering to the Applications Abstract Electrospinning is the process of choice for the elaboration of nanofibrous mats. During the process, a thin and continuous charged jet of a polymer solution is traveling from an emitter subjected to a high voltage toward a grounded collector. Although the duration of the jet travel is in the order of few tens of milliseconds, the physical interactions acting between the jet and the air play a key role on the resulting fiber morphology. These interactions mainly rely on the amount of water molecules in air. This review deals with the effect of humidity during electrospinning on solvent evaporation, the solidification rate of nanofibers and finally, on the morphology at length scales ranging from the non‐woven mat, the nanofiber itself down to the polymer crystal. Original electrospinning processes operating under specific environmental conditions as well as specificities encountered in needleless and free‐surface electrospinning dedicated to industrial‐scale mass production are also discussed. Then, it is shown how the control of humidity during electrospinning and the understanding of its influence on the fibrous structure can be exploited to target various applications dedicated to energy, environment, and health. Finally, current challenges and ideas for future research and new developments are presented. Electrospinning ; Materials science ; Nanofiber ; Polymer ; Humidity ; Evaporation ; Nanotechnology ; Jet (fluid) ; Composite material ; Mechanics Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Chaochao Gao | Xin Min | Minghao Fang | Tianyi Tao | Xiaohong Zheng | Yangai Liu | Xiaowen Wu | Zhaohui Huang
Source: Advanced Functional Materials (article, 2021-10-04)
Field: Materials Science — Materials Chemistry

Innovative Materials Science via Machine Learning Abstract Nowadays, the research on materials science is rapidly entering a phase of data‐driven age. Machine learning, one of the most powerful data‐driven methods, have been being applied to materials discovery and performances prediction with undoubtedly tremendous application foreground. Herein, the challenges and current progress of machine learning are summarized in materials science, the design strategies are classified and highlighted, and possible perspectives are proposed for the future development. It is hoped this review can provide important scientific guidance for innovating materials science and technology via machine learning in the future. Artificial intelligence ; Materials science ; Computer science ; Nanotechnology ; Machine learning ; Engineering ethics ; Data science ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mengyao Ouyang | Konstantinos G. Papanikolaou | Alexey Boubnov | Adam S. Hoffman | Georgios Giannakakis | Simon R. Bare | Michail Stamatakis | Maria Flytzani‐Stephanopoulos | E. Charles H. Sykes
Source: Nature Communications (article, 2021-03-09)
Field: Materials Science — Materials Chemistry

Directing reaction pathways via in situ control of active site geometries in PdAu single-atom alloy catalysts The atomic scale structure of the active sites in heterogeneous catalysts is central to their reactivity and selectivity. Therefore, understanding active site stability and evolution under different reaction conditions is key to the design of efficient and robust catalysts. Herein we describe theoretical calculations which predict that carbon monoxide can be used to stabilize different active site geometries in bimetallic alloys and then demonstrate experimentally that the same PdAu bimetallic catalyst can be transitioned between a single-atom alloy and a Pd cluster phase. Each state of the catalyst exhibits distinct selectivity for the dehydrogenation of ethanol reaction with the single-atom alloy phase exhibiting high selectivity to acetaldehyde and hydrogen versus a range of products from Pd clusters. First-principles based Monte Carlo calculations explain the origin of this active site ensemble size tuning effect, and this work serves as a demonstration of what should be a general phenomenon that enables in situ control over catalyst selectivity. In situ ; Catalysis ; Atom (system on chip) ; Active site ; Alloy ; Materials science ; Chemistry ; Metallurgy ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Taiki Miyazawa | Mayuko Itaya | Gregor Carpentero Burdeos | Kiyotaka Nakagawa | Teruo Miyazawa
Source: International Journal of Nanomedicine (review, 2021-06-01)
Field: Materials Science — Biomaterials

A Critical Review of the Use of Surfactant-Coated Nanoparticles in Nanomedicine and Food Nanotechnology Surfactants, whose existence has been recognized as early as 2800 BC, have had a long history with the development of human civilization. With the rapid development of nanotechnology in the latter half of the 20th century, breakthroughs in nanomedicine and food nanotechnology using nanoparticles have been remarkable, and new applications have been developed. The technology of surfactant-coated nanoparticles, which provides new functions to nanoparticles for use in the fields of nanomedicine and food nanotechnology, is attracting a lot of attention in the fields of basic research and industry. This review systematically describes these "surfactant-coated nanoparticles" through various sections in order: 1) surfactants, 2) surfactant-coated nanoparticles, application of surfactant-coated nanoparticles to 3) nanomedicine, and 4) food nanotechnology. Furthermore, current progress and problems of the technology using surfactant-coated nanoparticles through recent research reports have been discussed. Nanomedicine ; Nanotechnology ; Nanoparticle ; Pulmonary surfactant ; Materials science ; Engineering ; Chemical engineering Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Nanoparticles: synthesis and applications
Authors: Kevin Maik Jablonka | Giriprasad Melpatti Jothiappan | Shefang Wang | Berend Smit | Brian Yoo
Source: Nature Communications (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Bias free multiobjective active learning for materials design and discovery The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches. Leverage (statistics) ; Computer science ; Pareto principle ; Multi-objective optimization ; Set (abstract data type) ; Pareto optimal ; Active learning (machine learning) ; Mathematical optimization ; Machine learning ; Mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Machine Learning and Algorithms
Authors: Yasmin A. Mehanna | Emma Sadler | Rebekah L. Upton | Andrew G. Kempchinsky | Yao Lu | Colin R. Crick
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Materials Science — Surfaces, Coatings and Films

The challenges, achievements and applications of submersible superhydrophobic materials Superhydrophobic materials have been widely reported throughout the scientific literature. Their properties originate from a highly rough morphology and inherently water repellent surface chemistry. Despite promising an array of functionalities, these materials have seen limited commercial development. This could be attributed to many factors, like material compatibility, low physical resilience, scaling-up complications, etc. In applications where persistent water contact is required, another limitation arises as a major concern, which is the stability of the air layer trapped at the surface when submerged or impacted by water. This review is aimed at examining the diverse array of research focused on monitoring/improving air layer stability, and highlighting the most successful approaches. The reported complexity of monitoring and enhancing air layer stability, in conjunction with the variety of approaches adopted, results in an assortment of suggested routes to achieving success. The review is addressing the challenge of finding a balance between maximising water repulsion and incorporating structures that protect air pockets from removal, along with challenges related to the variant approaches to testing air-layer stability across the research field, and the gap between the achieved progress and the required performance in real-life applications. Framing (construction) ; Nanotechnology ; Engineering ; Architectural engineering ; Construction engineering ; Engineering ethics ; Systems engineering ; Computer science ; Materials science ; Civil engineering Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Aerogels and thermal insulation
Authors: Marjolein Dijkstra | Erik Luijten
Source: Nature Materials (review, 2021-05-27)
Field: Materials Science — Materials Chemistry

From predictive modelling to machine learning and reverse engineering of colloidal self-assembly Computer science ; Nanotechnology ; Colloidal crystal ; Colloid ; Materials science ; Engineering Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Enhanced Oil Recovery Techniques
Authors: Zekun Ren | Siyu Tian | Juhwan Noh | Felipe Oviedo | Guangzong Xing | Jiali Li | Qiaohao Liang | Ruiming Zhu | Armin G. Aberle | Shijing Sun | Xiaonan Wang | Yi Liu | Qianxiao Li | J. Senthilnath | Kedar Hippalgaonkar | Yousung Jung | Tonio Buonassisi
Source: Matter (article, 2021-12-20)
Field: Materials Science — Materials Chemistry

An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties Inverse ; Invertible matrix ; Representation (politics) ; Crystal (programming language) ; Set (abstract data type) ; Space (punctuation) ; Property (philosophy) ; Autoencoder ; Generative grammar ; Mathematics ; Materials science ; Computer science ; Crystallography ; Pure mathematics ; Chemistry ; Geometry ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Ferroelectric and Negative Capacitance Devices
Authors: Daniel Schwalbe‐Koda | Soonhyoung Kwon | Cecilia Paris | Estefanía Bello‐Jurado | Zach Jensen | Elsa Olivetti | Tom Willhammar | Avelino Corma | Yuriy Román‐Leshkov | Manuel Moliner | Rafael Gómez‐Bombarelli
Source: Science (article, 2021-09-16)
Field: Chemistry — Inorganic Chemistry

A priori control of zeolite phase competition and intergrowth with high-throughput simulations Zeolites are versatile catalysts and molecular sieves with large topological diversity, but managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this work, we controlled phase selectivity in templated zeolite synthesis from first principles by combining high-throughput atomistic simulations, literature mining, human-computer interaction, synthesis, and characterization. Proposed binding metrics distilled from more than 586,000 zeolite-molecule simulations reproduced the extracted literature and rationalized framework competition in the design of organic structure-directing agents. Energetic, geometric, and electrostatic descriptors of template molecules were found to regulate synthetic accessibility windows and aluminum distributions in pure-phase zeolites. Furthermore, these parameters allowed us to realize an intergrowth zeolite through a single bi-selective template. The computation-first approach enables control of both zeolite synthesis and structure composition using a priori theoretical descriptors. Zeolite ; A priori and a posteriori ; Nucleation ; Competition (biology) ; Computer science ; Phase (matter) ; Biochemical engineering ; Throughput ; Selectivity ; Rational design ; Nanotechnology ; Chemistry ; Materials science ; Ecology ; Engineering ; Organic chemistry ; Catalysis ; Biology Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Vundrala Sumedha Reddy | Yilong Tian | Chuanqi Zhang | Zhen Ye | Kallol Roy | Amutha Chinnappan | Seeram Ramakrishna | Wei Liu | Rituparna Ghosh
Source: Polymers (review, 2021-10-29)
Field: Materials Science — Biomaterials

A Review on Electrospun Nanofibers Based Advanced Applications: From Health Care to Energy Devices Electrospun nanofibers have been exploited in multidisciplinary fields with numerous applications for decades. Owing to their interconnected ultrafine fibrous structure, high surface-to-volume ratio, tortuosity, permeability, and miniaturization ability along with the benefits of their lightweight, porous nanofibrous structure, they have been extensively utilized in various research fields for decades. Electrospun nanofiber technologies have paved unprecedented advancements with new innovations and discoveries in several fields of application including energy devices and biomedical and environmental appliances. This review article focused on providing a comprehensive overview related to the recent advancements in health care and energy devices while emphasizing on the importance and uniqueness of utilizing nanofibers. A brief description regarding the effect of electrospinning techniques, setup modifications, and parameters optimization on the nanofiber morphology was also provided. The article is concluded with a short discussion on current research challenges and future perspectives. Nanofiber ; Electrospinning ; Nanotechnology ; Materials science ; Miniaturization ; Polymer ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications