jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yujie Jiang | Zibei Chen | Ning Sui | Zhiling Zhu
Source: Journal of the American Chemical Society (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

Data-Driven Evolutionary Design of Multienzyme-like Nanozymes Multienzyme-like nanozymes are nanomaterials with multiple enzyme-like activities and are the focus of nanozyme research owing to their ability to facilitate cascaded reactions, leverage synergistic effects, and exhibit environmentally responsive selectivity. However, multienzyme-like nanozymes exhibit varying enzyme-like activities under different conditions, making them difficult to precisely regulate according to the design requirements. Moreover, individual enzyme-like activity in a multienzyme-like activity may accelerate, compete, or antagonize each other, rendering the overall activity a complex interplay of these factors rather than a simple sum of single enzyme-like activity. A theoretically guided strategy is highly desired to accelerate the design of multienzyme-like nanozymes. Herein, nanozyme information was collected from 4159 publications to build a nanozyme database covering element type, element ratio, chemical valence, shape, pH, etc. Based on the clustering correlation coefficients of the nanozyme information, the material features in distinct nanozyme classifications were reorganized to generate compositional factors for multienzyme-like nanozymes. Moreover, advanced methods were developed, including the quantum mechanics/molecular mechanics method for analyzing the surface adsorption and binding energies of substrates, transition states, and products in the reaction pathways, along with machine learning algorithms to identify the optimal reaction pathway, to aid the evolutionary design of multienzyme-like nanozymes. This approach culminated in creating CuMnCo<sub>7</sub>O<sub>12</sub>, a highly active multienzyme-like nanozyme. This process is named the genetic-like evolutionary design of nanozymes because it resembles biological genetic evolution in nature and offers a feasible protocol and theoretical foundation for constructing multienzyme-like nanozymes. Chemistry ; Nanotechnology Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Junwen Su | Lamei Yang | Ziran Sun | Xianquan Zhan
Source: Molecular & Cellular Proteomics (article, 2024-02-13)
Field: Chemistry — Spectroscopy

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized medicine in the pharmacy field. Protein drugs are crucial in the field of personalized drug therapy and are currently the mainstay, which possess higher target specificity and biological activity than small-molecule chemical drugs, making them efficient in regulating disease-related biological processes, and have significant potential in the development of personalized drugs. Currently, protein drugs are designed and developed for specific protein targets based on patient-specific protein data. However, due to the rapid development of two-dimensional gel electrophoresis and mass spectrometry, it is now widely recognized that a canonical protein actually includes multiple proteoforms, and the differences between these proteoforms will result in varying responses to drugs. The variation in the effects of different proteoforms can be significant and the impact can even alter the intended benefit of a drug, potentially making it harmful instead of lifesaving. As a result, we propose that protein drugs should shift from being targeted through the lens of protein (proteomics) to being targeted through the lens of proteoform (proteoformics). This will enable the development of personalized protein drugs that are better equipped to meet patients' specific needs and disease characteristics. With further development in the field of proteoformics, individualized drug therapy, especially personalized protein drugs aimed at proteoforms as a drug target, will improve the understanding of disease mechanisms, discovery of new drug targets and signaling pathways, provide a theoretical basis for the development of new drugs, aid doctors in conducting health risk assessments and making more cost-effective targeted prevention strategies conducted by artificial intelligence/machine learning, promote technological innovation, and provide more convenient treatment tailored to individualized patient profile, which will benefit the affected individuals and society at large. Personalized medicine ; Drug discovery ; Drug development ; Drug ; Proteomics ; Computer science ; Disease ; Computational biology ; Medicine ; Bioinformatics ; Pharmacology ; Biology ; Internal medicine Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Feras Alasali | Mohammed I. Abuashour | Waleed Hammad | Derar Almomani | Amr M. Obeidat | William Holderbaum
Source: Energy Science & Engineering (review, 2024-03-06)
Field: Materials Science — Materials Chemistry

A review of hydrogen production and storage materials for efficient integrated hydrogen energy systems Abstract The rapidly growing global need for environmentally friendly energy solutions has inspired extensive research and development efforts aimed at harnessing the potential of hydrogen energy. Hydrogen, with its diverse applications and relatively straightforward acquisition, is viewed as a promising energy carrier capable of tackling pressing issues, such as carbon emissions reduction and energy storage. This study conducts a preliminary investigation into effective hydrogen generation and storage systems, encompassing methods like water electrolysis, biomass reforming, and solar‐driven processes. Specifically, the study focuses on assessing the potential of nanostructured catalysts and innovative materials to enhance the productivity and versatility of hydrogen energy systems. Additionally, the utilization of novel materials not only improves hydrogen storage capacity and safety but also opens up possibilities for inventive applications, including on‐demand release and efficient transportation. Furthermore, critical factors such as catalyst design, material engineering, system integration, and technoeconomic viability are examined to identify challenges and chart paths for future advancements. The research emphasizes the importance of fostering interdisciplinary collaborations to advance hydrogen energy technologies and contribute to a sustainable energy future. Hydrogen storage ; Hydrogen production ; Hydrogen ; Production (economics) ; Energy storage ; Hydrogen fuel ; Computer science ; Materials science ; Environmental science ; Process engineering ; Engineering ; Chemistry ; Physics ; Thermodynamics ; Economics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Haikuan Dong | Yongbo Shi | Penghua Ying | Ke Xu | Ting Liang | Yanzhou Wang | Zezhu Zeng | Xin Wu | Wenjiang Zhou | Shiyun Xiong | Shunda Chen | Zheyong Fan
Source: Journal of Applied Physics (review, 2024-04-24)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini-review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials as implemented in the GPUMD package. Our aim with this mini-review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies. Neuroevolution ; Computer science ; Molecular dynamics ; Dynamics (music) ; Artificial intelligence ; Statistical physics ; Physics ; Artificial neural network Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: Xi Zhou | Quan Zhou | Zhaozhi He | Yi Xiao | Yan Liu | Zhuohang Huang | Yaoji Sun | Jiawei Wang | Zhengdong Zhao | Xiaozhou Liu | Bin Zhou | Lei Ren | Yu Sun | Zhiwei Chen | Xingcai Zhang
Source: Nano-Micro Letters (article, 2024-03-21)
Field: Materials Science — Materials Chemistry

ROS Balance Autoregulating Core–Shell CeO2@ZIF-8/Au Nanoplatform for Wound Repair Reactive oxygen species (ROS) plays important roles in living organisms. While ROS is a double-edged sword, which can eliminate drug-resistant bacteria, but excessive levels can cause oxidative damage to cells. A core-shell nanozyme, CeO<sub>2</sub>@ZIF-8/Au, has been crafted, spontaneously activating both ROS generating and scavenging functions, achieving the multi-faceted functions of eliminating bacteria, reducing inflammation, and promoting wound healing. The Au Nanoparticles (NPs) on the shell exhibit high-efficiency peroxidase-like activity, producing ROS to kill bacteria. Meanwhile, the encapsulation of CeO<sub>2</sub> core within ZIF-8 provides a seal for temporarily limiting the superoxide dismutase and catalase-like activities of CeO<sub>2</sub> nanoparticles. Subsequently, as the ZIF-8 structure decomposes in the acidic microenvironment, the CeO<sub>2</sub> core is gradually released, exerting its ROS scavenging activity to eliminate excess ROS produced by the Au NPs. These two functions automatically and continuously regulate the balance of ROS levels, ultimately achieving the function of killing bacteria, reducing inflammation, and promoting wound healing. Such innovative ROS spontaneous regulators hold immense potential for revolutionizing the field of antibacterial agents and therapies. Reactive oxygen species ; Catalase ; Chemistry ; Bacteria ; Superoxide dismutase ; Inflammation ; Wound healing ; Cell biology ; Mitochondrial ROS ; Oxidative stress ; Biophysics ; Biochemistry ; Biology ; Immunology Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Yasaman Pajouhan | Samad Sabbaghi | Kamal Rasouli | Jamal Rasouli | Wafa Dastyar | Esber Andiroglu
Source: Process Safety and Environmental Protection (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Enhanced photocatalytic degradation of tetracycline using α-Fe2O3@TiO2- impregnated Mxene photocatalyst: Mechanism and optimization of process via RSM and ANN Photocatalysis ; Degradation (telecommunications) ; Mechanism (biology) ; Process (computing) ; Tetracycline ; Response surface methodology ; Chemical engineering ; Chemistry ; Materials science ; Computer science ; Chromatography ; Biochemistry ; Engineering ; Catalysis ; Physics ; Antibiotics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Han Yang | Chenxi Hu | Yichi Zhou | Xixian Liu | Shi Yu | Jielan Li | Guanzhi Li | Zekun Chen | Shuizhou Chen | Claudio Zeni | Matthew K. Horton | Robert Pinsler | Andrew Fowler | Daniel Zügner | Tian Xie | Jake A. Smith | Lixin Sun | Qian Wang | Lingyu Kong | Chang Liu | Hongxia Hao | Ziheng Lu
Source: arXiv (Cornell University) (preprint, 2024-05-08)
Field: Materials Science — Materials Chemistry

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions pose significant challenges for accurately modeling arbitrary material candidates and forecasting their properties. We present MatterSim, a deep learning model actively learned from large-scale first-principles computations, for efficient atomistic simulations at first-principles level and accurate prediction of broad material properties across the periodic table, spanning temperatures from 0 to 5000 K and pressures up to 1000 GPa. Out-of-the-box, the model serves as a machine learning force field, and shows remarkable capabilities not only in predicting ground-state material structures and energetics, but also in simulating their behavior under realistic temperatures and pressures, signifying an up to ten-fold enhancement in precision compared to the prior best-in-class. This enables MatterSim to compute materials' lattice dynamics, mechanical and thermodynamic properties, and beyond, to an accuracy comparable with first-principles methods. Specifically, MatterSim predicts Gibbs free energies for a wide range of inorganic solids with near-first-principles accuracy and achieves a 15 meV/atom resolution for temperatures up to 1000K compared with experiments. This opens an opportunity to predict experimental phase diagrams of materials at minimal computational cost. Moreover, MatterSim also serves as a platform for continuous learning and customization by integrating domain-specific data. The model can be fine-tuned for atomistic simulations at a desired level of theory or for direct structure-to-property predictions, achieving high data efficiency with a reduction in data requirements by up to 97%. Deep learning ; Materials science ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: X. Song | Wenwu Wang | Yongjun Deng | Yutai Su | Fengrui Jia | Qasim Zaheer | Xu Long
Source: Engineering Structures (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Data-driven modeling for residual velocity of projectile penetrating reinforced concrete slabs The penetration of projectiles into reinforced concrete (RC) structures is crucial for defense technology. However, accurately estimating the resistance of RC structures to projectile penetration is challenging due to complex parameter nonlinearities. This paper proposes a data-driven modeling methodology that combines validated finite element (FE) simulations with machine learning (ML) algorithms to investigate the penetration resistance of RC slabs against projectiles. The methodology employs an extreme learning machine (ELM) network to identify extreme conditions characterizing penetration resistance, reducing data requirements, and improving prediction efficiency. Predicted results from the ELM network are then used as input for a recurrent neural network (RNN) to predict residual velocity curves over time, a crucial metric for evaluating penetration resistance. To cover various loading scenarios, 64 sets of FE models with different incident velocities, caliber-radius-head (CRH), and projectile masses are developed based on available experimental studies. The Johnson-Holmquist II model and Johnson-Cook model are employed for concrete and steel, respectively, to describe material behavior at high strain rates . The FE prediction results form a training sample database, and ML models are established using CRH, incident velocity, and projectile mass as input layers and projectile residual velocity as the output layer. The results demonstrate high Pearson correlation coefficients of 0.984 and 0.991 for the ELM and RNN prediction results, respectively, with numerical simulation results. The proposed networks provide accurate and efficient alternatives to time-consuming FE simulations for predicting projectile penetration performance . This strategy offers a new ML-based method for rapid evaluation, particularly for in-situ applications. • Finite element and machine learning methods are combined to investigate the penetration resistance of reinforced concrete structures . • The Johnson-Holmquist II intrinsic model is extended to concrete-like materials and verified by experiments and empirical formulations. • The effect of projectile parameters on the residual velocity of the projectile and damage to reinforced concrete slabs is analyzed. • Data-driven modeling enables rapid prediction of residual velocity of projectile, with good applicability and accurate results. Projectile ; Residual ; Structural engineering ; Reinforced concrete ; LS-DYNA ; Materials science ; Geotechnical engineering ; Geology ; Engineering ; Finite element method ; Computer science High-Velocity Impact and Material Behavior ; Geophysical Methods and Applications ; Structural Response to Dynamic Loads
Authors: Sri Harish Kumar Paleti | Young-Seok Kim | Joost Kimpel | Mariavittoria Craighero | Shuichi Haraguchi | Christian Müller
Source: Chemical Society Reviews (review, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Impact of doping on the mechanical properties of conjugated polymers Conjugated polymers exhibit a unique portfolio of electrical and electrochemical behavior, which - paired with the mechanical properties that are typical for macromolecules - make them intriguing candidates for a wide range of application areas from wearable electronics to bioelectronics. However, the degree of oxidation or reduction of the polymer can strongly impact the mechanical response and thus must be considered when designing flexible or stretchable devices. This tutorial review first explores how the chain architecture, processing as well as the resulting nano- and microstructure impact the rheological and mechanical properties. In addition, different methods for the mechanical characterization of thin films and bulk materials such as fibers are summarized. Then, the review discusses how chemical and electrochemical doping alter the mechanical properties in terms of stiffness and ductility. Finally, the mechanical response of (doped) conjugated polymers is discussed in the context of (1) organic photovoltaics, representing thin-film devices with a relatively low charge-carrier density, (2) organic thermoelectrics, where chemical doping is used to realize thin films or bulk materials with a high doping level, and (3) organic electrochemical transistors, where electrochemical doping allows high charge-carrier densities to be reached, albeit accompanied by significant swelling. In the future, chemical and electrochemical doping may not only allow modulation and optimization of the electrical and electrochemical behavior of conjugated polymers, but also facilitate the design of materials with a tunable mechanical response. Bioelectronics ; Conjugated system ; Polymer ; Doping ; Nanotechnology ; Materials science ; Electrochemistry ; Conductive polymer ; Electronics ; Organic electronics ; Wearable technology ; Wearable computer ; Chemistry ; Optoelectronics ; Computer science ; Biosensor ; Electrode ; Transistor ; Composite material ; Electrical engineering ; Physical chemistry ; Engineering Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Mariko Takahashi | Harrison B. Chong | Siwen Zhang | Tzu-yi Yang | Matthew J. Lazarov | Stefan Harry | Michelle Maynard | Brendan J. Hilbert | Ryan D. White | Heather E. Murrey | Chih‐Chiang Tsou | Kira Vordermark | Jonathan Assaad | Magdy Gohar | Benedikt R. Dürr | Marianne Richter | Himani Patel | Gregory V. Kryukov | Natasja Brooijmans | Aliyu Sidi Omar Alghali | Karla Rubio | A. R. Villanueva | Junbing Zhang | Maolin Ge | Farah Makram | Hanna Griesshaber | Drew Harrison | Ann-Sophie Koglin | Samuel Ojeda | Barbara Karakyriakou | Alexander Healy | George Popoola | Inbal Rachmin | Neha Khandelwal | Jason R. Neil | Pei-Chieh Tien | Nicholas Chen | Tobias Hosp | Sanne van den Ouweland | Toshiro Hara | Lillian Bussema | Rui Dong | Lei Shi | Martin Q. Rasmussen | Ana Carolina Martins Domingues | Aleigha Lawless | Jacy Fang | Satoshi Yoda | Linh Phuong Nguyen | Sarah Reeves | Farrah Nicole Wakefield | Adam Acker | Sarah E. Clark | Taronish D. Dubash | John Kastanos | Eugene Oh | David E. Fisher | Shyamala Maheswaran | Daniel A. Haber | Genevieve M. Boland | Moshe Sade-Feldman | Russell W. Jenkins | Aaron N. Hata | Nabeel Bardeesy | Mario L. Suvà | Brent R. Martin | Brian B. Liau | Christopher J. Ott | Miguel N. Rivera | Michael S. Lawrence | Liron Bar‐Peled
Source: Cell (article, 2024-04-22)
Field: Chemistry — Organic Chemistry

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability Cysteine ; Transcription factor ; Biology ; Cancer cell ; Computational biology ; Genetics ; Biochemistry ; Cancer ; Gene ; Enzyme Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; RNA Interference and Gene Delivery
Authors: Ge Lei | R. Docherty | Samuel J. Cooper
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Materials science in the era of large language models: a perspective This perspective paper explores the potential of Large Language Models (LLMs) in materials science, highlighting their abilities to handle ambiguous tasks, automate processes, and extract knowledge at scale across various disciplines. Perspective (graphical) ; Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science
Authors: Shiva Singh | Shakshi Bhardwaj | Pragya Tiwari | Keshav Dev | Kaushik Ghosh | Pradip K. Maji
Source: Materials Advances (review, 2024-01-01)
Field: Materials Science — Biomaterials

Recent advances in cellulose nanocrystals-based sensors: a review Cellulose nanocrystals (CNCs) continue to pique the interest of researchers as sensors for sustainable development. Cellulose ; Nanocrystal ; Nanotechnology ; Materials science ; Chemical engineering ; Engineering Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Polysaccharides and Plant Cell Walls
Authors: Yun Dai | Yifan Guo | Weicheng Tang | Dan Chen | Liru Xue | Ying Chen | Yican Guo | Simin Wei | Meng Wu | Jun Dai | Shixuan Wang
Source: Journal of Nanobiotechnology (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Reactive oxygen species-scavenging nanomaterials for the prevention and treatment of age-related diseases With increasing proportion of the elderly in the population, age-related diseases (ARD) lead to a considerable healthcare burden to society. Prevention and treatment of ARD can decrease the negative impact of aging and the burden of disease. The aging rate is closely associated with the production of high levels of reactive oxygen species (ROS). ROS-mediated oxidative stress in aging triggers aging-related changes through lipid peroxidation, protein oxidation, and DNA oxidation. Antioxidants can control autoxidation by scavenging free radicals or inhibiting their formation, thereby reducing oxidative stress. Benefiting from significant advances in nanotechnology, a large number of nanomaterials with ROS-scavenging capabilities have been developed. ROS-scavenging nanomaterials can be divided into two categories: nanomaterials as carriers for delivering ROS-scavenging drugs, and nanomaterials themselves with ROS-scavenging activity. This study summarizes the current advances in ROS-scavenging nanomaterials for prevention and treatment of ARD, highlights the potential mechanisms of the nanomaterials used and discusses the challenges and prospects for their applications. Reactive oxygen species ; Scavenging ; Chemistry ; Medicine ; Nanotechnology ; Antioxidant ; Biochemistry ; Materials science Advanced Nanomaterials in Catalysis ; Hydrogen's biological and therapeutic effects ; Medical and Biological Ozone Research