jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eleftherios Christofi | Petra Bačová | Vagelis Harmandaris
Source: Journal of Chemical Information and Modeling (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator ("one-to-one" problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones ("one-to-many" problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach. Macromolecule ; Resolution (logic) ; Degrees of freedom (physics and chemistry) ; Transformation (genetics) ; Computer science ; Range (aeronautics) ; Simple (philosophy) ; Space (punctuation) ; Statistical physics ; Biological system ; Artificial intelligence ; Physics ; Materials science ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Xiaoming Song | Xiufang Hou | Mingxuan Dang | Qingxia Zhao | Shuai Liu | Zhihu Ma | Yixia Ren
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-06-12)
Field: Chemistry — Inorganic Chemistry

Design and preparation of a multi-responsive Cd-based fluorescent coordination polymer for smart sensing of nitrobenzene and ornidazole Ornidazole ; Nitrobenzene ; Fluorescence ; Combinatorial chemistry ; Chemistry ; Chromatography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials
Authors: Adedotun Adetunla | Esther T. Akinlabi | Tien‐Chien Jen | Samuel-Soma M. Ajibade
Source:  (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Harnessing the Power of Artificial Intelligence in Materials Science: An Overview The integration of artificial intelligence (AI) into the realm of material science has ushered in a new era, by changing the process of material discovery and design. Leveraging advanced computational methods, machine learning algorithms, and predictive modeling, AI accelerates the identification of novel materials with tailored properties. From quantum simulations to high-throughput experimentation, AI-driven techniques enable rapid screening and prediction of material behaviors, significantly reducing the time and resources traditionally required for innovation. This synergy has paved the way for the creation of smart and adaptive materials, responsive to external stimuli and tailored for specific applications across industries. The marriage of AI and material science extends beyond discovery, encompassing efficient process optimization, manufacturing improvements, and the management of vast datasets through materials informatics. Challenges, including ethical considerations, data privacy, and responsible AI practices, must be navigated for sustainable integration. Looking forward, the collaborative potential of AI and material science promises continuous advancements. Ongoing research in machine learning, deep learning, and materials informatics anticipates breakthroughs in material applications, pushing the boundaries of innovation and sustainability. The union of AI and material science not only reshapes the landscape of scientific discovery but also holds the key to unlocking unprecedented opportunities for intelligent materials that will define the technological future. Computer science ; Power (physics) ; Artificial intelligence ; Physics Machine Learning in Materials Science
Authors: Sathyanarayanan Seetharaman | S. Suresh | Gopinath Dhamodaran | G.R. Kannan | S. Suyambazhahan | K.R. Suresh Kumar | Dinesh Babu Munuswamy
Source: Energy (article, 2024-08-30)
Field: Materials Science — Materials Chemistry

Catalytic converter performance prediction and engine optimization when powered by diisopropyl ether/gasoline blends: Combined application of response surface methodology and artificial neural network Diisopropyl ether ; Artificial neural network ; Response surface methodology ; Gasoline ; Catalytic converter ; Catalysis ; Automotive engineering ; Computer science ; Materials science ; Engineering ; Chemistry ; Organic chemistry ; Artificial intelligence ; Waste management ; Machine learning Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Biodiesel Production and Applications
Authors: Yi-Fan Hou | Lina Zhang | Quanhao Zhang | Fuchun Ge | Pavlo O. Dral
Source: Journal of Chemical Theory and Computation (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Physics-Informed Active Learning for Accelerating Quantum Chemical Simulations Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here, we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, automatic selection of initial data, uncertainty quantification, and convergence monitoring. The versatility of this protocol is shown in our implementation of quasi-classical molecular dynamics for simulating vibrational spectra, conformer search of a key biochemical molecule, and time-resolved mechanism of the Diels-Alder reaction. These investigations took us days instead of weeks of pure quantum chemical calculations on a high-performance computing cluster. Quantum chemical ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Quantum Computing Algorithms and Architecture
Authors: Adolfo E. Rosado-Miranda | Víctor Posligua | Javier Fdez. Sanz | Antonio M. Márquez | Pinku Nath | José J. Plata
Source: ACS Applied Materials & Interfaces (article, 2024-05-21)
Field: Materials Science — Materials Chemistry

Design Principles Guided by DFT Calculations and High-Throughput Frameworks for the Discovery of New Diamond-like Chalcogenide Thermoelectric Materials Rational design principles are one pathway to discovering new materials. However, technological breakthroughs rarely occur in this way because these design principles are usually based on incremental advances that seldom lead to disruptive applications. The emergence of machine-learning (ML) and high-throughput (HT) techniques has changed the paradigm, opening up new possibilities for efficiently screening large chemical spaces and creating on-the-fly design principles for the discovery of novel materials with desired properties. In this work, the approach is used to discover novel thermoelectric (TE) materials based on quaternary diamond-like chalcogenides. A HT framework that integrates density functional theory calculations, ML, and the solution of the Boltzmann transport equation is used to efficiently rationalize the transport properties of these compounds and identify those with potential as TE materials, achieving <i>ZT</i> values above 2. Materials science ; Chalcogenide ; Density functional theory ; Thermoelectric materials ; Nanotechnology ; Thermoelectric effect ; Material Design ; Throughput ; Diamond ; Rational design ; Work (physics) ; Computer science ; Biochemical engineering ; Computational chemistry ; Thermodynamics ; Physics ; Optoelectronics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Ümit Ecer | Berdan Ulaş | Şakir Yılmaz
Source: Langmuir (article, 2024-09-04)
Field: Chemistry — Organic Chemistry

Application of ANN and RSM for Rhodamine B and Safranine-O Decolorization on Zinc-Carbon Battery Waste Derived Ag/CoFe-LDH/rGO Catalyst The present work is first aimed at recovering graphite from carbon rods of waste zinc-carbon (Zn-C) batteries for applications such as wastewater treatment, in order to contribute to the development of a sustainable environment. Then, a composite material, cobalt-iron layered double hydroxide combination with reduced graphene oxide, and with subsequent Ag nanoparticles deposition via NaBH<sub>4</sub> reduction method (Ag/CoFe-LDH/rGO) was prepared for the catalytic activity of Rhodamine B (RhB) and Safranine-O (SO) as model contaminants from aquatic media. The catalytic activity of RhB and SO by Ag/CoFe-LDH/rGO in the presence of NaBH<sub>4</sub> was studied to model and optimize the process parameters (NaBH<sub>4</sub> amount, reaction time, initial dye concentration (<i>C</i><sub>o</sub>), and catalyst dosage) via central composite design (CCD)-response surface methodology (RSM). Also, an artificial neural network (ANN) model was developed to estimate the catalytic activity of each dye using an RSM data set. The catalytic activities of 99.54% and 99.96% were obtained for RhB and SO dyes, respectively, under the optimal conditions: NaBH<sub>4</sub> amount 12.32 mM, reaction time 3.19 min, <i>C</i><sub>o</sub> 33.46 mg/L, and catalyst dosage 1.24 mg/mL for RhB dye; NaBH<sub>4</sub> amount 16.76 mM, reaction time 3.06 min, <i>C</i><sub>o</sub> 15.10 mg/L, and catalyst dosage 1.46 mg/mL for SO dye. The optimum conditions of process parameters by ANN with gray wolf optimizer (GWO) were in good agreement with the points determined the RSM-CCD. These results demonstrate that RSM and ANN approaches can be applied practically and efficiently to maximize the catalytic activity of RhB and SO by Ag/CoFe-LDH/rGO in the existence of NaBH<sub>4</sub>. On the other hand, from the kinetic and thermodynamic studies, the positive activation enthalpy, Δ<i>H</i><sup>#</sup> and the negative activation entropy, Δ<i>S</i><sup>#</sup> values for each dye demonstrated that the catalytic performance was endothermic and less random at the solid/liquid interface. Graphene ; Graphite ; Carbon fibers ; Catalysis ; Zinc ; Hydroxide ; Materials science ; Battery (electricity) ; Wastewater ; Rhodamine B ; Cobalt ; Oxide ; Layered double hydroxides ; Composite number ; Graphite oxide ; Chemical engineering ; Nanoparticle ; Inorganic chemistry ; Chemistry ; Metallurgy ; Nanotechnology ; Waste management ; Organic chemistry ; Photocatalysis ; Composite material Nanomaterials for catalytic reactions ; Extraction and Separation Processes ; Advancements in Battery Materials
Authors: Harry D. A. Brough | David Cheneler | John G. Hardy
Source: Biomacromolecules (review, 2024-10-22)
Field: Materials Science — Biomaterials

Progress in Multiscale Modeling of Silk Materials As a result of their hierarchical structure and biological processing, silk fibers rank among nature's most remarkable materials. The biocompatibility of silk-based materials and the exceptional mechanical properties of certain fibers has inspired the use of silk in numerous technical and medical applications. In recent years, computational modeling has clarified the relationship between the molecular architecture and emergent properties of silk fibers and has demonstrated predictive power in studies on novel biomaterials. Here, we review advances in modeling the structure and properties of natural and synthetic silk-based materials, from early structural studies of silkworm cocoon fibers to cutting-edge atomistic simulations of spider silk nanofibrils and the recent use of machine learning models. We explore applications of modeling across length scales: from quantum mechanical studies on model peptides, to atomistic and coarse-grained molecular dynamics simulations of silk proteins, to finite element analysis of spider webs. As computational power and algorithmic efficiency continue to advance, we expect multiscale modeling to become an indispensable tool for understanding nature's most impressive fibers and developing bioinspired functional materials. SILK ; Polymer science ; Materials science ; Nanotechnology ; Composite material Silk-based biomaterials and applications ; Metal Forming Simulation Techniques ; Silkworms and Sericulture Research
Authors: Wenhao Sun | Nicholas David
Source: Faraday Discussions (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes Synthesis of predicted materials is the key and final step needed to realize a vision of computationally accelerated materials discovery. Because so many materials have been previously synthesized, one would anticipate that text-mining synthesis recipes from the literature would yield a valuable dataset to train machine-learning models that can predict synthesis recipes for new materials. Between 2016 and 2019, the corresponding author (Wenhao Sun) participated in efforts to text-mine 31 782 solid-state synthesis recipes and 35 675 solution-based synthesis recipes from the literature. Here, we characterize these datasets and show that they do not satisfy the "4 Vs" of data-science-that is: volume, variety, veracity and velocity. For this reason, we believe that machine-learned regression or classification models built from these datasets will have limited utility in guiding the predictive synthesis of novel materials. On the other hand, these large datasets provided an opportunity to identify anomalous synthesis recipes-which in fact did inspire new hypotheses on how materials form, which we later validated by experiment. Our case study here urges a re-evaluation on how to extract the most value from large historical materials-science datasets. Reflection (computer programming) ; Key (lock) ; Computer science ; Critical reflection ; Data science ; Psychology ; Programming language Machine Learning in Materials Science
Authors: Mingzan Zhang | Tuo Zhang | Yuan Wang | Xueyi Duan | Lulu Pu | Yuan Zhang | Qin Li | Yabing Liu
Source: Chemosensors (article, 2024-09-11)
Field: Chemistry — Analytical Chemistry

Accurate Prediction of Tea Catechin Content with Near-Infrared Spectroscopy by Deep Learning Based on Channel and Spatial Attention Mechanisms The assessment of catechin content stands as a pivotal determinant of tea quality. In tea production and quality grading, the development of accurate and non-destructive techniques for the accurate prediction of various catechin content is paramount. Near-infrared spectroscopy (NIRS) has emerged as a widely employed tool for analyzing the chemical composition of tea. Nevertheless, the spectral information obtained from NIRS faces challenges when discerning different types of catechins in black tea, owing to their similar physical and chemical properties. Moreover, the vast number of NIRS wavelengths exceeds the available tea samples, further complicating the accurate assessment of catechin content. This study introduces a novel deep learning approach that integrates specific wavelength selection and attention mechanisms to accurately predict the content of various catechins in black tea simultaneously. First, a wavelength selection algorithm is proposed based on feature interval combination sensitivity segmentation, which effectively extracts the NIRS feature information of tea. Subsequently, a one-dimensional convolutional neural network (CNN) incorporating channel and spatial–sequential attention mechanisms is devised to independently extract the key features from the selected wavelength variables. Finally, a multi-output predictor is employed to accurately predict the four main catechins in tea. The experimental results demonstrate the superiority of the proposed model over existing methods in terms of prediction accuracy and stability (R2 = 0.92, RMSE = 0.018 for epicatechin; R2 = 0.96, RMSE = 0.11 for epicatechin gallate; R2 = 0.97, RMSE = 0.14 for epigallocatechin; R2 = 0.97, RMSE = 0.32 for epigallocatechin gallate). This innovative deep learning approach amalgamates wavelength selection with attention mechanisms, provides a new perspective for the simultaneous assessment of the major components in tea, and contributes to the advancement of precision management in the tea industry’s production and grading processes. Catechin ; Mean squared error ; Gallate ; Artificial intelligence ; Pattern recognition (psychology) ; Convolutional neural network ; Near-infrared spectroscopy ; Black tea ; Computer science ; Epigallocatechin gallate ; Biological system ; Chemistry ; Mathematics ; Food science ; Statistics ; Polyphenol ; Physics ; Biology ; Optics ; Biochemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Gulshan Verma | Anisha Gokarna | Hind Kadiri | Gilles Lerondel | Ankur Gupta
Source: Applied Materials Today (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Flexible ZnO nanowire platform by metal-seeded chemical bath deposition: Parametric analysis and predictive modeling Deposition (geology) ; Nanowire ; Seeding ; Materials science ; Parametric statistics ; Nanotechnology ; Chemical bath deposition ; Metal ; Chemical deposition ; Chemical engineering ; Optoelectronics ; Metallurgy ; Engineering ; Thin film ; Aerospace engineering ; Geology ; Mathematics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Quantum Dots Synthesis And Properties
Authors: Mattia Salomone | Michele Re Fiorentin | Francesca Risplendi | Federico Raffone | Timo Sommer | Max García‐Melchor | Giancarlo Cicero
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Efficient mapping of CO adsorption on Cu<sub>1−<i>x</i></sub>M<sub><i>x</i></sub> bimetallic alloys <i>via</i> machine learning A two-step machine learning model to predict CO binding energies on CuM(111)/(100) bimetallic surfaces and enhance the CO 2 RR selectivity towards C 2 products. Bimetallic strip ; Adsorption ; Selectivity ; Materials science ; Crystallography ; Chemistry ; Physical chemistry ; Metallurgy ; Catalysis ; Organic chemistry ; Metal Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Odongo Francis Ngome Okello | Dong‐Hwan Yang | Seung‐Young Seo | Jewook Park | Gunho Moon | Dongwon Shin | Yuseong Chu | Sejung Yang | Teruyasu Mizoguchi | Moon‐Ho Jo | Si‐Young Choi
Source: ACS Nano (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Atomistic Probing of Defect-Engineered 2H-MoTe<sub>2</sub> Monolayers Point defects dictate various physical, chemical, and optoelectronic properties of two-dimensional (2D) materials, and therefore, a rudimentary understanding of the formation and spatial distribution of point defects is a key to advancement in 2D material-based nanotechnology. In this work, we performed the demonstration to directly probe the point defects in 2H-MoTe<sub>2</sub> monolayers that are tactically exposed to (i) 200 °C-vacuum-annealing and (ii) 532 nm-laser-illumination; and accordingly, we utilize a deep learning algorithm to classify and quantify the generated point defects. We discovered that tellurium-related defects are mainly generated in both 2H-MoTe<sub>2</sub> samples; but interestingly, 200 °C-vacuum-annealing and 532 nm-laser-illumination modulate a strong n-type and strong p-type 2H-MoTe<sub>2,</sub> respectively. While 200 °C-vacuum-annealing generates tellurium vacancies or tellurium adatoms, 532 nm-laser-illumination prompts oxygen atoms to be adsorbed/chemisorbed at tellurium vacancies, giving rise to the p-type characteristic. This work significantly advances the current understanding of point defect engineering in 2H-MoTe<sub>2</sub> monolayers and other 2D materials, which is critical for developing nanoscale devices with desired functionality. Monolayer ; Annealing (glass) ; Tellurium ; Materials science ; Crystallographic defect ; Nanoscopic scale ; Chemical physics ; Nanotechnology ; Ultra-high vacuum ; Optoelectronics ; Crystallography ; Chemistry ; Metallurgy 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; Quantum Dots Synthesis And Properties
Authors: Qadeer Akbar Sial | Usman Safder | Rana Basit Ali | Shahid Iqbal | Le Thai Duy | Bruno G. Pollet | Shankara S. Kalanur | Hyungtak Seo
Source: Journal of Power Sources (article, 2024-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced supercapacitive performance of lead vanadate hybrid architect on nickel foam with machine learning-driven capacitance prediction Supercapacitor ; Lead (geology) ; Capacitance ; Vanadate ; Nickel ; Materials science ; Metal foam ; Process engineering ; Chemical engineering ; Metallurgy ; Composite material ; Engineering ; Chemistry ; Porosity ; Electrode ; Geology Supercapacitor Materials and Fabrication ; Nanoporous metals and alloys ; Electrospun Nanofibers in Biomedical Applications