jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huachen Tao | Tianyi Wu | Matteo Aldeghi | Tony Wu | Alán Aspuru‐Guzik | Eugenia Kumacheva
Source: Nature Reviews Materials (review, 2021-07-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanoparticle synthesis assisted by machine learning Nanoparticle ; Nanotechnology ; Computer science ; Dispersity ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Felix Musil | Andrea Grisafi | Albert P. Bartók | Christoph Ortner | Gábor Csányi | Michele Ceriotti
Source: Chemical Reviews (article, 2021-07-26)
Field: Materials Science — Materials Chemistry

Physics-Inspired Structural Representations for Molecules and Materials The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable <i>representation</i>. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field. Set (abstract data type) ; Lead (geology) ; Cartesian coordinate system ; Molecule ; Current (fluid) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Musil, F | Grisafi, A | Bartók, AP | Ortner, C | Csányi, G | Ceriotti, M | ,
Source: Cambridge University Engineering Department Publications Database (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Physics-Inspired Structural Representations for Molecules and Materials The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field. Representation (politics) ; Chemistry ; Scale (ratio) ; Field (mathematics) ; Set (abstract data type) ; Management science ; Computer science ; Nanotechnology ; Data science ; Physics ; Mathematics ; Engineering ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Andrew Rosen | Shaelyn Iyer | Debmalya Ray | Zhenpeng Yao | Alán Aspuru‐Guzik | Laura Gagliardi | Justin M. Notestein | Randall Q. Snurr
Source: Matter (article, 2021-04-05)
Field: Materials Science — Materials Chemistry

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would be optimal for a given application. High-throughput computational screening and machine learning are promising routes to efficiently navigate the vast chemical space of MOFs but have rarely been used for the prediction of properties that need to be calculated by quantum mechanical methods. Here in this paper, we introduce the Quantum MOF (QMOF) database, a publicly available database of computed quantum-chemical properties for more than 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how machine learning models trained on the QMOF database can be used to rapidly discover MOFs with targeted electronic structure properties, using the prediction of theoretically computed band gaps as a representative example. We conclude by highlighting several MOFs predicted to have low band gaps, a challenging task given the electronically insulating nature of most MOFs. Chemical space ; Modular design ; Computer science ; Quantum chemical ; Metal-organic framework ; Quantum ; Task (project management) ; Nanotechnology ; Artificial intelligence ; Materials science ; Machine learning ; Molecule ; Physics ; Chemistry ; Drug discovery ; Systems engineering ; Engineering Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Snežana Đorđević | María Medel | Inmaculada Conejos‐Sánchez | Bárbara Carreira | Sabina Pozzi | Rita C. Acúrcio | Ronit Satchi‐Fainaro | Helena F. Florindo | Marı́a J. Vicent
Source: Drug Delivery and Translational Research (review, 2021-07-23)
Field: Materials Science — Biomaterials

Current hurdles to the translation of nanomedicines from bench to the clinic The field of nanomedicine has significantly influenced research areas such as drug delivery, diagnostics, theranostics, and regenerative medicine; however, the further development of this field will face significant challenges at the regulatory level if related guidance remains unclear and unconsolidated. This review describes those features and pathways crucial to the clinical translation of nanomedicine and highlights considerations for early-stage product development. These include identifying those critical quality attributes of the drug product essential for activity and safety, appropriate analytical methods (physical, chemical, biological) for characterization, important process parameters, and adequate pre-clinical models. Additional concerns include the evaluation of batch-to-batch consistency and considerations regarding scaling up that will ensure a successful reproducible manufacturing process. Furthermore, we advise close collaboration with regulatory agencies from the early stages of development to assure an aligned position to accelerate the development of future nanomedicines. Nanomedicine ; Bench to bedside ; Risk analysis (engineering) ; Consistency (knowledge bases) ; Process (computing) ; New product development ; Drug development ; Quality (philosophy) ; Process development ; Quality by Design ; Product (mathematics) ; Computer science ; Medicine ; Nanotechnology ; Biochemical engineering ; Systems engineering ; Process management ; Medical physics ; Engineering ; Drug ; Business ; Pharmacology ; Materials science ; Artificial intelligence Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Nongnuch Artrith | Keith T. Butler | François‐Xavier Coudert | Seungwu Han | Olexandr Isayev | Anubhav Jain | Aron Walsh
Source: Nature Chemistry (article, 2021-05-31)
Field: Materials Science — Materials Chemistry

Best practices in machine learning for chemistry Chemistry ; Workflow ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Data science ; Computer science ; Programming language ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Cheng Chang | Wei Chen | Ye Chen | Yonghua Chen | Yu Chen | Feng Ding | Chunhai Fan | Hong Jin Fan | Zhanxi Fan | Cheng Gong | Yongji Gong | Qiyuan He | Xun Hong | Sheng Hu | Weida Hu | Wei Huang | Yuan Huang | Wei Ji | Dehui Li | Lain‐Jong Li | Qiang Li | Lin Li | Chongyi Ling | Minghua Liu | Nan Liu | Zhuang Liu | Kian Ping Loh | Jianmin Ma | Feng Miao | Hailin Peng | Mingfei Shao | Li Song | Shao Su | Shuo Sun | Chaoliang Tan | Zhiyong Tang | Dingsheng Wang | Huan Wang | Jinlan Wang | Xin Wang | Xinran Wang | Andrew T. S. Wee | Zhongming Wei | Yuen Wu | Zhong‐Shuai Wu | Jie Xiong | Qihua Xiong | Weigao Xu | Peng Yin | Haibo Zeng | Zhiyuan Zeng | Tianyou Zhai | Han Zhang | Hui Zhang | Qichun Zhang | Tierui Zhang | Xiang Zhang | Lidong Zhao | Meiting Zhao | Weijie Zhao | Yunxuan Zhao | Kai‐Ge Zhou | Xing Zhou | Yu Zhou | Hongwei Zhu | Hua Zhang | Zhongfan Liu
Source: Acta Physico-Chimica Sinica (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Recent Progress on Two-Dimensional Materials Computer science MXene and MAX Phase Materials ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Mohd Nurazzi Norizan | M. R. M. Asyraf | Khalina Abdan | Norli Abdullah | Fatimah Athiyah Sabaruddin | Siti Hasnah Kamarudin | So’bah Ahmad | Annie Maria Mahat | Chuan Li Lee | H. A. Aisyah | Mohd Nor Faiz Norrrahim | R.A. Ilyas | M.M. Harussani | Mohamad Ridzwan Ishak | S.M. Sapuan
Source: Polymers (review, 2021-03-26)
Field: Materials Science — Materials Chemistry

Fabrication, Functionalization, and Application of Carbon Nanotube-Reinforced Polymer Composite: An Overview A novel class of carbon nanotube (CNT)-based nanomaterials has been surging since 1991 due to their noticeable mechanical and electrical properties, as well as their good electron transport properties. This is evidence that the development of CNT-reinforced polymer composites could contribute in expanding many areas of use, from energy-related devices to structural components. As a promising material with a wide range of applications, their poor solubility in aqueous and organic solvents has hindered the utilizations of CNTs. The current state of research in CNTs-both single-wall carbon nanotubes (SWCNT) and multiwalled carbon nanotube (MWCNT)-reinforced polymer composites-was reviewed in the context of the presently employed covalent and non-covalent functionalization. As such, this overview intends to provide a critical assessment of a surging class of composite materials and unveil the successful development associated with CNT-incorporated polymer composites. The mechanisms related to the mechanical, thermal, and electrical performance of CNT-reinforced polymer composites is also discussed. It is vital to understand how the addition of CNTs in a polymer composite alters the microstructure at the micro- and nano-scale, as well as how these modifications influence overall structural behavior, not only in its as fabricated form but also its functionalization techniques. The technological superiority gained with CNT addition to polymer composites may be advantageous, but scientific values are here to be critically explored for reliable, sustainable, and structural reliability in different industrial needs. Surface modification ; Carbon nanotube ; Fabrication ; Materials science ; Composite number ; Nanotechnology ; Polymer ; Composite material ; Polymer science ; Chemical engineering ; Engineering Carbon Nanotubes in Composites ; Conducting polymers and applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Teo Lombardo | Marc Duquesnoy | Hassna El-Bouysidy | Fabian Årén | A. Gallo‐Bueno | Peter Bjørn Jørgensen | Arghya Bhowmik | Arnaud Demortière | Elixabete Ayerbe | Francisco Alcaide | Marine Reynaud | Javier Carrasco | Alexis Grimaud | Chao Zhang | Tejs Vegge | Patrik Johansson | Alejandro A. Franco
Source: Chemical Reviews (review, 2021-09-16)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Applied to Battery Research: Hype or Reality? This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered. Chemistry ; Battery (electricity) Machine Learning in Materials Science ; Fault Detection and Control Systems ; Advanced Battery Technologies Research
Authors: Markus Meuwly
Source: Chemical Reviews (review, 2021-06-07)
Field: Materials Science — Materials Chemistry

Machine Learning for Chemical Reactions Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be particularly relevant for problems involving both computation and experiments. For one, Bayesian inference is a powerful approach to develop models consistent with knowledge from experiments. Second, ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques, and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, in both research and academic teaching. Field (mathematics) ; Artificial intelligence ; Inference ; Machine learning ; Chemistry ; Computer science ; Bayesian inference ; State (computer science) ; Biochemical engineering ; Bayesian probability ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ehsan Kianfar
Source: Journal of Nanobiotechnology (review, 2021-05-29)
Field: Materials Science — Biomaterials

Protein nanoparticles in drug delivery: animal protein, plant proteins and protein cages, albumin nanoparticles In this article, we will describe the properties of albumin and its biological functions, types of sources that can be used to produce albumin nanoparticles, methods of producing albumin nanoparticles, its therapeutic applications and the importance of albumin nanoparticles in the production of pharmaceutical formulations. In view of the increasing use of Abraxane and its approval for use in the treatment of several types of cancer and during the final stages of clinical trials for other cancers, to evaluate it and compare its effectiveness with conventional non formulations of chemotherapy Paclitaxel is paid. In this article, we will examine the role and importance of animal proteins in Nano medicine and the various benefits of these biomolecules for the preparation of drug delivery carriers and the characteristics of plant protein Nano carriers and protein Nano cages and their potentials in diagnosis and treatment. Finally, the advantages and disadvantages of protein nanoparticles are mentioned, as well as the methods of production of albumin nanoparticles, its therapeutic applications and the importance of albumin nanoparticles in the production of pharmaceutical formulations. Albumin ; Nanoparticle ; Drug delivery ; Drug ; Nanomedicine ; Chemistry ; Paclitaxel ; Pharmacology ; Nanotechnology ; Medicine ; Cancer ; Biochemistry ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced Drug Delivery Systems
Authors: Guoheng Tang | Jiuyang He | Juewen Liu | Xiyun Yan | Kelong Fan
Source: Exploration (review, 2021-08-01)
Field: Materials Science — Materials Chemistry

Nanozyme for tumor therapy: Surface modification matters As the next generation of artificial enzymes, nanozymes have shown unique properties compared to its natural counterparts, such as stability in harsh environment, low cost, and ease of production and modification, paving the way for its biomedical applications. Among them, tumor catalytic therapy mediated by the generation of reactive oxygen species (ROS) has made great progress mainly from the peroxidase-like activity of nanozymes. Fe<sub>3</sub>O<sub>4</sub> nanozymes, the earliest type of nanomaterial discovered to possess peroxidase-like activity, has consequently received wide attention for tumor therapy due to its ROS generation ability and tumor cell killing ability. However, inconsistent results of cytotoxicity were observed between different reports, and some even showed the scavenging of ROS in some cases. By collectively studying these inconsistent outcomes, we raise the question whether surface modification of Fe<sub>3</sub>O<sub>4</sub> nanozymes, either through affecting peroxidase activity or by affecting the biodistribution and intracellular fate, play an important role in its therapeutic effects. This review will go over the fundamental catalytic mechanisms of Fe<sub>3</sub>O<sub>4</sub> nanozymes and recent advances in tumor catalytic therapy, and discuss the importance of surface modification. Employing Fe<sub>3</sub>O<sub>4</sub> nanozymes as an example, we hope to provide an outlook on the improvement of nanozyme-based antitumor activity. Reactive oxygen species ; Surface modification ; Peroxidase ; Catalysis ; Nanotechnology ; Computer science ; Chemistry ; Biochemical engineering ; Materials science ; Enzyme ; Engineering ; Biochemistry Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Santanu Panja | Dave J. Adams
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Materials Science — Biomaterials

Stimuli responsive dynamic transformations in supramolecular gels Supramolecular gels are formed by the self-assembly of small molecules under the influence of various non-covalent interactions. As the interactions are individually weak and reversible, it is possible to perturb the gels easily, which in turn enables fine tuning of their properties. Synthetic supramolecular gels are kinetically trapped and usually do not show time variable changes in material properties after formation. However, such materials potentially become switchable when exposed to external stimuli like temperature, pH, light, enzyme, redox, and chemical analytes resulting in reconfiguration of gel matrix into a different type of network. Such transformations allow gel-to-gel transitions while the changes in the molecular aggregation result in alteration of physical and chemical properties of the gel with time. Here, we discuss various methods that have been used to achieve gel-to-gel transitions by modifying a pre-formed gel material through external perturbation. We also describe methods that allow time-dependent autonomous switching of gels into different networks enabling synthesis of next generation functional materials. Dynamic modification of gels allows construction of an array of supramolecular gels with various properties from a single material which eventually extend the limit of applications of the gels. In some cases, gel-to-gel transitions lead to materials that cannot be accessed directly. Finally, we point out the necessity and possibility of further exploration of the field. Supramolecular chemistry ; Chemistry ; Chemical engineering ; Nanotechnology ; Materials science ; Organic chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Photoreceptor and optogenetics research
Authors: Catherine P. Raptopoulou
Source: Materials (review, 2021-01-09)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks: Synthetic Methods and Potential Applications Metal-organic frameworks represent a porous class of materials that are build up from metal ions or oligonuclear metallic complexes and organic ligands. They can be considered as sub-class of coordination polymers and can be extended into one-dimension, two-dimensions, and three-dimensions. Depending on the size of the pores, MOFs are divided into nanoporous, mesoporous, and macroporous items. The latter two are usually amorphous. MOFs display high porosity, a large specific surface area, and high thermal stability due to the presence of coordination bonds. The pores can incorporate neutral molecules, such as solvent molecules, anions, and cations, depending on the overall charge of the MOF, gas molecules, and biomolecules. The structural diversity of the framework and the multifunctionality of the pores render this class of materials as candidates for a plethora of environmental and biomedical applications and also as catalysts, sensors, piezo/ferroelectric, thermoelectric, and magnetic materials. In the present review, the synthetic methods reported in the literature for preparing MOFs and their derived materials, and their potential applications in environment, energy, and biomedicine are discussed. Materials science ; Nanoporous ; Metal-organic framework ; Molecule ; Nanotechnology ; Polymer ; Mesoporous material ; Amorphous solid ; Biomolecule ; Porosity ; Metal ; Adsorption ; Catalysis ; Organic chemistry ; Chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jack Payne | Matthew D. Jones
Source: ChemSusChem (review, 2021-04-07)
Field: Materials Science — Biomaterials

The Chemical Recycling of Polyesters for a Circular Plastics Economy: Challenges and Emerging Opportunities Whilst plastics have played an instrumental role in human development, growing environmental concerns have led to increasing public scrutiny and demands for outright bans. This has stimulated considerable research into renewable alternatives, and more recently, the development of alternative waste management strategies. Herein, the aim was to highlight recent developments in the catalytic chemical recycling of two commercial polyesters, namely poly(lactic acid) (PLA) and poly(ethylene terephthalate) (PET). The concept of chemical recycling is first introduced, and associated opportunities/challenges are discussed within the context of the governing depolymerisation thermodynamics. Chemical recycling methods for PLA and PET are then discussed, with a particular focus on upcycling and the use of metal-based catalysts. Finally, the attention shifts to the emergence of new materials with the potential to modernise the plastics economy. Emerging opportunities and challenges are discussed within the context of industrial feasibility. Circular economy ; Polyester ; Waste management ; Biochemical engineering ; Nanotechnology ; Chemistry ; Environmental science ; Natural resource economics ; Materials science ; Engineering ; Organic chemistry ; Economics ; Ecology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer crystallization and properties