jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Max Cui | Adelina Chau | Michelle Pan | Vaibhav Vaiyakarnam | Larry McMahan
Source:  (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Molecular Geometry Generation Processes Through Hybrid Quantum-Classical Generative Adversarial Networks and Python-Based Self-Consistent Field Molecular Calculations Current compound synthesis and reaction pathway determination are implemented with computational chemistry methods, but methodological improvements are needed to increase precision, accuracy, and efficiency. We are implementing a molecular geometry generation process using Hybrid Quantum-Classical Graph Generative Adversarial Networks (QNetGAN), STO-nG basis set, Hartree-Fock (HF) and Post-Hartree-Fock (PHF) approximations of Schrödinger's equation for molecular energy calculations using PySCF. QNetGAN generates molecular graphs, ensuring atom connectedness, druglikeness, and Octet Rule satisfaction, at a fast runtime and great efficiency. Although a work in progress, QNetGAN has demonstrated its capabilities with an impressive training time of 10.164 minutes and 47% success rate, generating 141/300 structurally valid molecular geometries that fulfill Lipinski's Rule of Five. Meanwhile, we have developed a Python-based molecular energy and properties program with proven accuracy. By testing pre-existing organic molecules using HF (and later, PHF) and comparing our experimental results with accepted values from the CCCBDB government database, we are verifying the reliability of our program and preparing to determine the stability and feasibility of future QNetGAN-generated molecules. Currently, the QNetGAN and the HF/PHF molecular geometry and molecular energy calculations are separate; future work involves improving the accuracy of QNetGAN before integrating these components together and the current classical GAN/Density Functional Theory (DFT) systems. The ultimate goal is to increase efficiency and lower costs in R&D processes, accelerating the development of new medicines and treatments for currently incurable diseases. Computer science ; Python (programming language) ; Density functional theory ; Generative grammar ; Coupled cluster ; Quantum ; Topology (electrical circuits) ; Computational science ; Theoretical computer science ; Statistical physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Physics ; Molecule ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yifan Liu | Runze Liu | Jinyu Duan | Li Wang | Xiangwen Zhang | Guozhu Li
Source: Chemical Engineering Science (article, 2023-03-24)
Field: Materials Science — Materials Chemistry

Deep generative fuel design in low data regimes via multi-objective imitation Generative grammar ; Autoencoder ; Chemical space ; Computer science ; Stacking ; Convergence (economics) ; Generative model ; Property (philosophy) ; Domain (mathematical analysis) ; Space (punctuation) ; Artificial intelligence ; Deep learning ; Biochemical engineering ; Chemistry ; Engineering ; Mathematics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nuclear Materials and Properties
Authors: Michael A. Kazda | Michael D. Monkowski | George Antony
Source:  (article, 2023-09-10)
Field: Materials Science — Materials Chemistry

APEX: Recommending Design Flow Parameters Using a Variational Autoencoder Modern physical synthesis flows employ thousands of parameters. Choosing the optimal set is an important problem. We introduce an offline ML based system that suggests parameter values to improve congestion, timing, and power. We employ a variational autoencoder to encode a high dimensional space into a low dimensional latent space from physical synthesis jobs and generate parameters and values. This compressed information requires minutes to predict instead of multiple days and large machine resources required for existing auto-tuner methods. Our results show this method is over 10 times faster than the present state of the art with similar quality. Autoencoder ; Computer science ; Set (abstract data type) ; Representation (politics) ; Flow (mathematics) ; Space (punctuation) ; Tuner ; ENCODE ; Power (physics) ; Mathematical optimization ; Discriminative model ; Algorithm ; Artificial intelligence ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; VLSI and FPGA Design Techniques
Authors: Ali Can Özarslan | Fatih Çiftçi
Source: Konya Journal of Engineering Sciences (article, 2023-11-30)
Field: Materials Science — Biomaterials

A COMPARATIVE STUDY OF RELEASE KINETICS BEHAVIOR MODELS AND SHELF LIFE ASSESSMENT OF BACITRACIN ZINC-LOADED PLA COMPOSITES Mathematical modeling aims to simplify the complex process of drug release and to gain knowledge about the release mechanisms specific to a given material system. Consequently, a mathematical model focuses primarily on one or two important factors. Drug release aims to maximize the bioactivity of both naturally derived and synthetically derived macromolecules, thus increasing their clinical applicability and improving the overall quality of life. This study focused on fabricating PLA composites with different weight percentages of Bacitracin Zinc (0.5, 1.0, and 2.0) and evaluating their potential as a drug delivery system. To understand the release mechanism of Bacitracin Zinc from the PLA composites, we developed a Franz diffusion kinetic model and a mathematical model for cumulative release kinetics. The Franz diffusion model was utilized to analyze the release behavior of the PLA/Bacitracin Zinc composite structure. The results indicated a sustained release rate, following a Zero Order release kinetics pattern. Furthermore, the shelf life of the composite structure was determined to be 125 days. Python programming was employed to model the release behavior and estimate the shelf life of Bacitracin Zinc (0.5, 1.0, and 2.0) incorporated into the PLA matrix to compare different weight percentages' behavior and shelf life. Shelf life ; Kinetics ; Zinc ; Diffusion ; Bacitracin ; Controlled release ; Drug delivery ; Materials science ; Chemistry ; Chemical engineering ; Nanotechnology ; Biochemistry ; Food science ; Thermodynamics ; Metallurgy ; Physics ; Engineering biodegradable polymer synthesis and properties ; Hydrogels: synthesis, properties, applications ; Bone Tissue Engineering Materials
Authors: Yanyan Liu | Mingjun Xu | Luís M. Portela | Valeria Garbin
Source: ChemRxiv (preprint, 2023-06-30)
Field: Materials Science — Materials Chemistry

Diffusion across particle-laden interfaces in Pickering droplets Emulsions stabilized by nanoparticles, known as Pickering emulsions, exhibit remarkable stability which enables many applications, from encapsulation, to advanced materials, to chemical conversion. The layer of nanoparticles at the interface of Pickering droplets forms a semi-permeable barrier between the two liquid phases, which can affect the rate of release of encapsulates, and the interfacial transfer of reactants and products in biphasic chemical conversion. The current lack of understanding of diffusion in multi-phase systems with particle-laden interfaces limits the optimal development of these applications. To address this gap, we developed an experimental approach for in-situ, real-time quantification of concentration fields in Pickering droplets in a Hele-Shaw geometry and investigated the effect of the layer of nanoparticles on diffusion of solute across a liquid-liquid interface. The experiments did not reveal a significant hindrance on the diffusion of solute across an interface densely covered by nanoparticles. We interpret this result using an unsteady diffusion model to predict the spatio-temporal effect of particles on diffusion across a particle- laden interface. We find that the concentration field of solute is only affected in the immediate vicinity of the layer of particles, where the area available for diffusion is affected by the particles. This defines a characteristic time scale for the problem, which is the time for diffusion across the layer of particles. The far-field concentration profile evolves towards that of a bare interface. This localized effect of the particle hindrance is not measurable in our experiments, which take place over a much longer time scale. Our model also predicts that the hindrance by particles can be more pronounced depending on the particle size and physicochemical properties of the liquids and can ultimately affect performance in applications. Pickering emulsion ; Diffusion ; Nanoparticle ; Particle (ecology) ; Chemical physics ; Materials science ; Nanotechnology ; Molecular diffusion ; Chemical engineering ; Chemistry ; Thermodynamics ; Physics Pickering emulsions and particle stabilization ; Proteins in Food Systems
Authors: Haote Li | Yu Shee | Brandon Allen | Federica Maschietto | Víctor S. Batista
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Materials Chemistry

Kernel-Elastic Autoencoder for Molecular Design We introduce the Kernel-Elastic Autoencoder (KAE), a self-supervised generative model based on the transformer architecture with enhanced performance for molecular design. KAE is formulated based on two novel loss functions: modified maximum mean discrepancy and weighted reconstruction. KAE addresses the long-standing challenge of achieving valid generation and accurate reconstruction at the same time. KAE achieves remarkable diversity in molecule generation while maintaining near-perfect reconstructions on the independent testing dataset, surpassing previous molecule-generating models. KAE enables conditional generation and allows for decoding based on beam search resulting in state-of-the-art performance in constrained optimizations. Furthermore, KAE can generate molecules conditional to favorable binding affinities in docking applications as confirmed by AutoDock Vina and Glide scores, outperforming all existing candidates from the training dataset. Beyond molecular design, we anticipate KAE could be applied to solve problems by generation in a wide range of applications. Autoencoder ; Computer science ; Kernel (algebra) ; Algorithm ; Artificial intelligence ; Deep learning ; Mathematics ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Falguni S. Bhavsar | Mitesh G. Mapari | Shailesh R. Tivalekar | Erum Gul Naz | Dipak Gorakh Babar
Source: ChemistrySelect (article, 2023-11-14)
Field: Materials Science — Materials Chemistry

A Nanocomposite of ZnO and <i>N</i> , <i>P</i> ‐Co‐Doped Carbon Dots for the Photocatalytic Degradation of Various Dyes and their Kinetic Study Abstract Environmental pollution is becoming more severe by the day, and it is critical to discover an effective method to overcome and reduce pollution. Because nanomaterials are important in all fields, we employed a ZnO‐based N and P doped carbon quantum dots (NPCdots) nanocomposite to degrade multiple dyes including Methylene blue (MB), Methyl orange (MO), and Rhodamine B (RhB). ZnO‐based NPCdots nanocomposite (ZnO‐NPCdots) was synthesized using a single step solvothermal technique, and the resulting nanocomposite was analysed using XRD, HRTEM, and XPS. These analyses unequivocally demonstrate that the ZnO‐NPCdots nanocomposite was successfully formed. The prepared nanocomposite was then used for the photocatalytic degradation of the different dyes under UV light, and it shows an effective catalytic property. In addition, four different kinetic models viz., zero order, pseudo first order, parabolic diffusion model, and modified Freundlich models were used to analyse the reaction‘s kinetics and actual catalytic phenomenon behind the reaction. The modified Freundlich model was found to be effective, indicating the surface phenomenon of the catalyst. Nanocomposite ; Photocatalysis ; Materials science ; Methyl orange ; Rhodamine B ; Catalysis ; High-resolution transmission electron microscopy ; X-ray photoelectron spectroscopy ; Chemical engineering ; Kinetics ; Degradation (telecommunications) ; Nanotechnology ; Chemistry ; Organic chemistry Carbon and Quantum Dots Applications ; Nanomaterials for catalytic reactions ; Nanocluster Synthesis and Applications
Authors: Takayoshi Katase | Toshio Kamiya
Source: Journal of the Ceramic Society of Japan (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Enhancement of thermoelectric performance by breaking thermopower – conductivity trade-off relation in artificially designed oxide thin-film heterostructures We introduce new approaches to break the trade-off relationship between thermopower (S) and electronic conductivity (σ) in thermoelectric oxide materials by using artificially designed thin-film heterostructures. The output electric power generated by thermoelectric conversion is characterized by power factor (PF = S2 × σ) but PF takes a maximum at a certain carrier concentration, which is restricted by the electron-diffusion model of transport theory. Here, we employ transition metal oxides of LaTiO3 and LaNiO3 to demonstrate two approaches to break the trade-off relationship. One is to introduce compressive strain in LaTiO3 film to control a Mott insulator state to a massive electron metallic state, allowing improved S and σ, giving rise to a 102-times enhancement of PF. Second one is to confine massive electrons and enhance phonon-drag effect in ultra-thin LaNiO3 film sandwiched between wider bandgap LaAlO3 capping layer and substrate with assistance of phonon-leaking effect, leading to a 10-fold increase of S. The present approaches open up new avenues for developing high-performance thermoelectric oxide materials beyond the conventional electron-diffusion theory. Seebeck coefficient ; Materials science ; Phonon drag ; Thermoelectric effect ; Heterojunction ; Condensed matter physics ; Thin film ; Thermoelectric materials ; Oxide ; Electrical resistivity and conductivity ; Substrate (aquarium) ; Band gap ; Optoelectronics ; Nanotechnology ; Engineering physics ; Composite material ; Thermal conductivity ; Electrical engineering ; Metallurgy ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Magnetic and transport properties of perovskites and related materials ; Thermal Expansion and Ionic Conductivity
Authors: Shaohua Chen | Nuo Xu
Source: Modelling and Simulation in Materials Science and Engineering (article, 2023-05-12)
Field: Materials Science — Ceramics and Composites

Shear behavior of SiC<sub>f</sub>/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design Abstract The mechanical behavior of composite interface can be influenced by multiple factors, including the morphological roughness, the structure of coating interphase, and the temperature. Here, high-throughput molecular dynamics (MD) simulations are carried out to investigate the entangled effects of these factors on the shear stiffness <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>G</mml:mi> </mml:math> , the friction coefficient <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> , the debonding strain <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>ϵ</mml:mi> <mml:mi>d</mml:mi> </mml:msub> </mml:mrow> </mml:math> and stress <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>τ</mml:mi> <mml:mi>d</mml:mi> </mml:msub> </mml:mrow> </mml:math> , of SiC f /SiC interface. We find that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>G</mml:mi> </mml:math> is maximized by small roughness and high temperature for the optimal chemical bonding effect; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>ϵ</mml:mi> <mml:mi>d</mml:mi> </mml:msub> </mml:mrow> </mml:math> are maximized by large roughness and low temperature, taking advantage of the mechanical interlocking effect while avoiding cusp softening; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>τ</mml:mi> <mml:mi>d</mml:mi> </mml:msub> </mml:mrow> </mml:math> demonstrates two local maxima which result from the competition between chemical bonding and mechanical interlocking. Provided the MD simulation results, a variational autoencoder (VAE) model is proposed to design the microstructure of SiC f /SiC interface for desired shear properties. According to the validations, the VAE-predicted interfacial configuration demonstrates highly similar shear properties to the reference one, justifying its potential for the microstructure design of composite interface. The results of this work can be employed to facilitate the development of SiC f /SiC composite by taking advantage of the synergistic effects of multiple designable factors. Algorithm ; Materials science ; Artificial intelligence ; Machine learning ; Computer science Advanced ceramic materials synthesis ; Advanced Surface Polishing Techniques ; Metal and Thin Film Mechanics
Authors: Saad A. Aljlil
Source: Membranes (article, 2023-03-27)
Field: Materials Science — Biomaterials

A Mathematical Simulation of Copper and Nickel Ions Separation Using Prepared Nanocellulose Material Environmental risks can arise from the existence of heavy metals in wastewater and their land disposal. To address this concern, a mathematical technique is introduced in this article that enables the anticipation of breakthrough curves and the imitation of copper and nickel ion separation onto nanocellulose in a fixed-bed system. The mathematical model is based on mass balances for copper and nickel and partial differential equations for pore diffusion in a fixed bed. The study evaluates the impact of experimental parameters such as bed height and initial concentration on the shape of the breakthrough curves. At 20 °C, the maximum adsorption capacities for copper and nickel ions on nanocellulose were 5.7 mg/g and 5 mg/g, respectively. The breakthrough point decreased with increasing solution concentration at higher bed heights, while at an initial concentration of 20 mg/L, the breakthrough point increased with bed height. The fixed-bed pore diffusion model showed excellent agreement with the experimental data. The use of this mathematical approach can help alleviate the environmental hazards that arise from the presence of heavy metals in wastewater. The study highlights the potential of nanocellulose as a material for membrane technology, which can effectively address these risks. Nanocellulose ; Nickel ; Copper ; Materials science ; Diffusion ; Adsorption ; Wastewater ; Chemical engineering ; Environmental science ; Environmental engineering ; Metallurgy ; Thermodynamics ; Chemistry ; Engineering ; Physics Advanced Cellulose Research Studies ; Recycling and Waste Management Techniques ; Membrane Separation Technologies
Authors: Mathis Benyaya | Marie‐Alexandrine Bolzinger | Yves Chevalier | Claire Bordes
Source: Polymer (article, 2023-03-06)
Field: Chemistry — Physical and Theoretical Chemistry

Formulation of polymeric particles with controlled charges by alkaline tempering Comonomer ; Zeta potential ; Surface charge ; Methacrylic acid ; Particle (ecology) ; Hydroxide ; Chemistry ; Deprotonation ; Aqueous solution ; Polyelectrolyte ; Chemical engineering ; Inorganic chemistry ; Polymer chemistry ; Materials science ; Ion ; Organic chemistry ; Physical chemistry ; Polymer ; Nanotechnology ; Polymerization ; Nanoparticle Electrostatics and Colloid Interactions ; Electrowetting and Microfluidic Technologies ; Microfluidic and Capillary Electrophoresis Applications
Authors: Refilwe Mogale | Kovo G. Akpomie | Jeanet Conradie | Еrnst H.G. Langner
Source: International Journal of Environmental & Analytical Chemistry (article, 2023-02-14)
Field: Chemistry — Inorganic Chemistry

Adsorption of nortriptyline antidepressant from contaminated water by emerging aluminium-based dicarboxylate metal-organic-frameworks The increasing concentration of antidepressants in wastewater due to economic hardship and pressures correlated to the global pandemic is an environmental threat. Therefore, new and efficient methods for their removal need to be developed. Metal-organic frameworks (MOFs) are potent adsorbents for wastewater but there are limited reports on the adsorption potentials of an emerging class of dicarboxylate MOFs. Herein, we synthesised two aluminium-based dicarboxylate MOFs containing terephthalic (Al-MIL-53) and biphenyl (Al-Biphen) linkers for the adsorption of nortriptyline antidepressants from contaminated water. The MOFs were characterised by the TGA, SEM, PXRD, BET and FTIR and exhibited desirable surface properties for potent adsorption of nortriptyline. BET surface areas of 1024 m<sup>2</sup> g<sup>−1</sup> and 624 m<sup>2</sup> g<sup>−1</sup> were obtained for Al-Biphen and Al-MIL-53, respectively, which resulted in a maximum nortriptyline uptake capacity of 284.9 mg g<sup>−1</sup> and 246.3 mg g<sup>−1</sup>. Optimum adsorption of nortriptyline was obtained at pH of 7.0, adsorbate concentration of 50 mg/L, contact time of 40 min and a temperature of 300 K. The Langmuir model was well correlated with the adsorption onto Al-Biphen, while both the Langmuir and Freundlich models were more suited for Al-MIL-53. The drug adsorption kinetics was well interpreted by the pseudo-second-order, Elovich, and intraparticle diffusion models. For both materials, the adsorption processes are exothermic and spontaneous with decreasing randomness. Moreover, less than 10.9% of the initial efficiency of the MOFs was lost after four cycles of reuse, which showed that the dicarboxylate MOFs are highly efficient, fast, and reusable for nortriptyline adsorption from aqueous phase. Adsorption ; Nortriptyline ; Freundlich equation ; Langmuir ; Metal-organic framework ; Chemistry ; Langmuir adsorption model ; BET theory ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Inorganic chemistry ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: Gilbert Romeo Nkana Nkana | Lajeunesse André | Bruno Chabot | Phuong Nguyen‐Tri
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Organic Chemistry

Design of Porous Spherical Biomaterials from Carboxymethyl Chitosan for Removal of Fluoxetine in Aqueous Medium Chitosan ; Chemical engineering ; Porosity ; Aqueous solution ; Materials science ; Chemistry ; Polymer chemistry ; Biomedical engineering ; Composite material ; Organic chemistry ; Engineering Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Analytical chemistry methods development
Authors: Shaoshuang Zhang | Yuanyuan Liang | Renbo Song | Changhong Cai | Lican Sun | Beijia Feng | Yimin Wang
Source: Surfaces and Interfaces (article, 2023-08-19)
Field: Materials Science — Materials Chemistry

Microscopic mechanism of big-white-spot defect formation in hot-based galvanized strip steel: Theoretical calculation and molecular dynamics simulation Materials science ; Nucleation ; Galvanization ; Coating ; Diffusion ; Microstructure ; Molecular dynamics ; Layer (electronics) ; Composite material ; Aluminium ; Substrate (aquarium) ; Metallurgy ; Thermodynamics ; Computational chemistry Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals