jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Valentine P. Ananikov
Source: ChemRxiv (preprint, 2024-04-12)
Field: Materials Science — Materials Chemistry

Top 20 Influential AI-Based Technologies in Chemistry The beginning and ripening of digital chemistry is analyzed focusing on the role of artificial intelligence (AI) in an expected leap in chemical sciences to bring this area to the next evolutionary level. The analytic description selects and highlights the top 20 AI-based technologies and 7 broader themes that are reshaping the field. It underscores the integration of digital tools such as machine learning, big data, digital twins, the Internet of Things (IoT), robotic platforms, smart control of chemical processes, virtual reality and blockchain, among many others, in enhancing research methods, educational approaches, and industrial practices in chemistry. The significance of this study lies in its focused overview of how these digital innovations foster a more efficient, sustainable, and innovative future in chemical sciences. This article not only illustrates the transformative impact of these technologies but also draws new pathways in chemistry, offering a broad appeal to researchers, educators, and industry professionals to embrace these advancements for addressing contemporary challenges in the field. Transformative learning ; Field (mathematics) ; Big data ; Appeal ; Data science ; Computer science ; Emerging technologies ; Engineering ethics ; Chemistry ; Nanotechnology ; Engineering ; Artificial intelligence ; Sociology ; Political science ; Materials science Machine Learning in Materials Science
Authors: Nour Muhammad Salih Mahdi | Ayad K. Hassan | Fatima J. Al-Hasani | Waleed Ameen Mahmoud Al-Jawher
Source: Journal Port Science Research (article, 2024-08-12)
Field: Materials Science — Biomaterials

Classification Of Biomaterials and Their Applications Biomaterials, designed to interact with living systems, play a vital role in various medical applications. Classifying these materials effectively is crucial for understanding their properties and ensuring optimal use. Biomaterials are classified based on their chemical composition, structure, and properties relevant for biological applications. In this paper five types of Biomaterials classification methods are given namely. Chemical, Functional, Source of biomaterials, Structural and Smart levels classification. Each of these five-biomaterial classification method offers unique advantages and disadvantages. Chemical Classification is simple and well-established method, easy to understand and interpret and provides a basic framework for material identification. Functional Classification is directly related to the application of the biomaterial, provides insight into the desired material properties and useful for identifying materials for specific therapeutic needs. Source-Based Classification is straightforward method based on material origin and can be useful for initial categorization and understanding general material properties (e.g., natural materials often biocompatible). Structural Classification provides information about material properties like strength, degradation, and permeability and can be relevant for understanding biocompatibility and material performance. Smart Level Classification captures the advanced functionalities of next-generation biomaterials, provides insights into targeted drug delivery or controlled cell interactions and useful for identifying materials for specific therapeutic applications. The most suitable method depends on the specific context and information needs. Computer science ; Materials science Collagen: Extraction and Characterization
Authors: Vadim Korolev | Artem Mitrofanov
Source: iScience (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

The carbon footprint of predicting CO2 storage capacity in metal-organic frameworks within neural networks While artificial intelligence drives remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate environmental impacts of deep learning in materials science through extensive benchmarking. In particular, a set of diverse neural networks is trained for a given supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective showed diminishing returns, manifesting themselves as a 28% decrease in mean absolute error and nearly a 15,000% increase in the carbon footprint of model training in 2016-2022. By means of up-to-date graphics processing units, it is possible to partially offset the immense growth of GHG emissions. Nonetheless, the practice of employing energy-efficient hardware is overlooked by the materials informatics community, as follows from a literature analysis in the field. On the basis of our findings, we encourage researchers to report GHG emissions together with standard performance metrics. Greenhouse gas ; Benchmarking ; Carbon footprint ; Artificial neural network ; Computer science ; Deep learning ; Inference ; Graphics ; Artificial intelligence ; Data science ; Environmental economics ; Machine learning ; Ecology ; Economics ; Management Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; CO2 Reduction Techniques and Catalysts
Authors: Victor Trinquet | Matthew L. Evans | Cameron J. Hargreaves | Pierre-Paul De Breuck | Gian‐Marco Rignanese
Source: Faraday Discussions (preprint, 2024-07-10)
Field: Materials Science — Materials Chemistry

Optical materials discovery and design with federated databases and machine learning Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested. Computer science ; Workflow ; Rendering (computer graphics) ; Database ; Machine learning ; Artificial intelligence ; Computation ; Data mining ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Geochemistry and Geologic Mapping
Authors: Prathamesh Satpute | Saurabh Tiwari | Maneet Gupta | Supriyo Ghosh
Source: Materials Today Communications (article, 2024-06-19)
Field: Materials Science — Materials Chemistry

Exploring large language models for microstructure evolution in materials Leverage (statistics) ; Context (archaeology) ; Computer science ; Code (set theory) ; Exploit ; Management science ; Artificial intelligence ; Engineering ; Programming language ; Biology Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Solidification and crystal growth phenomena
Authors: Junkil Park | Youhan Lee | Jihan Kim
Source: ChemRxiv (preprint, 2024-07-04)
Field: Materials Science — Materials Chemistry

Multi-modal conditioning for metal-organic frameworks generation using 3D modeling techniques The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) were employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibited exceptional generation performance, and demonstrated its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations. Computer science ; Flexibility (engineering) ; Categorical variable ; Generative grammar ; Representation (politics) ; Generative model ; Modal ; Modalities ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics Machine Learning in Materials Science
Authors: Ioanna Pallikara | Jonathan M. Skelton | Lauren E. Hatcher | Anuradha R. Pallipurath
Source: Crystal Growth & Design (review, 2024-08-19)
Field: Materials Science — Materials Chemistry

Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design When Olga Kennard founded the Cambridge Crystallographic Data Centre in 1965, the Cambridge Structural Database was a pioneering attempt to collect scientific data in a standard format. Since then, it has evolved into an indispensable resource in contemporary molecular materials science, with over 1.25 million structures and comprehensive software tools for searching, visualizing and analyzing the data. In this perspective, we discuss the use of the CSD and CCDC tools to address the multiscale challenge of predictive materials design. We provide an overview of the core capabilities of the CSD and CCDC software and demonstrate their application to a range of materials design problems with recent case studies drawn from topical research areas, focusing in particular on the use of data mining and machine learning techniques. We also identify several challenges that can be addressed with existing capabilities or through new capabilities with varying levels of development effort. Perspective (graphical) ; Computer science ; Resource (disambiguation) ; Software ; Data science ; Database ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Natanael de Sousa Sousa | R.F. Silva | Jaldyr de Jesus Gomes Varela Júnior | Adeílton Pereira Maciel
Source: Langmuir (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Advances in Selective Detection of Cadaverine by Electronic, Optical, and Work Function Sensors Based on Cu-Modified B<sub>12</sub>N<sub>12</sub> and Al<sub>12</sub>N<sub>12</sub> Nanocages: A Density Functional Theory (DFT) Study [Image: see text] This work explores Cu-modified B(12)N(12) and Al(12)N(12) nanocages for cadaverine diamine (Cad) detection using advanced density functional theory (DFT) calculations. The study found that Cu modification altered the geometry of the nanocages, increased the dipole moment, reduced the energy gap, and enhanced the reactivity. While pristine B(12)N(12) and Al(12)N(12) were not sensitive to Cad, the modified Cu(b(64))B(12)N(12) and Cu(b(66))Al(12)N(12) nanocages showed significantly higher electronic sensitivity (Δgap = 39.8% and 35.6%, respectively), surpassing the literature data. However, molecular dynamics (MD) revealed that the Cu(b(66))Al(12)N(12) nanocage is not stable in the long term, since the nanocage changes configuration to Cu(b(64))Al(12)N(12), which is less sensitive and has an even longer recovery time for Cad sensing. Adsorption energy analysis (E(ads)) showed a strong interaction of Cad/nanocages, while charge analysis suggested that the nanocages act as Lewis acids, accepting electrons from Cad. UV–vis spectra confirmed that Cu(b(64))B(12)N(12) responds optically to the presence of Cad. Furthermore, Cu(b(64))B(12)N(12) showed greater sensitivity to Cad compared to NO, H(2), H(2)S, CO, COCl(2), N(2)O, N(2) gases, or H(2)O, showing high selectivity to diamine against interfering gases or water, standing out as a promising material for environmental applications in electronic, optical or work function sensors for cadaverine detection, even in humid environments. Cadaverine ; Work (physics) ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Nanotechnology ; Physics ; Chromatography ; Thermodynamics ; Organic chemistry Boron and Carbon Nanomaterials Research ; Anesthesia and Neurotoxicity Research ; Boron Compounds in Chemistry
Authors: M. H. Tareq | Sadeq H. Lafta | Hussain Wafaa A.
Source: Eurasian Physical Technical Journal (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Исследование магнитных свойств наночастиц CsyCo1-0.5yFe2O4 при низком молярном соотношении Эффект замещения феррита ранее не использовавшимися элементами для управления магнитными свойствами представляет большой интерес для исследователей. Данное исследование иллюстрирует влияние низкого замещения Cs с молярными соотношениями y, равных 0,0, 0,05, 0,15 и 0,25, на структурные и магнитные свойства наночастиц CsyCo1-0.5yFe2O4. Метод синтеза являлся методом осаждения. Хлориды металлов использовались для проведения реакции в дистиллированной воде с использованием NaOH для достижения pH 10. Для всех образцов были проведены рентгеновская дифракция, полевая эмиссионная сканирующая электронная микроскопия, электронно-дисперсионная рентгенография и магнитометрия вибрирующих образцов. У всех образцов шпинельная структура в значительной степени совпадает с структурой феррита Со. Наблюдалось общее увеличение постоянной решетки с ростом содержания Cs, в то время как размер кристаллитов уменьшался примерно с 18-ти до 12,2 нм при увеличении молярного соотношения от 0 до 0,25. Электронно-микроскопическое исследование показало, что все образцы имеют сферические наночастицы без каких-либо других форм. Средний размер частиц составлял от 40 до 60 нм при увеличении содержания Cs1+. Магнитные параметры в основном показали относительно высокую коэрцитивную силу (широкие петли) и снижение насыщенности намагниченности (до 50,43 мкГ/г), кристаллической анизотропной постоянной и коэффициента квадратичности. Coercivity ; Crystallite ; Materials science ; Lattice constant ; Analytical Chemistry (journal) ; Scanning electron microscope ; Spinel ; Distilled water ; Magnetometer ; Anisotropy ; Ferrite (magnet) ; Diffraction ; Nuclear magnetic resonance ; Magnetic field ; Chemistry ; Condensed matter physics ; Metallurgy ; Composite material ; Optics ; Chromatography ; Physics Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Multiferroics and related materials
Authors: Hanfeng Zhai | Hongxia Hao | Jingjie Yeo
Source: APL Materials (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Benchmarking inverse optimization algorithms for materials design Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algorithms are not as widely applied to materials discovery applications as machine learning methods. There are ongoing challenges in applying different optimization algorithms to discover materials with single- or multi-elemental compositions and how these algorithms differ in mining the ideal materials. We comprehensively compare 11 different optimization algorithms for the design of single- and multi-elemental crystals with targeted properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing the parameterized elemental composition representations, we estimated the unique counts of elemental compositions, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials’ representations and objectives. We found that nature-inspired algorithms contain more uncertainties in the defined elemental composition design tasks, which correspond to their dependency on multiple hyperparameters. Runge–Kutta optimization (RUN) exhibits higher mean objectives, whereas Bayesian optimization (BO) displayed low mean objectives compared with other methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more elemental compositions and hence have lower mean objectives, yet RUN will repeatedly sample the targeted elemental compositions with higher objective values. Our work sheds light on the automated digital design of materials with single- and multi-elemental compositions and is expected to elicit future studies on materials optimization, such as composite and alloy design based on specific desired properties. Bayesian optimization ; Algorithm ; Elemental analysis ; Computer science ; Metaheuristic ; Materials science ; Inverse ; Machine learning ; Mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Memory and Neural Computing
Authors: Xin Yang | Martin Hoffmann Petersen | Renata Sechi | William Sandholt Hansen | Sam Walton Norwood | Yogeshwaran Krishnan | Smobin Vincent | Jonas Busk | François Cornet | Ole Winther | Juan Maria Garcia Lastra | Tejs Vegge | Heine Anton Hansen | Arghya Bhowmik
Source: ChemRxiv (preprint, 2024-02-16)
Field: Materials Science — Materials Chemistry

CURATOR: Building Robust Machine Learning Potentials for Atomistic Simulations Autonomously with Batch Active Learning To enable fast, resource efficient development and broad scale deployment of of high accuracy Machine-Learned Interatomic Potentials (MLIPs) with minimum expert involvement, we introduce CURATOR, an autonomous batch active learning workflow for constructing MLIPs. CURATOR integrates state of the art models, uncertainty quantification techniques, batch selection algorithms with user defined labeling and chemical-structure space exploration methods for data and compute efficient active learning. We also developed a novel efficient gradient computation method that calculates forces and stress based on the energy derivative with respect to accelerate CURATOR. Our evaluation across different chemical systems demonstrates that CURATOR considerably reduces the computational resources and time required to develop reliable MLIPs. In practical applications in novel complex materials and interfaces, CURATOR shows promising results, underscoring its potential in accelerating materials discovery. The flexibility and efficiency of CURATOR mark a significant advancement in the field of computational materials science, paving the way for more efficient and larger time-length scale atomistic simulations. Workflow ; Computer science ; Flexibility (engineering) ; Software deployment ; Computation ; Artificial intelligence ; Scale (ratio) ; Active learning (machine learning) ; Field (mathematics) ; Computational science ; Resource (disambiguation) ; Machine learning ; Algorithm ; Database ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Protein Structure and Dynamics
Authors: Jian Chen | Xiaoyu Cao | Wei Liu | Jianghua Liu | Liang Qi | Minmin Wei | Xiangbin Zou
Source: Foods (article, 2024-09-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Functionalized MXene (Ti3C2TX) Loaded with Ag Nanoparticles as a Raman Scattering Substrate for Rapid Furfural Detection in Baijiu Furfural is an essential compound that contributes to the distinctive flavor of sauce-flavored Baijiu. However, traditional detection methods are hindered by lengthy and complex sample preparation procedures, as well as the need for expensive equipment. Therefore, there is an urgent need for a new approach that allows rapid detection. In this study, we developed a novel surface-enhanced Raman spectroscopy (SERS) substrate by constructing MXene (Ti3C2TX) @Ag nanoparticles (Ag NPs) through an electrostatic attraction method. The MXene (Ti3C2TX) @Ag NPs were successfully fabricated, with adsorbed NaCl-treated Ag NPs uniformly absorbed on the surface of MXene (Ti3C2TX), creating high-density distributed SERS “hot spots”. The prepared substrate demonstrated excellent sensitivity, uniformity, repeatability, and long-term stability, with a low detectable concentration of 10−9 M for R6G (Rhodamine 6G) and an enhancement factor of up to 7.08 × 105. When applied for the in situ SERS detection of furfural in Baijiu, the detection limit was as low as 0.5 mg/L. Overall, the proposed method offers rapid, low-cost, and sensitive quantitative analysis, which is significant not only for detecting furfural in Baijiu but also for identifying hazardous substances and distinguishing between authentic and counterfeit Baijiu products. Furfural ; Rhodamine 6G ; Substrate (aquarium) ; Detection limit ; Raman spectroscopy ; Materials science ; Raman scattering ; Nanoparticle ; Adsorption ; Chemical engineering ; Nanotechnology ; Chemistry ; Chromatography ; Organic chemistry ; Molecule ; Catalysis Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Melamine detection and toxicity
Authors: Zhenglin Zeng | W. T. Tan | Yanhe Deng | Quan Cheng | Liuyue Fu | Chao Tang
Source: Journal of Applied Physics (article, 2024-03-04)
Field: Materials Science — Materials Chemistry

Molecular simulation of different types of polysilsesquioxane doped cellulose insulating paper: A guide for special cellulose insulating paper To develop special insulating paper is of great significance to promote the service life of transformers. Using molecular simulation to guide the development of special insulating paper can greatly reduce the trial-and-error rate and waste of resources in traditional experiments. The effect of different types of polysilsesquioxane (POSS) on cellulose insulating paper was investigated by using molecular simulation. This paper investigated the thermal stability and mechanical properties and electrical characteristics of caged POSS, semi-caged POSS, and ladder-like POSS doped cellulose insulating paper. The results show that POSS with all types can enhance the performance of cellulose insulating paper, and ladder-like POSS possess the best modification effect. The glass transition temperature was increased by 58 K, and the bulk modulus, shear modulus, and elastic modulus of cellulose insulating paper doped with ladder-like POSS can improve up to 27.07%, 45.67%, and 41.28%, respectively. Meanwhile, the dielectric properties of ladder-like POSS modified insulating paper are also significantly improved. The findings of this paper propose a method for the preparation of ladder-like POSS modified insulating paper, which provides theoretical guidance for the experimental preparation of special insulating paper. Materials science ; Cellulose ; Modulus ; Dielectric ; Composite material ; Thermal stability ; Doping ; Service life ; Electrical insulation paper ; Transformer ; Chemical engineering ; Optoelectronics ; Electrical engineering Silicone and Siloxane Chemistry ; High voltage insulation and dielectric phenomena ; Surface Modification and Superhydrophobicity