jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuecheng Shao | Jian Lv | Peng Liu | Sen Shao | Pengyue Gao | Hanyu Liu | Yanchao Wang | Yanming Ma
Source: The Journal of Chemical Physics (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

A symmetry-orientated divide-and-conquer method for crystal structure prediction Crystal structure prediction has been a subject of topical interest but remains a substantial challenge especially for complex structures as it deals with the global minimization of the extremely rugged high-dimensional potential energy surface. In this paper, a symmetry-orientated divide-and-conquer scheme was proposed to construct a symmetry tree graph, where the entire search space is decomposed into a finite number of symmetry dependent subspaces. An artificial intelligence-based symmetry selection strategy was subsequently devised to select the low-lying subspaces with high symmetries for global exploration and in-depth exploitation. Our approach can significantly simplify the problem of crystal structure prediction by avoiding exploration of the most complex P1 subspace on the entire search space and has the advantage of preserving the crystal symmetry during structure evolution, making it well suitable for predicting the complex crystal structures. The effectiveness of the method has been validated by successful prediction of the candidate structures of binary Lennard-Jones mixtures and the high-pressure phase of ice, containing more than 100 atoms in the simulation cell. The work therefore opens up an opportunity toward achieving the long-sought goal of crystal structure prediction of complex systems. Linear subspace ; Symmetry (geometry) ; Divide and conquer algorithms ; Homogeneous space ; Subspace topology ; Crystal structure prediction ; Computer science ; Binary number ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Theoretical physics ; Mathematics ; Crystal structure ; Statistical physics ; Physics ; Geometry ; Chemistry ; Crystallography ; Arithmetic Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Josny Joy | Anand Krishnamoorthy | Ashish R. Tanna | Vishal Kamathe | Rupali Nagar | Sesha S. Srinivasan
Source: Applied Sciences (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

Recent Developments on the Synthesis of Nanocomposite Materials via Ball Milling Approach for Energy Storage Applications This review is focused on the topical developments in the synthesis of nanocomposites using the simplest top-down approach, mechanochemical milling, and the related aspects of the interfacial interactions. Milling constraints include time duration of milling, ball size, the ball-to-sample content proportion, rotation speed, and energy that took part in a vital part of the structure–property relationships and composite interactions. Milled nanocomposites are being used in different structural applications for their higher performance rate and throughput. The synthesis of different nanocomposites and the effect of various parameters on the mill-ability of nanocomposites are discussed. Moreover, some of the major advancements in the energy sector are discussed in the latter part of the review. Ball mill ; Nanocomposite ; Materials science ; Ball (mathematics) ; Nanotechnology ; Composite material ; Mechanical engineering ; Engineering ; Mathematics Graphene research and applications ; Aluminum Alloys Composites Properties ; Carbon Nanotubes in Composites
Authors: Dávid Kovács | Nóra Igaz | Mohana Krishna Gopisetty | Mónika Kiricsi
Source: International Journal of Molecular Sciences (review, 2022-01-13)
Field: Materials Science — Materials Chemistry

Cancer Therapy by Silver Nanoparticles: Fiction or Reality? As an emerging new class, metal nanoparticles and especially silver nanoparticles hold great potential in the field of cancer biology. Due to cancer-specific targeting, the consequently attenuated side-effects and the massive anti-cancer features render nanoparticle therapeutics desirable platforms for clinically relevant drug development. In this review, we highlight those characteristics of silver nanoparticle-based therapeutic concepts that are unique, exploitable, and achievable, as well as those that represent the critical hurdle in their advancement to clinical utilization. The collection of findings presented here will describe the features that distinguish silver nanoparticles from other anti-cancer agents and display the realistic opportunities and implications in oncotherapeutic innovations to find out whether cancer therapy by silver nanoparticles is fiction or reality. Silver nanoparticle ; Nanotechnology ; Cancer therapy ; Cancer ; Nanoparticle ; Cancer treatment ; Computer science ; Medicine ; Materials science ; Internal medicine Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications
Authors: Zhengkai Tu | Thijs Stuyver | Connor W. Coley
Source: Chemical Science (review, 2022-11-28)
Field: Materials Science — Materials Chemistry

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry. Software deployment ; Computer science ; Chemistry ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Engineering ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jiheng Fang | Ming Xie | Xingqun He | Jiming Zhang | Jieqiong Hu | Yongtai Chen | Youcai Yang | Qinglin Jin
Source: Materials Today Communications (article, 2022-11-09)
Field: Materials Science — Materials Chemistry

Machine learning accelerates the materials discovery Machine learning ; Computer science ; Artificial intelligence ; Process (computing) ; Relevance (law) ; Identification (biology) ; Computational learning theory ; Scale (ratio) ; Active learning (machine learning) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Iron and Steelmaking Processes
Authors: Yifei He | Nadzeya A. Kukhta | Adam Marks | Christine K. Luscombe
Source: Journal of Materials Chemistry C (review, 2022-01-01)
Field: Materials Science — Polymers and Plastics

The effect of side chain engineering on conjugated polymers in organic electrochemical transistors for bioelectronic applications Bioelectronics focuses on the establishment of the connection between the ion-driven biosystems and readable electronic signals. Organic electrochemical transistors (OECTs) offer a viable solution for this task. Organic mixed ionic/electronic conductors (OMIECs) rest at the heart of OECTs. The balance between the ionic and electronic conductivities of OMIECs is closely connected to the OECT device performance. While modification of the OMIECs' electronic properties is largely related to the development of conjugated scaffolds, properties such as ion permeability, solubility, flexibility, morphology, and sensitivity can be altered by side chain moieties. In this review, we uncover the influence of side chain molecular design on the properties and performance of OECTs. We summarise current understanding of OECT performance and focus specifically on the knowledge of ionic-electronic coupling, shedding light on the significance of side chain development of OMIECs. We show how the versatile synthetic toolbox of side chains can be successfully employed to tune OECT parameters <i>via</i> controlling the material properties. As the field continues to mature, more detailed investigations into the crucial role side chain engineering plays on the resultant OMIEC properties will allow for side chain alternatives to be developed and will ultimately lead to further enhancements within the field of OECT channel materials. Materials science ; Conjugated system ; Side chain ; Polymer ; Transistor ; Electrochemistry ; Chain (unit) ; Nanotechnology ; Organic semiconductor ; Thin-film transistor ; Optoelectronics ; Electrical engineering ; Composite material ; Electrode ; Engineering ; Chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Analytical Chemistry and Sensors
Authors: Yanpei Song | Joshua Phipps | Changjia Zhu | Shengqian Ma
Source: Angewandte Chemie International Edition (review, 2022-12-20)
Field: Materials Science — Materials Chemistry

Porous Materials for Water Purification Water pollution is a growing threat to humanity due to the pervasiveness of contaminants in water bodies. Significant efforts have been made to separate these hazardous components to purify polluted water through various methods. However, conventional remediation methods suffer from limitations such as low uptake capacity or selectivity, and current water quality standards cannot be met. Recently, advanced porous materials (APMs) have shown promise in improved segregation of contaminants compared to traditional porous materials in uptake capacity and selectivity. These materials feature merits of high surface area and versatile functionality, rendering them ideal platforms for the design of novel adsorbents. This Review summarizes the development and employment of APMs in a variety of water treatments accompanied by assessments of task-specific adsorption performance. Finally, we discuss our perspectives on future opportunities for APMs in water purification. Environmental remediation ; Environmental science ; Adsorption ; Water quality ; Contamination ; Hazardous waste ; Biochemical engineering ; Process engineering ; Waste management ; Chemistry ; Engineering Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Silvan Käser | Luis Itza Vazquez-Salazar | Markus Meuwly | Kai Töpfer
Source: Digital Discovery (review, 2022-12-21)
Field: Materials Science — Materials Chemistry

Neural network potentials for chemistry: concepts, applications and prospects Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale. Computer science ; Artificial neural network ; Representation (politics) ; Field (mathematics) ; Reliability (semiconductor) ; Artificial intelligence ; Scale (ratio) ; Machine learning ; Biochemical engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xiaozhang Chen | Yuan Xue | Yibo Sun | Jiabin Shen | Sannian Song | Min Zhu | Zhitang Song | Zengguang Cheng | Peng Zhou
Source: Advanced Materials (article, 2022-06-17)
Field: Materials Science — Materials Chemistry

Neuromorphic Photonic Memory Devices Using Ultrafast, Non‐Volatile Phase‐Change Materials The search for ultrafast photonic memory devices is inspired by the ever-increasing number of cloud-computing, supercomputing, and artificial-intelligence applications, together with the unique advantages of signal processing in the optical domain such as high speed, large bandwidth, and low energy consumption. By embracing silicon photonics with chalcogenide phase-change materials (PCMs), non-volatile integrated photonic memory is developed with promising potential in photonic integrated circuits and nanophotonic applications. While conventional PCMs suffer from slow crystallization speed, scandium-doped antimony telluride (SST) has been recently developed for ultrafast phase-change random-access memory applications. An ultrafast non-volatile photonic memory based on an SST thin film with a 2 ns write/erase speed is demonstrated, which is the fastest write/erase speed ever reported in integrated phase-change photonic devices. SST-based photonic memories exhibit multilevel capabilities and good stability at room temperature. By mapping the memory level to the biological synapse weight, an artificial neural network based on photonic memory devices is successfully established for image classification. Additionally, a reflective nanodisplay application using SST with optoelectronic modulation capabilities is demonstrated. Both the optical and electrical changes in SST during the phase transition and the fast-switching speed demonstrate their potential for use in photonic computing, neuromorphic computing, nanophotonics, and optoelectronic applications. Photonics ; Neuromorphic engineering ; Materials science ; Ultrashort pulse ; Optoelectronics ; Phase-change memory ; Nanophotonics ; Optical switch ; Computer science ; Silicon photonics ; Nanotechnology ; Laser ; Optics ; Artificial neural network ; Physics Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Advanced Memory and Neural Computing
Authors: Riduan Khaddam-Aljameh | Miloš Stanisavljević | Jordi Fornt | Geethan Karunaratne | Matthias Brändli | Feng Liu | Abhairaj Singh | Silvia M. Müller | Urs Egger | Αναστάσιος Πετρόπουλος | Theodore Antonakopoulos | Kevin Brew | S. Choi | Injo Ok | Fee Li Lie | Nicole Saulnier | V. Chan | Ishtiaq Ahsan | Vijay Narayanan | S. R. Nandakumar | Manuel Le Gallo | Pier Andrea Francese | Abu Sebastian | Evangelos Eleftheriou
Source: IEEE Journal of Solid-State Circuits (article, 2022-01-28)
Field: Materials Science — Materials Chemistry

HERMES-Core—A 1.59-TOPS/mm <sup>2</sup> PCM on 14-nm CMOS In-Memory Compute Core Using 300-ps/LSB Linearized CCO-Based ADCs We present a 256 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 256 in-memory compute (IMC) core designed and fabricated in 14-nm CMOS technology with backend-integrated multi-level phase change memory (PCM). It comprises 256 linearized current-controlled oscillator (CCO)-based A/D converters (ADCs) at a compact 4- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> pitch and a local digital processing unit (LDPU) performing affine scaling and ReLU operations. A frequency-linearization technique for CCO is introduced, which increases the maximum CCO frequency beyond 3 GHz, while ensuring accurate on-chip matrix–vector multiplications (MVMs). Moreover, the design and functionality of the digital ADC calibration procedure is described in detail and the MVM accuracy is quantified. Finally, the measured classification accuracies of deep learning (DL) inference applications on the MNIST and CIFAR-10 datasets, when two IMC cores are employed, are presented. For a performance density of 1.59 TOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a measured energy efficiency of 10.5 TOPS/W, at a main clock frequency of 1 GHz, is achieved. CMOS ; MNIST database ; Algorithm ; Scaling ; Computer science ; Mathematics ; Arithmetic ; Computer hardware ; Electrical engineering ; Artificial intelligence ; Engineering ; Deep learning Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Semiconductor materials and devices
Authors: Lăcrămioara Popa | Mihaela Violeta Ghica | Elena-Emilia Tudoroiu | Diana-Georgiana Ionescu | Cristina‐Elena Dinu‐Pîrvu
Source: Materials (review, 2022-01-29)
Field: Materials Science — Biomaterials

Bacterial Cellulose—A Remarkable Polymer as a Source for Biomaterials Tailoring Nowadays, the development of new eco-friendly and biocompatible materials using 'green' technologies represents a significant challenge for the biomedical and pharmaceutical fields to reduce the destructive actions of scientific research on the human body and the environment. Thus, bacterial cellulose (BC) has a central place among these novel tailored biomaterials. BC is a non-pathogenic bacteria-produced polysaccharide with a 3D nanofibrous structure, chemically identical to plant cellulose, but exhibiting greater purity and crystallinity. Bacterial cellulose possesses excellent physicochemical and mechanical properties, adequate capacity to absorb a large quantity of water, non-toxicity, chemical inertness, biocompatibility, biodegradability, proper capacity to form films and to stabilize emulsions, high porosity, and a large surface area. Due to its suitable characteristics, this ecological material can combine with multiple polymers and diverse bioactive agents to develop new materials and composites. Bacterial cellulose alone, and with its mixtures, exhibits numerous applications, including in the food and electronic industries and in the biotechnological and biomedical areas (such as in wound dressing, tissue engineering, dental implants, drug delivery systems, and cell culture). This review presents an overview of the main properties and uses of bacterial cellulose and the latest promising future applications, such as in biological diagnosis, biosensors, personalized regenerative medicine, and nerve and ocular tissue engineering. Bacterial cellulose ; Cellulose ; Polymer ; Polymer science ; Materials science ; Chemical engineering ; Nanotechnology ; Composite material ; Engineering Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Nanoparticles: synthesis and applications
Authors: Maria Alvi | Azka Yaqoob | Kanwal Rehman | Syed Muhammad Shoaib | Muhammad Sajid Hamid Akash
Source: AAPS Open (article, 2022-08-01)
Field: Materials Science — Biomaterials

PLGA-based nanoparticles for the treatment of cancer: current strategies and perspectives Abstract Research on cancer treatment is always of great importance because of the extensive and difficult treatment options and side effects of chemotherapeutic agents. Due to this, novel techniques for cancer treatment are the need of the day. Nowadays, nanotechnology is of great interest for its applications as diagnostic tools, theragnostic, contrasting agents, and vehicles for delivering drugs. Nanoparticles (NPs) are made up of biocompatible and biodegradable polymers that improve the pharmacokinetic and pharmacodynamic properties of drugs, reduce side effects, improve stability, prolong the release of drug, and reduce the dosing frequency. Poly (lactic-co-glycolic acid) (PLGA) is FDA-approved synthetic polymer which can be used to formulate NPs that can be targeted to a specific site for the safe and effective delivery of drugs. PLGA-based NPs can be used for a variety of cancer therapies including tumor-targeted drug delivery, gene therapy, hyperthermia, and photodynamic therapy. This article discusses the method of preparation, characterization, encapsulation of chemotherapeutic drugs, effect of physicochemical properties of PLGA- based NPs, and how we can exploit these aspects through various methods of preparation for drug loading, biodistribution, target specificity, and their use in cancer treatment. Along with these targeting strategies, gene therapy, cancer immunotherapy, and various applications have also been discussed. This article also aims to discuss the incorporation of diagnostic tools and therapeutic moiety in one versatile formulation of PLGA-NPs and the difficulties faced in translating this promising tool to clinical use. PLGA ; Nanocarriers ; Biodistribution ; Cancer treatment ; Cancer therapy ; Nanotechnology ; Drug delivery ; Drug ; Cancer ; Pharmacology ; Medicine ; Materials science ; Nanoparticle ; In vivo ; Biotechnology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Lei Jiang | Huadong Fu | Hongtao Zhang | Jianxin Xie
Source: Acta Materialia (article, 2022-03-22)
Field: Materials Science — Materials Chemistry

Physical mechanism interpretation of polycrystalline metals’ yield strength via a data-driven method: A novel Hall–Petch relationship Materials science ; Crystallite ; Grain boundary ; Grain boundary strengthening ; Condensed matter physics ; Hall effect ; Grain size ; Electrical resistivity and conductivity ; Metallurgy ; Microstructure ; Physics Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Seyed Mohamad Moosavi | Balázs Álmos Novotny | Daniele Ongari | Elias Moubarak | Mehrdad Asgari | Özge Kadioglu | Charithea Charalambous | Andres Ortega‐Guerrero | Amir H. Farmahini | Lev Sarkisov | Susana García | Frank Noé | Berend Smit
Source: Nature Materials (article, 2022-10-13)
Field: Chemistry — Inorganic Chemistry

A data-science approach to predict the heat capacity of nanoporous materials Heat capacity ; Materials science ; Work (physics) ; Process engineering ; Flue gas ; Carbon fibers ; Nanoporous ; Sorbent ; Process (computing) ; Adsorption ; Computer science ; Thermodynamics ; Mechanical engineering ; Nanotechnology ; Composite material ; Waste management ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Wen Lei | Chen Yang | Yi Wu | Guoqing Ru | Xianglei He | Xiangmin Tong | Shibing Wang
Source: Journal of Nanobiotechnology (review, 2022-01-21)
Field: Materials Science — Biomaterials

Nanocarriers surface engineered with cell membranes for cancer targeted chemotherapy This review will describe the manufacturing processes for CMCNs and the therapeutic uses for different kinds of cell membrane-coated nanocarrier-based drug delivery systems, as well as addressing obstacles and future prospects. Nanocarriers ; Drug delivery ; Membrane ; Targeted drug delivery ; Cell ; Nanotechnology ; Cancer cell ; Cell membrane ; Chemistry ; Materials science ; Cancer ; Biology ; Biochemistry Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Mallikarjuna N. Nadagouda | Pooja Vijayasarathy | Aebin Sin | Heeyeon Nam | Sanaullah Khan | Javad B. M. Parambath | Ahmed A. Mohamed | Changseok Han
Source: Medicinal Chemistry Research (article, 2022-07-02)
Field: Chemistry — Organic Chemistry

Antimicrobial activity of quaternary ammonium salts: structure-activity relationship Antimicrobial ; Chemistry ; Antibacterial activity ; Ammonium ; Biofouling ; Biofilm ; Disinfectant ; Combinatorial chemistry ; Microbiology ; Bacteria ; Organic chemistry ; Biochemistry ; Biology Antimicrobial agents and applications ; Environmental Chemistry and Analysis ; Marine Biology and Environmental Chemistry