jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kun Li | Chen Ji | Shengwen Bai | Bin Jiang | Fusheng Pan
Source: Journal of Material Science and Technology (article, 2023-03-02)
Field: Materials Science — Biomaterials

Selective laser melting of magnesium alloys: Necessity, formability, performance, optimization and applications Formability ; Selective laser melting ; Materials science ; Magnesium alloy ; Magnesium ; Microstructure ; Process optimization ; Corrosion ; Alloy ; Composite number ; Metallurgy ; Process engineering ; Composite material ; Engineering Magnesium Alloys: Properties and Applications ; Additive Manufacturing Materials and Processes ; Bone Tissue Engineering Materials
Authors: Andres M Bran | Sam Cox | Schilter, Oliver | Baldassari, Carlo | White, Andrew D | Schwaller, Philippe
Source: arXiv (Cornell University) (preprint, 2023-04-11)
Field: Materials Science — Materials Chemistry

ChemCrow: Augmenting large-language models with chemistry tools Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their full potential by overcoming steep learning curves. Recently, large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related problems. Moreover, these models lack access to external knowledge sources, limiting their usefulness in scientific applications. In this study, we introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design. By integrating 18 expert-designed tools, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent, three organocatalysts, and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Surprisingly, we find that GPT-4 as an evaluator cannot distinguish between clearly wrong GPT-4 completions and Chemcrow's performance. Our work not only aids expert chemists and lowers barriers for non-experts, but also fosters scientific advancement by bridging the gap between experimental and computational chemistry. Limiting ; Bridging (networking) ; Computer science ; Set (abstract data type) ; Drug discovery ; Chemistry ; Data science ; Nanotechnology ; Artificial intelligence ; Engineering ; Programming language Machine Learning in Materials Science
Authors: Guannan Huang | Yani Guo | Ye Chen | Zhengwei Nie
Source: Materials (review, 2023-08-31)
Field: Materials Science — Materials Chemistry

Application of Machine Learning in Material Synthesis and Property Prediction Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented. Machine learning ; Computer science ; Artificial intelligence ; Field (mathematics) ; Process (computing) ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Nahyun Kim | Hyun Lee | Ginam Han | Min‐Ho Kang | Sinwoo Park | Dong Eung Kim | Minyoung Lee | Moon‐Jo Kim | Yuhyun Na | SeKwon Oh | Seo‐Jun Bang | Tae‐Sik Jang | Hyoun‐Ee Kim | Jungwon Park | Su Ryon Shin | Hyun‐Do Jung
Source: Advanced Science (article, 2023-04-19)
Field: Materials Science — Biomaterials

3D‐Printed Functional Hydrogel by DNA‐Induced Biomineralization for Accelerated Diabetic Wound Healing Chronic wounds in diabetic patients are challenging because their prolonged inflammation makes healing difficult, thus burdening patients, society, and health care systems. Customized dressing materials are needed to effectively treat such wounds that vary in shape and depth. The continuous development of 3D-printing technology along with artificial intelligence has increased the precision, versatility, and compatibility of various materials, thus providing the considerable potential to meet the abovementioned needs. Herein, functional 3D-printing inks comprising DNA from salmon sperm and DNA-induced biosilica inspired by marine sponges, are developed for the machine learning-based 3D-printing of wound dressings. The DNA and biomineralized silica are incorporated into hydrogel inks in a fast, facile manner. The 3D-printed wound dressing thus generates provided appropriate porosity, characterized by effective exudate and blood absorption at wound sites, and mechanical tunability indicated by good shape fidelity and printability during optimized 3D printing. Moreover, the DNA and biomineralized silica act as nanotherapeutics, enhancing the biological activity of the dressings in terms of reactive oxygen species scavenging, angiogenesis, and anti-inflammation activity, thereby accelerating acute and diabetic wound healing. These bioinspired 3D-printed hydrogels produce using a DNA-induced biomineralization strategy are an excellent functional platform for clinical applications in acute and chronic wound repair. Biomineralization ; Wound healing ; 3d printed ; Self-healing hydrogels ; Biomedical engineering ; Materials science ; Chemistry ; Chemical engineering ; Medicine ; Surgery ; Polymer chemistry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications ; Wound Healing and Treatments
Authors: Sanjit Manohar Majhi | Ashraf Ali | Yaser E. Greish | H.F. El-Maghraby | Saleh T. Mahmoud
Source: Scientific Reports (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

V2CTX MXene-based hybrid sensor with high selectivity and ppb-level detection for acetone at room temperature High-performance, room temperature-based novel sensing materials are one of the frontier research topics in the gas sensing field, and MXenes, a family of emerging 2D layered materials, has gained widespread attention due to their distinctive properties. In this work, we propose a chemiresistive gas sensor made from V<sub>2</sub>CT<sub>x</sub> MXene-derived, urchin-like V<sub>2</sub>O<sub>5</sub> hybrid materials (V<sub>2</sub>C/V<sub>2</sub>O<sub>5</sub> MXene) for gas sensing applications at room temperature. The as-prepared sensor exhibited high performance when used as the sensing material for acetone detection at room temperature. Furthermore, the V<sub>2</sub>C/V<sub>2</sub>O<sub>5</sub> MXene-based sensor exhibited a higher response (S% = 11.9%) toward 15 ppm acetone than pristine multilayer V<sub>2</sub>CT<sub>x</sub> MXenes (S% = 4.6%). Additionally, the composite sensor demonstrated a low detection level at ppb levels (250 ppb) at room temperature, as well as high selectivity among different interfering gases, fast response-recovery time, good repeatability with minimal amplitude fluctuation, and excellent long-term stability. These improved sensing properties can be attributed to the possible formation of H-bonds in multilayer V<sub>2</sub>C MXenes, the synergistic effect of the newly formed composite of urchin-like V<sub>2</sub>C/V<sub>2</sub>O<sub>5</sub> MXene sensor, and high charge carrier transport at the interface of V<sub>2</sub>O<sub>5</sub> and V<sub>2</sub>C MXene. Acetone ; Selectivity ; Materials science ; Chromatography ; Chemistry ; Analytical Chemistry (journal) ; Organic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Xiaojun Chen | Jiao Lin | Ke Wang
Source: Laser & Photonics Review (review, 2023-02-01)
Field: Materials Science — Materials Chemistry

A Review of Silicon‐Based Integrated Optical Switches Abstract Recently, silicon‐integrated optical circuits have attracted intensive interests, thanks to the compatibility with the complementary metal‐oxide‐semiconductor (CMOS) technology that enables mass production at low cost. The optical switch is an essential part of optical integrated circuits, with broad applications in optical communications and networks, optical computing, and sensing such as LiDAR. In general, the silicon‐integrated optical switch adopts thermo‐optic or carrier dispersion effect to realize reconfigurable signal routing. However, the use of thermo‐optic effect leads to high power consumption, and the carrier dispersion effect has the disadvantage of small refractive index change. In addition, both effects are non‐latching, and hence, continuous power consumption is required even when switching is not needed. For overcoming these drawbacks, phase‐change materials (PCMs) have been introduced into silicon‐integrated optical switches. In this paper, silicon‐integrated optical switches are classified according to the underlying structure and recent research is reviewed. Recent studies on silicon‐integrated optical switches incorporating PCMs are also reviewed. Furthermore, the pros and cons of different types of integrated optical switches with and without PCMs are compared and discussed. Optical switch ; Silicon photonics ; Optical performance monitoring ; Materials science ; Silicon ; Optical transistor ; CMOS ; Integrated circuit ; Electronic engineering ; Electronic circuit ; Optical power ; Computer science ; Optoelectronics ; Electrical engineering ; Engineering ; Laser ; Optics ; Wavelength-division multiplexing ; Transistor ; Physics ; Voltage Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Advanced Memory and Neural Computing
Authors: Celia María Curieses Andrés | José Manuel Pérez de la Lastra | Celia Andrés | Francisco J. Plou | Eduardo Pérez‐Lebeña
Source: Processes (article, 2023-09-16)
Field: Chemistry — Organic Chemistry

Polyphenols as Antioxidant/Pro-Oxidant Compounds and Donors of Reducing Species: Relationship with Human Antioxidant Metabolism In this review, we have focused mainly on the study of their antioxidant and pro-oxidant capacity, and the analysis of the oxidation of the catechol group to o-quinone. The redox balance established between the different oxidase and reductase enzymes generates reducing species, H+ and e−, and allows the oxidation of polyphenolic groups to quinones to be reversible. This continuous balance between these nucleophilic and electrophilic substances allows the activation of the NRF2/ARE axis, which regulates cellular antioxidant responses against oxidative stress, as well as cell proliferation. Understanding the ambivalent character of polyphenols, which can act simultaneously as antioxidants and pro-oxidants, will allow the design of specific therapies that can serve science and medicine in their tasks. Antioxidant ; Polyphenol ; Chemistry ; Oxidative stress ; Quinone ; Pro-oxidant ; Redox ; Biochemistry ; Catechol ; Electrophile ; Enzyme ; Nucleophile ; Metabolism ; Organic chemistry ; Catalysis Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities ; Genomics, phytochemicals, and oxidative stress
Authors: Reza Abazari | Soheila Sanati | Majed A. Bajaber | Muhammad Sufyan Javed | ‬Peter C. Junk | Ashok Kumar Nanjundan | Jinjie Qian | Deepak P. Dubal
Source: Small (review, 2023-11-23)
Field: Chemistry — Inorganic Chemistry

Design and Advanced Manufacturing of NU‐1000 Metal–Organic Frameworks with Future Perspectives for Environmental and Renewable Energy Applications Metal-organic frameworks (MOFs) represent a relatively new family of materials that attract lots of attention thanks to their unique features such as hierarchical porosity, active metal centers, versatility of linkers/metal nodes, and large surface area. Among the extended list of MOFs, Zr-based-MOFs demonstrate comparably superior chemical and thermal stabilities, making them ideal candidates for energy and environmental applications. As a Zr-MOF, NU-1000 is first synthesized at Northwestern University. A comprehensive review of various approaches to the synthesis of NU-1000 MOFs for obtaining unique surface properties (e.g., diverse surface morphologies, large surface area, and particular pore size distribution) and their applications in the catalysis (electro-, and photo-catalysis), CO<sub>2</sub> reduction, batteries, hydrogen storage, gas storage/separation, and other environmental fields are presented. The review further outlines the current challenges in the development of NU-1000 MOFs and their derivatives in practical applications, revealing areas for future investigation. Metal-organic framework ; Nanotechnology ; Materials science ; Renewable energy ; Porosity ; Catalysis ; Hydrogen storage ; Energy storage ; Chemistry ; Organic chemistry ; Engineering ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Covalent Organic Framework Applications
Authors: Xi Fan | Nathan E. Stott | Jixi Zeng | Yunfei Li | Jianyong Ouyang | Liang Chu | Weijie Song
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

PEDOT:PSS materials for optoelectronics, thermoelectrics, and flexible and stretchable electronics Cutting-edge approaches to prepare PEDOT:PSS films/electrodes, significant progress on related devices are summarized; advantages, plausible questions, challenges, and suggestions are also presented for the continued development of modern electronics. PEDOT:PSS ; Electronics ; Materials science ; Thermoelectric materials ; Engineering physics ; Nanotechnology ; Enhanced Data Rates for GSM Evolution ; Electrode ; Optoelectronics ; Electrical engineering ; Engineering ; Composite material ; Telecommunications ; Layer (electronics) ; Physics ; Thermal conductivity Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Advanced Thermoelectric Materials and Devices
Authors: Mingjian Wen | Evan Walter Clark Spotte‐Smith | Samuel M. Blau | Matthew J. McDermott | Aditi S. Krishnapriyan | Kristin A. Persson
Source: Nature Computational Science (article, 2023-01-16)
Field: Materials Science — Materials Chemistry

Chemical reaction networks and opportunities for machine learning Computer science ; Perspective (graphical) ; Range (aeronautics) ; Data science ; Artificial intelligence ; Machine learning ; Management science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jordi Burés | Igor Larrosa
Source: Nature (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Organic reaction mechanism classification using machine learning Mechanism (biology) ; Computer science ; Artificial intelligence ; Biochemical engineering ; Artificial neural network ; Machine learning ; Variety (cybernetics) ; Logarithm ; Biological system ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maksim Kulichenko | Kipton Barros | Nicholas Lubbers | Ying Wai Li | Richard A. Messerly | Sergei Tretiak | Justin S. Smith | Benjamin Nebgen
Source: Nature Computational Science (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Uncertainty-driven dynamics for active learning of interatomic potentials Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the data set. Here we develop a strategy to more rapidly discover configurations that meaningfully augment the training data set. The approach, uncertainty-driven dynamics for active learning (UDD-AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. The performance of UDD-AL is demonstrated for two AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore the chemically relevant configuration space, which may be inaccessible using regular dynamical sampling at target temperature conditions. Molecular dynamics ; Computer science ; Configuration space ; Sampling (signal processing) ; Space (punctuation) ; Active learning (machine learning) ; Set (abstract data type) ; Fidelity ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: László Szabó | Romain Milotskyi | Gyanendra Sharma | Kenji Takahashi
Source: Green Chemistry (article, 2023-01-01)
Field: Materials Science — Biomaterials

Cellulose processing in ionic liquids from a materials science perspective: turning a versatile biopolymer into the cornerstone of our sustainable future The past two decades have seen fruitful efforts in shaping cellulose into functional materials using ionic liquids. This Tutorial Review aims at providing guidance from a materials science perspective to stimulate more research in this field. Ionic liquid ; Cornerstone ; Perspective (graphical) ; Cellulose ; Biopolymer ; Nanotechnology ; Field (mathematics) ; Chemistry ; Engineering ethics ; Materials science ; Polymer science ; Organic chemistry ; Computer science ; Engineering ; Polymer ; Artificial intelligence ; Catalysis ; Mathematics Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Nanocomposite Films for Food Packaging
Authors: Senyao Meng | Ge Li | Ping Wang | Miao He | Xiaohua Sun | Zhenxing Li
Source: Materials Chemistry Frontiers (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Rare earth-based MOFs for photo/electrocatalysis This review provides current research progress on rare earth MOFs, including synthesis methods and photocatalytic and electrocatalytic applications of rare earth MOFs. Rare earth ; Electrocatalyst ; Materials science ; Nanotechnology ; Photocatalysis ; Earth (classical element) ; Astrobiology ; Catalysis ; Chemistry ; Electrochemistry ; Metallurgy ; Electrode ; Physical chemistry ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Inorganic Fluorides and Related Compounds
Authors: Mingwei Hu | Qiyang Tan | Ruth Knibbe | Miao Xu | Bin Jiang | Sen Wang | Xue Li | Mingxing Zhang
Source: Materials Science and Engineering R Reports (review, 2023-06-16)
Field: Materials Science — Materials Chemistry

Recent applications of machine learning in alloy design: A review Inefficiency ; Alloy ; Computer science ; Work (physics) ; Domain (mathematical analysis) ; Key (lock) ; Resource (disambiguation) ; Industrial engineering ; Artificial intelligence ; Mechanical engineering ; Materials science ; Engineering ; Metallurgy ; Mathematics ; Computer security Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Markus Scheidgen | Lauri Himanen | Alvin Noe Ladines | David Sikter | Mohammad Nakhaee | Ádám Fekete | Theodore L. Chang | Amir Golparvar | J.A. Marquez | Sándor Brockhauser | Sebastian Brückner | Luca M. Ghiringhelli | Felix Dietrich | Daniel Lehmberg | Thea Denell | Andrea Albino | Hampus Näsström | Sherjeel Shabih | Florian Dobener | Markus Kühbach | Rubel Mozumder | Joseph F. Rudzinski | Nathan Daelman | José M. Pizarro | Martin Kubáň | C. Gómez de Salazar | Pavel Ondračka | Hans‐Joachim Bungartz | Claudia Draxl
Source: The Journal of Open Source Software (article, 2023-10-15)
Field: Materials Science — Materials Chemistry

NOMAD: A distributed web-based platform for managingmaterials science research data Materials science research is becoming increasingly data-driven, which requires more effort to manage, share, and publish data. NOMAD is a web-based application that provides data management for materials science research data. In addition to core data management functions like uploading and sharing files, NOMAD allows for entering structured data using customizable forms providing the software with electronic laboratory notebook (ELN) functionalities. It automatically extracts rich meta-data from supported file formats, normalizes and converts data from these formats, and provides a faceted search with materials science-specific filters based on extracted meta-data. NOMAD integrates data analysis and machine learning tools.<br>Installations of NOMAD can be connected to share data between research institutes and can<br>publish data to an open central NOMAD service. The NOMAD software is distributed as a<br>Docker image to create data management services and as a Python package to automate the client’s use of these services. World Wide Web ; Computer science ; Data science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Atif Khurshid Wani | Nahid Akhtar | Tahir ul Gani Mir | Rattandeep Singh | Prakash Kumar Jha | Shyam Kumar Mallik | Shruti Sinha | Surya Kant Tripathi | A. C. JAIN | Aprajita Jha | Hari Prasad Devkota | Ajit Prakash
Source: Biomolecules (review, 2023-01-18)
Field: Materials Science — Materials Chemistry

Targeting Apoptotic Pathway of Cancer Cells with Phytochemicals and Plant-Based Nanomaterials Apoptosis is the elimination of functionally non-essential, neoplastic, and infected cells via the mitochondrial pathway or death receptor pathway. The process of apoptosis is highly regulated through membrane channels and apoptogenic proteins. Apoptosis maintains cellular balance within the human body through cell cycle progression. Loss of apoptosis control prolongs cancer cell survival and allows the accumulation of mutations that can promote angiogenesis, promote cell proliferation, disrupt differentiation, and increase invasiveness during tumor progression. The apoptotic pathway has been extensively studied as a potential drug target in cancer treatment. However, the off-target activities of drugs and negative implications have been a matter of concern over the years. Phytochemicals (PCs) have been studied for their efficacy in various cancer cell lines individually and synergistically. The development of nanoparticles (NPs) through green synthesis has added a new dimension to the advancement of plant-based nanomaterials for effective cancer treatment. This review provides a detailed insight into the fundamental molecular pathways of programmed cell death and highlights the role of PCs along with the existing drugs and plant-based NPs in treating cancer by targeting its programmed cell death (PCD) network. Apoptosis ; Programmed cell death ; Cancer cell ; Cancer ; Cell biology ; Cell cycle ; Angiogenesis ; Biology ; Cell growth ; Cell ; Cancer research ; Biochemistry ; Genetics Nanoparticles: synthesis and applications ; Curcumin's Biomedical Applications ; Medicinal Plants and Neuroprotection