jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Enrico Di Lucente | Michele Simoncelli | Nicola Marzari
Source: arXiv (Cornell University) (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Crossover from Boltzmann to Wigner thermal transport in thermoelectric skutterudites Skutterudites are crystals with a cage-like structure that can be augmented with filler atoms ("rattlers"), usually leading to a reduction in thermal conductivity that can be exploited for thermoelectric applications. Here, we leverage the recently introduced Wigner formulation of thermal transport to elucidate the microscopic physics underlying heat conduction in skutterudites, showing that filler atoms can drive a crossover from the Boltzmann to the Wigner regimes of thermal transport, i.e., from particle-like conduction to wave-like tunnelling. At temperatures where the thermoelectric efficiency of skutterudites is largest, wave-like tunneling can become comparable to particle-like propagation. We define a Boltzmann deviation descriptor able to differentiate the two regimes and relate the competition between the two mechanisms to the materials' chemistry, providing a design strategy to select rattlers and identify optimal compositions for thermoelectric applications. Thermoelectric effect ; Thermal conduction ; Condensed matter physics ; Thermal conductivity ; Quantum tunnelling ; Crossover ; Thermal ; Materials science ; Boltzmann constant ; Thermoelectric materials ; Leverage (statistics) ; Physics ; Thermodynamics ; Mathematics ; Computer science Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Topological Materials and Phenomena
Authors: Jiacheng Xiong | Wei Zhang | Zunyun Fu | Jiatao Huang | Xiangtai Kong | Yitian Wang | Zhaoping Xiong | Mingyue Zheng
Source: ChemRxiv (preprint, 2023-12-26)
Field: Materials Science — Materials Chemistry

Improve retrosynthesis planning with a molecular editing language Retrosynthetic analysis is a fundamental strategy in the field of organic synthesis, and many computational methods have been developed to address this significant task. A widely adopted approach is to treat retrosynthetic prediction as a sequence-to-sequence (seq2seq) translation task, where the Simplified Molecular Input Line Entry System (SMILES) of a product is translated into the SMILES of its corresponding reactants. However, these sequence-based models using SMILES also face many issues, including limited performance, lack of interpretability, and controllability. In this work, we introduce a novel chemical language for retrosynthetic prediction named E-SMILES, which is an extension of SMILES specially designed for seq2seq retrosynthetic prediction. This language not only documents the static molecular structure but also encodes the editing operations of the molecule in the retrosynthetic process, enabling it to characterize retrosynthesis reactions more effectively. By using E-SMILES, seq2seq retrosynthetic models can simulate the stepwise retrosynthetic analysis strategy of chemists, ensuring the matching of atoms between the predicted reactants and product, and yielding more interpretable and controllable predictions. Furthermore, E-SMILES is naturally aligned with the product's SMILES, reducing the edit distance between the model's input and output sequences. This liberates the model from learning the complex SMILES syntax and allows it to focus more on the retrosynthesis process itself. Leveraging E-SMILES, our retrosynthesis model achieves top-1 accuracies of 58.9% and 68.5% on the USPTO-50k dataset, with and without given reaction class, respectively, significantly surpassing previous state-of-the-art results. We envisage that E-SMILES can serve as a new foundational tool, promoting the development of sequence-based retrosynthetic prediction methods. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Process (computing) ; Sequence (biology) ; Natural language processing ; Task (project management) ; Programming language ; Engineering ; Chemistry Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Md. Mottahir Alam | Ahteshamul Haque | Asif Irshad Khan | Samir Kasim | Amjad Ali Pasha | Aasim Zafar | Kashif Irshad | Anis Ahmad Chaudhary | Md Samsuzzaman | Rezaul Azim
Source: Journal of Mathematics (article, 2023-06-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence Solar energy is a significant, environment-friendly source of renewable energy. The solar absorber transforms solar radiation into heat energy as an effective green energy source. Therefore, increasing its absorbing capacity can improve a solar absorber’s effectiveness. This paper proposes a tungsten tantalum alloy with silicon dioxide (WTa-SiO2) ceramic layer-based solar absorber system with two different metasurfaces to enhance absorptivity and boost the solar absorber efficacy. The absorbance is also improved by adjusting the resonator thickness and material thickness, and the maximum visible light absorption is achieved by the suggested solar filter design. Moreover, Golden Eagle Optimization (GE)-based deep AlexNet algorithm is proposed for predicting the parameter variation and their effect on absorbance. The optimization technique is used to increase the effectiveness of the solar absorber by optimizing the design parameters. The features from the WTa-SiO2 design are extracted by the proposed Principal Component-Autoencoder (PC-AE) method. Experimental results show that the proposed system can effectively predict absorptivity with a reduced computational time. The proposed method demonstrates superior prediction performance with an absorption prediction efficiency of 99.8% compared to the existing methods. Thus, the proposed WTa-SiO2 metasurface-based solar absorber can be used for photovoltaic applications. Materials science ; Molar absorptivity ; Absorption (acoustics) ; Photovoltaic system ; Absorbance ; Solar energy ; Renewable energy ; Optoelectronics ; Solar cell ; Computer science ; Optics ; Engineering ; Composite material ; Physics ; Electrical engineering Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Acoustic Wave Phenomena Research
Authors: Astrid Klipfel | Yaël Frégier | Adlane Sayede | Zied Bouraoui
Source:  (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Unified Model for Crystalline Material Generation One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties. Recently, the problem of generating crystal materials has received increasing attention, however, it remains unclear to what extent, or in what way, we can develop generative models that consider both the periodicity and equivalence geometric of crystal structures. To alleviate this issue, we propose two unified models that act at the same time on crystal lattice and atomic positions using periodic equivariant architectures. Our models are capable to learn any arbitrary crystal lattice deformation by lowering the total energy to reach thermodynamic stability. Code and data are available at https://github.com/aklipf/GemsNet. Equivariant map ; Crystal (programming language) ; Computer science ; Lattice (music) ; Equivalence (formal languages) ; Code (set theory) ; Crystal structure ; Generative grammar ; Theoretical computer science ; Crystal structure prediction ; Mathematics ; Crystallography ; Physics ; Programming language ; Artificial intelligence ; Discrete mathematics ; Pure mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Titanium Alloys Microstructure and Properties
Authors: Faggo Abdullahi Adamu | Ain Aqilah Basirun | Ahmad Syazwan Ismail | Mohd Ezuan Khayat | Mohd Yunus Shukor
Source: Bioremediation Science and Technology Research (article, 2023-07-31)
Field: Chemistry — Analytical Chemistry

Isothermal Remodelling of the Biosorption of Congo Red onto Kaolin A remodeling analysis was conducted on the sorption isotherm data for Congo Red adsorption onto kaolin using nonlinear regression. (CR) also known as 1-naphthalenesulfonic acid, 3,3′-(4,4′-biphenylenebis(azo)) bis(4-amino-) disodium salt, is a synthetic anionic azo dye widely utilized in various industrial sectors, including rubber, plastic, textiles, paper, and printing. This dye is of significant interest due to its versatile applications and unique chemical properties. To prevent overfitting due to the limited data points, isotherm models with up to three parameters were utilized. Statistical analysis based on error function assessments, including root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), accuracy factor (AF), bias factor (BF), Bayesian Information Criterion (BIC), corrected AICc (Akaike Information Criterion), and Hannan-Quinn Criterion (HQC), revealed that the best performance was achieved by the Freundlich model, followed by the Langmuir and Jovanovic models, which ranked as the top three models.The best isotherm model was found to be the Unilan followed by (descending order) Brouers–Sotolongo, Hill, Sips and Langmuir. The Unilan maximum adsorption capacity, shows large deviation from the experimentally observed value with a large 95% confidence interval, indicating poor fitting parameters despite being the best model based on the error function analysis. The next best model was Brouers–Sotolongo with a maximum adsorption capacity, qmBS of 5.48 mg g-1 (95% confidence interval from 4.791 to 6.172) which agrees with experimental observations. The value of the maximum monolayer adsorption capacity for Congo Red binding to kaolin according to the Langmuir’s parameter qmL was 5.49 mg g-1 (95% Confidence interval from 5.018 to 5.967), while bL (L mg-1), the Langmuir model constants was 0.5 L mg-1 (95% C.I. from 0.285 to 0.710). Congo red ; Freundlich equation ; Adsorption ; Langmuir ; Akaike information criterion ; Langmuir adsorption model ; Mean squared error ; Mathematics ; Coefficient of determination ; Sorption ; Isothermal process ; Thermodynamics ; Chemistry ; Materials science ; Biological system ; Chromatography ; Statistics ; Organic chemistry ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Dye analysis and toxicity ; Computational Drug Discovery Methods
Authors: A.J. Cooper | Michael P. Howard | Sanket Kadulkar | D.H. Zhao | Kris T. Delaney | Venkat Ganesan | Thomas M. Truskett | Glenn H. Fredrickson
Source: The Journal of Chemical Physics (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

Multiscale modeling of solute diffusion in triblock copolymer membranes We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane. Membrane ; Thermal diffusivity ; Kinetic Monte Carlo ; Copolymer ; Multiscale modeling ; Materials science ; Chemical physics ; Lattice model (finance) ; Statistical physics ; Solvation ; Diffusion ; Monte Carlo method ; Polymer ; Thermodynamics ; Solvent ; Chemistry ; Computational chemistry ; Physics Block Copolymer Self-Assembly ; Polymer Surface Interaction Studies ; Electrostatics and Colloid Interactions
Authors: Andreas Krämer | Aleksander P. Durumeric | Nicholas E. Charron | Yaoyi Chen | Cecilia Clementi | Frank Noé
Source: arXiv (Cornell University) (preprint, 2023-02-14)
Field: Materials Science — Materials Chemistry

Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force-fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins Chignolin and Tryptophan Cage and published as open-source code. Force field (fiction) ; Molecular dynamics ; Granularity ; Flexibility (engineering) ; Representation (politics) ; Computer science ; Atom (system on chip) ; Field (mathematics) ; Potential of mean force ; Code (set theory) ; Dynamics (music) ; Algorithm ; Statistical physics ; Biological system ; Physics ; Artificial intelligence ; Chemistry ; Mathematics ; Computational chemistry ; Parallel computing ; Set (abstract data type) ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Suha Altarawneh | Hani M. El‐Kaderi | Alexander J. Richard | Osama M. Alakayleh | Ibtesam Y. Aljaafreh | Mansour H. Almatarneh | Taher S. Ababneh | Lo′ay A. Al-Momani | Rawan H. Aldalabeeh
Source: Polymers (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Synthesis, Characterization, and Environmental Applications of Novel Per-Fluorinated Organic Polymers with Azo- and Azomethine-Based Linkers via Nucleophilic Aromatic Substitution This study reports on the synthesis and characterization of novel perfluorinated organic polymers with azo- and azomethine-based linkers using nucleophilic aromatic substitution. The polymers were synthesized via the incorporation of decafluorobiphenyl and hexafluorobenzene linkers with diphenols in the basic medium. The variation in the linkers allowed the synthesis of polymers with different fluorine and nitrogen contents. The rich fluorine polymers were slightly soluble in THF and have shown molecular weights ranging from 4886 to 11,948 g/mol. All polymers exhibit thermal stability in the range of 350-500 °C, which can be attributed to their structural geometry, elemental contents, branching, and cross-linking. For instance, the cross-linked polymers with high nitrogen content, DAB-Z-1h and DAB-Z-1O, are more stable than azomethine-based polymers. The cross-linking was characterized by porosity measurements. The azo-based polymer exhibited the highest surface area of 770 m<sup>2</sup>/g with a pore volume of 0.35 cm<sup>3</sup>/g, while the open-chain azomethine-based polymer revealed the lowest surface area of 285 m<sup>2</sup>/g with a pore volume of 0.0872 cm<sup>3</sup>/g. Porous structures with varied hydrophobicities were investigated as adsorbents for separating water-benzene and water-phenol mixtures and selectively binding methane/carbon dioxide gases from the air. The most hydrophobic polymers containing the decafluorbiphenyl linker were suitable for benzene separation, while the best methane uptake values were 6.14 and 3.46 mg/g for DAB-Z-1O and DAB-A-1O, respectively. On the other hand, DAB-Z-1h, with the highest surface area and being rich in nitrogen sites, has recorded the highest CO<sub>2</sub> uptake at 298 K (17.25 mg/g). Nucleophilic substitution ; Nucleophilic aromatic substitution ; Polymer ; Characterization (materials science) ; Substitution (logic) ; Polymer chemistry ; Organic chemistry ; Materials science ; Substitution reaction ; Chemistry ; Nanotechnology ; Computer science Luminescence and Fluorescent Materials ; Covalent Organic Framework Applications ; Synthesis and properties of polymers
Authors: M. F. L. Abdullah
Source: International Journal of Advanced Research in Computer Science (review, 2023-02-21)
Field: Materials Science — Materials Chemistry

THE CUTTING EDGE TECHNOLOGIES IN COMPUTER SCIENCE: A REVIEW Technological gadgets, methods, and accomplishments that make use of the most recent and advanced IT advances are examples of cutting-edge technology. "Cutting edge" is a term commonly used to describe the most advanced and forward-thinking companies in the IT industry. The term "cutting-edge technology" is used to describe the most advanced and up-to-date technological features, as opposed to "bleeding-edge technology," which is so novel that it presents risks to consumers. While the term "technology" is most often associated with computer and electronic devices, it can refer to advancements in virtually any field. The author of this paper has properly cited the most up-to-date cutting-edge research. Computer science ; Enhanced Data Rates for GSM Evolution ; Term (time) ; Field (mathematics) ; Edge computing ; Emerging technologies ; Edge device ; Data science ; Telecommunications ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Big Data and Business Intelligence
Authors: Halah T. Mohammed | Ahmed M. Kamil | Hayder M. Abduljalil | Abbas A-Ali Drea | Mohammed A. Al‐Seady
Source: Baghdad Science Journal (article, 2023-01-21)
Field: Chemistry — Analytical Chemistry

Degradation of Indigo Dye Using Quantum Mechanical Calculations The semiempirical (PM3) and DFT quantum mechanical methods were used to investigate the theoretical degradation of Indigo dye. The chemical reactivity of the Indigo dye was evaluated by comparing the potential energy stability of the mean bonds. Seven transition states were suggested and studied to estimate the actually starting step of the degradation reaction. The bond length and bond angle calculations indicate that the best active site in the Indigo dye molecule is at C10=C11. The most possible transition states are examined for all suggested paths of Indigo dye degradation predicated on zero-point energy and imaginary frequency. The first starting step of the reaction mechanism is proposed. The change in enthalpy, Gibbs free energy and change in entropy of the overall reaction are equal to -548268.223 kcal/mol, 30831.951 kcal/mol and 48.552 cal/mol.deg, respectively. The activation energy is 46176.405 kcal/mol. The reaction rate is equal to . Indigo ; Enthalpy ; Gibbs free energy ; Reaction rate constant ; Chemistry ; Activation energy ; Potential energy surface ; Zero-point energy ; Degradation (telecommunications) ; Physical chemistry ; Thermodynamics ; Molecule ; Photochemistry ; Kinetics ; Organic chemistry ; Physics Dye analysis and toxicity
Authors: Michael Scherbela | Leon Gerard | Philipp Grohs
Source: arXiv (Cornell University) (preprint, 2023-03-17)
Field: Materials Science — Materials Chemistry

DeepErwin Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schrödinger equation. However, despite their success and favorable scaling, these methods are still computationally too costly for wide adoption. A significant obstacle is the requirement to optimize the wavefunction from scratch for each new system, thus requiring long optimization. In this work, we propose a novel neural network ansatz, which effectively maps uncorrelated, computationally cheap Hartree-Fock orbitals, to correlated, high-accuracy neural network orbitals. This ansatz is inherently capable of learning a single wavefunction across multiple compounds and geometries, as we demonstrate by successfully transferring a wavefunction model pre-trained on smaller fragments to larger compounds. Furthermore, we provide ample experimental evidence to support the idea that extensive pre-training of a such a generalized wavefunction model across different compounds and geometries could lead to a foundation wavefunction model. Such a model could yield high-accuracy ab-initio energies using only minimal computational effort for fine-tuning and evaluation of observables. Ansatz ; Wave function ; Atomic orbital ; Artificial neural network ; Scaling ; Observable ; Quantum mechanics ; Statistical physics ; Physics ; Computer science ; Artificial intelligence ; Mathematics ; Geometry ; Electron Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Lei Xu | Yujiao Xie | Jie Lin | Aiguo Wu | Tianan Jiang
Source:  (preprint, 2023-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements in SERS-Based Biological Detection and Its Application and Perspectives in Pancreatic Cancer Surface-enhanced Raman scattering (SERS) has become an essential bio-detection technique. Due to its high sensitivity, good signal specificity, and resistance to photobleaching, SERS has been widely used in biomedical research fields such as molecular imaging, tumor diagnosis, and drug monitoring. This review focuses on the progress of SERS in biomedical applications. We first introduce the basic principle of SERS and the progress of substrate research. Then we summarize the latest research progress of SERS in drug monitoring, cell and exosome detection, tumor imaging, and detection platforms combining microfluidic and lateral flow technologies. Subsequently, the applied research of SERS in early diagnosis of pancreatic cancer and drug efficacy monitoring is described. Finally, the future development direction and possible challenges of SERS in tumor diagnosis and treatment are proposed. Nanotechnology ; Photobleaching ; Microfluidics ; Pancreatic cancer ; Raman scattering ; Computer science ; Materials science ; Cancer ; Medicine ; Raman spectroscopy ; Fluorescence ; Physics ; Internal medicine ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Extracellular vesicles in disease
Authors: Micke Kuwahara | Jun Fujima | Keisuke Takahashi | Lauren Takahashi
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Improving scientific image processing accessibility through development of graphical user interfaces for scikit-image Catalyst informatics has garnered a lot of attention within the research community as it is a very powerful method for discovering and understanding catalysts from the data perspective. Perspective (graphical) ; Informatics ; Computer science ; Image processing ; Human–computer interaction ; Graphical user interface ; Data science ; Image (mathematics) ; Engineering ; Artificial intelligence Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Catalysis and Oxidation Reactions