jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: F. Takano | Masaki HIRATSUKA | Takeshi Aoyagi | Kazuaki Z. Takahashi
Source: The Journal of Chemical Physics (article, 2022-10-17)
Field: Materials Science — Polymers and Plastics

Local order parameter that distinguishes crystalline and amorphous portions in polymer crystal lamellae The degradation of microplastics in relation to marine pollution has been receiving increasing attention. Because the spherulites that comprise microplastics have a highly ordered lamellar structure, their decomposition is thought to involve a lamellar structure collapse process. However, even in the simplest case of an order-disorder transition between lamellae and melt upon heating, the microscopic details of the transition have yet to be elucidated. In particular, it is unclear whether nucleation occurs at defects in the crystalline portion or at the interface between the crystalline and amorphous portions. To observe the transition in molecular simulations, an approach that distinguishes between the crystalline and amorphous structures that make up the lamella is needed. Local order parameters (LOPs) are an attempt to define the degree of order on a particle-by-particle basis and have demonstrated the ability to precisely render complex order structure transitions during phase transitions. In this study, 274 LOPs were considered to classify the crystalline and amorphous structures of polymers. Supervised machine learning was used to automatically and systematically search for the parameters. The identified optimal LOP does not require macroscopic information such as the overall orientation direction of the lamella layers but can precisely distinguish the crystalline and amorphous portions of the lamella layers using only a small amount of neighboring particle information. Amorphous solid ; Lamellar structure ; Lamella (surface anatomy) ; Nucleation ; Materials science ; Chemical physics ; Phase transition ; Polymer ; Particle (ecology) ; Crystal (programming language) ; Quenching (fluorescence) ; Crystallography ; Condensed matter physics ; Thermodynamics ; Chemistry ; Composite material ; Physics ; Computer science ; Optics ; Geology Polymer crystallization and properties ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Xiao-Tong Pan | Xuanye Yang | Tian-Qi Mao | Kang Liu | Zaozao Chen | Lina Ji | Dechen Jiang | Kang Wang | Zhongze Gu | Xing‐Hua Xia
Source: Biosensors (article, 2022-10-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Super-Long SERS Active Single Silver Nanowires for Molecular Imaging in 2D and 3D Cell Culture Models Establishing a systematic molecular information analysis strategy for cell culture models is of great significance for drug development and tissue engineering technologies. Here, we fabricated single silver nanowires with high surface-enhanced Raman scattering activity to extract SERS spectra in situ from two-dimensional (2D) and three-dimensional (3D) cell culture models. The silver nanowires were super long, flexible and thin enough to penetrate through multiple cells. A single silver nanowire was used in combination with a four-dimensional microcontroller as a cell endoscope for spectrally analyzing the components in cell culture models. Then, we adopted a machine learning algorithm to analyze the obtained spectra. Our results show that the abundance of proteins differs significantly between the 2D and 3D models, and that nucleic acid-rich and protein-rich regions can be distinguished with satisfactory accuracy. Nanowire ; Raman scattering ; Nanotechnology ; Materials science ; Cell culture ; In situ ; Nucleic acid ; Raman spectroscopy ; Biophysics ; Chemistry ; Biology ; Optics ; Biochemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Jonathan Y. C. Ting | Sichao Li | Amanda S. Barnard
Source: Advanced Theory and Simulations (article, 2022-07-24)
Field: Materials Science — Materials Chemistry

Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference Abstract Machine learning can extract complex structure/property relationships but is often insufficient to explain how to control or tune the properties of materials, particularly when they are multi‐functional. This study demonstrates the value of combining multi‐target regression and multi‐target causal graphs to address the need to simultaneously control multiple properties of nanomaterials, and the need to translate these relationships into actionable insights. Using nanodiamonds as an exemplar, recursive feature elimination is first used to identify nine structural features that allow simultaneous prediction of their electron charge transfer properties and thermochemical stability to high accuracy by an interpretable random forest regressor. A multi‐target Bayesian network with domain knowledge incorporated via interactive learning using a hill‐climbing algorithm then determines how these important structural features of nanodiamonds relate to their functional properties, proposing causal paths that can be used to inform experimental design. Computer science ; Machine learning ; Inference ; Random forest ; Stability (learning theory) ; Artificial intelligence ; Bayesian network ; Feature (linguistics) ; Causal inference ; Bayesian inference ; Bayesian probability ; Property (philosophy) ; Domain (mathematical analysis) ; Mathematics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Computational Drug Discovery Methods
Authors: Shahin Ahmadi | Soumya Ghosh | Alhadji Malloum | Charné Bornman | Christian Osagie | Leili Mohammadi | Chinenye Adaobi Igwegbe
Source: Adsorption Science & Technology (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Modeling the Liquid-Phase Adsorption of Cephalexin onto Coated Iron Nanoparticles Using Response Surface and Molecular Modeling In order to assess the interactions between process factors, the experiments involving the liquid-phase adsorption of cephalexin (CEX) onto silicon-coated iron nanoparticles (Fe 3 O 4 @SIO 2 ) were designed using the Box-Behnken Design-Response surface methodology (BBD-RSM). Optimal circumstances were used to investigate the synergistic influence on the process’s efficiency. In addition, the data was used to test and fit an artificial neural network (ANN) model. Molecular-level DFT calculations on the CEX molecule were carried out. The PW6B95D3/Def2-TZVP level of theory was used to build DFT-based descriptors for the CEX molecule. At 25°C, pH 5.83, 37.67 min, a dosage of 0.8 g Fe 3 O 4 @SIO 2 and 118.01 mg/L CEX, the removal efficiency achieved a maximum of 99.01 percent. For example, we found that OH --- O, NH --- O, CH --- O hydrogen bonds, NH --- π, OH --- π, CH --- π interactions as well as dipole-dipole interactions between CEX and the nanoparticles could all be used to connect the CEX and the nanoparticles. There is a strong correlation between the output and target values acquired by BBD-RSM and ANN fits. Fe 3 O 4 @SIO 2 proved to be an excellent tool for eliminating CEX. Adsorption ; Chemistry ; Response surface methodology ; Nanoparticle ; Molecule ; Hydrogen bond ; Density functional theory ; Phase (matter) ; Dipole ; Computational chemistry ; Nuclear chemistry ; Physical chemistry ; Nanotechnology ; Chromatography ; Organic chemistry ; Materials science Nanomaterials for catalytic reactions ; Analytical chemistry methods development ; Iron oxide chemistry and applications
Authors: Shuyu Qin | Yichen Guo | Alibek T. Kaliyev | Joshua Agar
Source: Advanced Materials (article, 2022-07-29)
Field: Materials Science — Materials Chemistry

Why it is Unfortunate that Linear Machine Learning “Works” so well in Electromechanical Switching of Ferroelectric Thin Films Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called "better", "faster", and "less-biased" ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. To improve the interpretability of the latent space, a variational Kullback-Leibler-divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies. Materials science ; Ferroelectricity ; Thin film ; Nanotechnology ; Optoelectronics ; Engineering physics ; Dielectric ; Engineering Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Raquel de Melo Barbosa | Cleanne C. Lima | Fabio F. de Oliveira | Gabriel Bezerra Motta Câmara | César Viseras | Tulio Flavio Accioly de Lima e Moura | Eliana B. Souto | Patrícia Severino | Fernanda Nervo Raffin | Marcelo A. C. Fernandes
Source: Nanomanufacturing (article, 2022-07-18)
Field: Chemistry — Spectroscopy

New Machine Learning Approach for the Optimization of Nano-Hybrid Formulations Nano-hybrid systems are products of interactions between organic and inorganic materials designed and planned to develop drug delivery platforms that can be self-assembled. Poloxamine, commercially available as Tetronic®, is formed by blocks of copolymers consisting of poly (ethylene oxide) (PEO) and poly (propylene oxide) (PPO) units arranged in a four-armed star shape. Structurally, Tetronics are similar to Pluronics®, with an additional feature as they are also pH-dependent due to their central ethylenediamine unit. Laponite is a synthetic clay arranged in the form of discs with a diameter of approximately 25 nm and a thickness of 1 nm. Both compounds are biocompatible and considered as candidates for the formation of carrier systems. The objective is to explore associations between a Tetronic (T1304) and LAP (Laponite) at concentrations of 1–20% (w/w) and 0–3% (w/w), respectively. Response surface methodology (RMS) and two types of machine learning (multilayer perceptron (MLP) and support vector machine (SVM)) were used to evaluate the physical behavior of the systems and the β-Lapachone (β-Lap) solubility in the systems. β-Lap (model drug with low solubility in water) has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. The results show an adequate machine learning approach to predict the physical behavior of nanocarrier systems with and without the presence of LAP. Additionally, the analysis performed with SVM showed better results (R2 &gt; 0.97) in terms of data adjustment in the evaluation of β-Lap solubility. Furthermore, this work presents a new methodology for classifying phase behavior using ML. The new methodology allows the creation of a phase behavior surface for different concentrations of T1304 and LAP at different pHs and temperatures. The machine learning strategies used were excellent in assisting in the optimized development of new nano-hybrid platforms. Solubility ; Support vector machine ; Materials science ; Nanocarriers ; Ethylene oxide ; Oxide ; Nano- ; Chemical engineering ; Poloxamer ; Drug delivery ; Nanotechnology ; Computer science ; Copolymer ; Chemistry ; Artificial intelligence ; Composite material ; Organic chemistry ; Polymer ; Engineering Analytical Chemistry and Chromatography ; Nanoparticle-Based Drug Delivery ; Microfluidic and Capillary Electrophoresis Applications
Authors: Marvin Poul | Liam Huber | Erik Bitzek | Jörg Neugebauer
Source: arXiv (Cornell University) (preprint, 2022-07-08)
Field: Materials Science — Materials Chemistry

Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential. Magnesium ; Construct (python library) ; Hexagonal crystal system ; Crystal structure ; Interatomic potential ; Set (abstract data type) ; Materials science ; Chemical physics ; Process (computing) ; Space (punctuation) ; Computer science ; Statistical physics ; Biological system ; Crystallography ; Chemistry ; Molecular dynamics ; Physics ; Computational chemistry ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Dragana Kukić | Marina Šćiban | Mirjana Brdar | Vesna Vasić | Aleksandar Takači | Mirjana Antov | Jelena Prodanović
Source: International Journal of Environmental Science and Technology (article, 2022-04-07)
Field: Materials Science — Biomaterials

Sugar beet lignocellulose waste as biosorbents: surface functionality, equilibrium studies and artificial neural network modeling Adsorption ; Chemistry ; Titration ; Fourier transform infrared spectroscopy ; Pectin ; Sugar ; Cellulose ; Hydrolysis ; Wastewater ; Biosorption ; Pulp and paper industry ; Sucrose ; Chemical engineering ; Chromatography ; Organic chemistry ; Waste management ; Biochemistry Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Food composition and properties
Authors: Shuizhou Chen | He Cao | Qiubao Ouyang | Xing Wu | Quan Qian
Source: Materials & Design (article, 2022-08-29)
Field: Materials Science — Materials Chemistry

ALDS: An active learning method for multi-source materials data screening and materials design High-quality internal experimental materials data are often “small data.” Using external datasets, e.g., data from the literature and other groups, to expand the “small data” to relatively “big data” is particularly important for improving the prediction accuracy of machine learning models. However, the most critical issue is how to use a small amount of internal reliable data to filter external data extracted from multiple sources and obtain optimal datasets with a distribution similar to that of internal data. This is called the multi-source materials data problem. This question was addressed by designing an active learning-based data screening (ALDS) model that is suitable for small material samples. This study used negative expansion materials as subjects. The results show that ALDS can be used to screen the external multi-source dataset and far exceeds traditional outlier filtering methods. The average mean absolute percentage error (MAPE) of the predictive key target property of the negative thermal expansion coefficient (NTEC) was reduced from 4.301 to 0.056. Furthermore, reverse design experiments were conducted on anti-perovskite manganese nitride (AMN), a type of negative thermal expansion material, to prove that the ALDS model can guide the reverse design on a small AMN dataset using the MAPE between ALDS prediction and the ground truth of samples’ three property indicators. This achieves high confidence levels with values of 0.203,0.126, and 0.115. This verifies that the ALDS proposed method improves the effect of ”materials small internal data” in guiding material design and property prediction. Outlier ; Property (philosophy) ; Computer science ; Data mining ; Materials science ; Algorithm ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Thermal properties of materials
Authors: K. B. Tan | Ziwen Zhao | Zhi-jiang Xu | W. C. Zuo | Jianxun Zhao | Bo Chen
Source: IEEE Transactions on Microwave Theory and Techniques (article, 2022-09-20)
Field: Materials Science — Materials Chemistry

Artificial Intelligence-Based Algorithm for Energy Exposure Analysis in Composite Material For massive dosimetry analysis of finite composite materials (CMs), an effective approach is proposed based on the artificial intelligence (AI) computational algorithm. Machine learning (ML) method is used to analyze systematic energy characteristics of finite CM for dosimetry exposure. To prove the validity of our algorithm, we analyzed a finite CM in a specific scenario and calculated its exposure dosimetry. The dataset for algorithm training is obtained through numerical analysis. In order to verify the efficiency of the proposed method, the accuracy of this algorithm and its time consumption are calculated. Five ML methods are subsequently used to compare the average absorption of the energy resonance exposure in the finite CM. The time consumption and error distribution in test sets prove the high efficiency of AI computational algorithm to analyze the energy exposure of finite CM. The proposed AI algorithm is useful for the massive numerical analysis in the research of electromagnetic (EM) exposure. Algorithm ; Dosimetry ; Computer science ; Finite element method ; Energy consumption ; Energy (signal processing) ; Mathematics ; Engineering ; Statistics ; Electrical engineering ; Nuclear medicine ; Structural engineering Graphite, nuclear technology, radiation studies ; Electromagnetic Compatibility and Measurements ; Electromagnetic wave absorption materials
Authors: Danping Luo | Yingna Li | Shouguo Tang | Ailian Liu | Liping Zhang
Source: Processes (article, 2022-12-19)
Field: Chemistry — Analytical Chemistry

The Tobacco Leaf Redrying Process Parameter Optimization Based on IPSO Hybrid Adaptive Penalty Function In the tobacco redrying process, process parameter settings are greatly influenced by ambient temperature and humidity, and the moisture content of the tobacco leaf. In the face of complex and variable tobacco leaf characteristics, it is difficult to accurately adapt the process parameters to fluctuations in the incoming material characteristics by manual experience alone. Therefore, an improved optimization method combining an improved particle swarm optimization algorithm (IPSO) and an adaptive penalty function is proposed, which can adaptively recommend the best combination of process parameters according to the dynamic incoming characteristics of the tobacco leaf, to reduce the deviation in the outlet moisture and temperature of the roaster under different processing standards of the tobacco leaf. Firstly, the Radial Basis Function (RBF) Neural Network is used to fit the relationship between process parameters and roaster exit moisture content and temperature. Then, taking the standard tobacco leaf redrying export quality as the optimization goal, the optimization algorithm is used to search for the optimal solution. From the high-dimensional nature of the process operating conditions, the difficulty of this study lies in searching for the optimal solution under complex nonlinear constraints of multiple processes. To improve the convergence speed and accuracy of the searching algorithm, the position update method of the particle swarm optimization algorithm is improved, and the adaptive penalty function is combined to search for the optimal global solution to the optimization problem. Redrying experiments are conducted using the method proposed in this paper. Compared with the manual regulation of outlet moisture and temperature, the fluctuation range values are reduced by 7.5% and 11.8%, respectively, which has good application prospects and promotion value. Particle swarm optimization ; Process (computing) ; Penalty method ; Mathematical optimization ; Convergence (economics) ; Local optimum ; Function (biology) ; Computer science ; Control theory (sociology) ; Mathematics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research ; Greenhouse Technology and Climate Control
Authors: Jia Fu | Zhitao Wan | Zhangzhang Yang | Li Liu | Qunchao Fan | Feng Xie | Yi Zhang | Jie Ma
Source: International Journal of Quantum Chemistry (article, 2022-07-04)
Field: Materials Science — Materials Chemistry

Combining ab initio and machine learning method to improve the prediction of diatomic vibrational energies Abstract Through the comprehensive analysis of ab initio and experimental results of a large number of diatomic systems, the systematic deviation of ab initio method in vibrational energies prediction caused by physical/mathematical simplification is located. A joint ab initio and machine learning method based on information across molecules is proposed to deal with the problem. Starting from an ab initio model, and then systematically modifying it through machine learning, the vibrational energies prediction of many diatomic systems (SiC, HBr, NO, PC, N 2 , SiO, O 2 , ClF, etc.) have been improved, and significantly surpassed the more complex ab initio model. In addition to the improvement of accuracy, the new method also greatly reduces the computational expense, and is applicable for the systems without experimental data. Diatomic molecule ; Ab initio ; Ab initio quantum chemistry methods ; Chemistry ; Computational chemistry ; Atomic physics ; Molecular physics ; Molecule ; Statistical physics ; Physics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Advanced Chemical Sensor Technologies