jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mikhail Suyetin
Source: ChemRxiv (preprint, 2023-06-21)
Field: Chemistry — Inorganic Chemistry

Exploring Propane/Propylene Separation through Molecular Dynamics Simulations of Flexible Metal-Organic Framework Models Propylene is a vital component for the petrochemical industry. The overwhelming majority of the studies is focused on propylene/propane separation. Nevertheless, as the ultimate goal is to obtain propylene as the end product, propylene-selective MOFs necessitate a demanding and complex desorption process, which consumes a substantial amount of energy and involves intricate operations. Consequently, having an adsorbent that preferentially adsorbs propane instead would be more advantageous. This approach would enable the production of high-purity propylene in a single step and lead to significant reductions in energy consumption and the quantity of adsorbent required. The Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 database is utilized for the computational screening of flexible MOFs using molecular dynamics simulations to identify materials with strong potential for separating propane/propylene via self-diffusion. This screening process is the first to fully consider the impact of framework flexibility on the discovery of guest self-diffusion coefficients. The study underscores the significance of accounting for framework flexibility in investigations of gas molecules transport in MOFs, showcases the potential of using data-driven approaches to identify high-performance materials, and suggests methods for enhancing the predictive capabilities of screening workflows. The top 5 MOFs from the CoRE MOF database for propane and propylene separation at a temperature of 298 K are identified. It is observed that the presence of carbonyl groups greatly enhances the separation properties between propane and propylene. Subsequently, a machine learning approach is utilized to develop a model and uncover important features. The machine learning model's predicted values for self-diffusion are reasonably consistent with the data obtained from molecular dynamics simulations. Propane ; Metal-organic framework ; Flexibility (engineering) ; Materials science ; Petrochemical ; Molecular dynamics ; Process engineering ; Adsorption ; Biochemical engineering ; Computer science ; Chemistry ; Organic chemistry ; Computational chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Lanthanide and Transition Metal Complexes
Authors: Sourav Deb | Anik Sahoo | Sujoy Baitalik
Source: European Journal of Inorganic Chemistry (article, 2023-03-22)
Field: Materials Science — Polymers and Plastics

Harnessing Deep Neural Networks to Analyze Multi‐Channel Anion Sensing Characteristics of a Ru(II)‐Pyrazolyl‐Bis(Benzimidazole) Complex Abstract In this work, the anion‐responsive conduct of a Ru(II)‐bipyridine complex incorporating pyrazolyl‐bis (benzimidazole) ligand is thoroughly investigated in acetonitrile and water via absorption and emission spectroscopy as well as by square‐wave voltammetry (SWV). Substantial alteration of the photo‐redox behavior of the complex is observed in the presence of the selected anions. The free form of the complex exhibits emission indicating the “on‐state”, while inclusion of anions leads to quenching of emission and represents the “off‐state”. The restoration of the initial state of the complex is feasible in the presence of acid and the process is reversible and can be recycled. In essence, the complex functions as anion‐ and acid‐responsive molecular switches. Additionally, we applied herein neural network based deep learning methodologies, viz. Artificial Neural Networks (ANNs) and Adaptive Neuro‐Fuzzy Inference System (ANFIS)} for thorough analysis and fully understand the multi‐channel anion sensing behavior of the complex. Chemistry ; Benzimidazole ; Bipyridine ; Ion ; Artificial neural network ; Acetonitrile ; Ligand (biochemistry) ; Quenching (fluorescence) ; Combinatorial chemistry ; Fluorescence ; Organic chemistry ; Artificial intelligence ; Computer science Conducting polymers and applications ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Weiliang Luo | Gengmo Zhou | Zhengdan Zhu | Guolin Ke | Zhewei Wei | Zhifeng Gao | Hang Zheng
Source: ChemRxiv (preprint, 2023-08-28)
Field: Materials Science — Materials Chemistry

Uni-pKa: An Accurate and Physically Consistent pKaPrediction through Protonation Ensemble Modeling Predicting pKa values of small molecules has key applications in drug discovery and molecular simulation. However, current methods face challenges in rigorously interpreting experimental data and ensuring thermodynamic consistency between successive pKa values. This study puts forward a protonation ensemble framework to address these limitations by modeling the full space of possible protonation microstates. Within this framework, we derive rigorous definitions connecting experimental macro-pKas to underlying micro-pKa equilibria. Under this new framework, we develop Uni-pKa, an accurate and reliable pKa predictor. Uni-pKa first pretrains on over 1 million predicted pKas from ChemBL to learn expressive molecular representations. It is then finetuned on experimental datasets that enforce consistency with the protonation ensemble definitions. The high-quality experimental pKa datasets are fitted to this framework by recovering underlying microstates from macro-pKas. Modeling the complete ensemble enables rigorous interpretation of macro-pKa data, and inherently preserves thermodynamic consistency, improving the prediction accuracy of Uni-pKa. Experiments demonstrate that Uni-pKa achieves state-of-the-art performance, outperforming previous methods. This novel protonation ensemble approach significantly advances machine learning for pKa prediction and molecular property modeling. Uni-pKa provides a good example of how to combine chemical knowledge and machine learning methods. Users can utilize Uni-pKa for predicting and ranking the protonation states of molecules under various pH conditions via https://app.bohrium.dp.tech/uni-pka. Protonation ; Computer science ; Acid dissociation constant ; Chemical space ; Consistency (knowledge bases) ; Chemistry ; Ensemble learning ; Machine learning ; Artificial intelligence ; Data mining ; Computational chemistry ; Drug discovery ; Aqueous solution ; Ion ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Elham Kiyani | Mahdi Kooshkbaghi | Khemraj Shukla | Rahul Koneru | Zhen Li | Luis Bravo | Anindya Ghoshal | George Em Karniadakis | Mikko Karttunen
Source: arXiv (Cornell University) (preprint, 2023-07-18)
Field: Materials Science — Materials Chemistry

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics (mDPD) simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a coarse parametric ordinary differential equation (ODE) that captures the spreading radius behavior of the CMAS droplets. The ODE parameters are then identified based on the Physics-Informed Neural Network (PINN) framework. Subsequently, the closed form dependency of parameter values found by PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modeling and state-of-the-art machine learning techniques. Ode ; Dissipative particle dynamics ; Wetting ; Parametric statistics ; Surface tension ; Mechanics ; RADIUS ; Dissipative system ; Particle (ecology) ; Ordinary differential equation ; Statistical physics ; Materials science ; Contact angle ; Computer science ; Thermodynamics ; Physics ; Applied mathematics ; Mathematics ; Differential equation ; Mathematical analysis ; Geology ; Composite material Block Copolymer Self-Assembly ; Material Dynamics and Properties ; Lattice Boltzmann Simulation Studies
Authors: Tiago F. T. Cerqueira | Antonio Sanna | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2023-07-20)
Field: Materials Science — Materials Chemistry

Sampling the Whole Materials Space for Conventional Superconducting Materials We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, we evaluate the transition temperature (Tc ) of approximately 200000 metallic compounds, all of which on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, we identify 545 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2 , which we predict to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has been previously synthesized in this phase, further heightening its potential for practical applications. Superconductivity ; Intermetallic ; Materials science ; Phonon ; Eigenvalues and eigenvectors ; Statistical physics ; Condensed matter physics ; Computer science ; Physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Chemical Physics Studies
Authors: Dexter Antonio | Hannah J. O’Toole | Randy P. Carney | Ambarish Kulkarni | Ahmet Palazoğlu
Source: arXiv (Cornell University) (preprint, 2023-06-29)
Field: Chemistry — Analytical Chemistry

Assessing the Performance of 1D-Convolution Neural Networks to Predict Concentration of Mixture Components from Raman Spectra An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to analytically determine real-time concentrations using non-destructive light irradiation in a label-free manner. Chemometric algorithms are used to interpret Raman spectra produced from complex mixtures of bioreactor contents as a reaction evolves. Finding the optimal algorithm for a specific bioreactor environment is challenging due to the lack of freely available Raman mixture datasets. The RaMix Python package addresses this challenge by enabling the generation of synthetic Raman mixture datasets with controllable noise levels to assess the utility of different chemometric algorithm types for real-time monitoring applications. To demonstrate the capabilities of this package and compare the performance of different chemometric algorithms, 48 datasets of simulated spectra were generated using the RaMix Python package. The four tested algorithms include partial least squares regression (PLS), a simple neural network, a simple convolutional neural network (simple CNN), and a 1D convolutional neural network with a ResNet architecture (ResNet). The performance of the PLS and simple CNN model was found to be comparable, with the PLS algorithm slightly outperforming the other models on 83\% of the data sets. The simple CNN model outperforms the other models on large, high noise datasets, demonstrating the superior capability of convolutional neural networks compared to PLS in analyzing noisy spectra. These results demonstrate the promise of CNNs to automatically extract concentration information from unprocessed, noisy spectra, allowing for better process control of industrial drug production. Code for this project is available at github.com/DexterAntonio/RaMix. Python (programming language) ; Convolutional neural network ; Computer science ; Raman spectroscopy ; Artificial neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Partial least squares regression ; Convolution (computer science) ; Algorithm ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Dmitrij Rappoport
Source: ChemRxiv (preprint, 2023-05-22)
Field: Materials Science — Materials Chemistry

Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models Selection bias is inevitable in manually curated computational reaction databases but can have a significant impact on generalizability of quantum chemical methods and machine learning models derived from these data sets. Here, we propose quasireaction subgraphs as a discrete, graph-based representation of reaction mechanisms that has a well-defined associated probability space and admits a similarity function using graph kernels. Quasireaction subgraphs are thus well suited for constructing representative or diverse data sets of reactions. Quasireaction subgraphs are defined as subgraphs of a network of formal bond breaks and bond formations (transition network) composed of all shortest paths between reactant and product nodes. However, due to their purely geometric construction, they do not guarantee that the corresponding reaction mechanisms are thermodynamically and kinetically feasible. As a result, a binary classification of feasible (reaction subgraphs) and infeasible (non-reactive subgraphs) must be applied after sampling. In this paper, we describe the construction and properties of quasireaction subgraphs and characterize the statistics of quasireaction subgraphs from CHO transition networks with up to six nonhydrogen atoms. We explore their clustering using Weisfeiler--Lehman graph kernels. Computer science ; Cluster analysis ; Graph ; Theoretical computer science ; Binary number ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Complex Network Analysis Techniques
Authors: Xiaofeng Liu | Yan Zhiyong | Fangling Leng | Yubin Bao | Yijie Huang
Source: Frontiers in Energy Research (article, 2023-01-05)
Field: Materials Science — Materials Chemistry

Machine learning predictive model for electronic slurries for smart grids Electricity is a fundamental energy that is essential to the growth of industrialization and human livelihood. Electric power resources can be used to meet living and production needs more steadily, effectively, and intelligently with the help of an intelligent power grid. The accuracy and stability of component requirements have increased due to the rapid growth of intelligent power networks. One of the fundamental components for component production is electronic slurry, so optimizing electronic paste’s properties is crucial for smart grids. In the field of materials science, the process of discovering new materials is drawn out and chance-based. The traditional computation process takes a very long time. Scientists have recently applied machine learning techniques to anticipate material properties and hasten the creation of novel materials. These techniques have proven to offer amazing benefits in a variety of fields. Machine learning techniques, such as the cross-validated nuclear ridge regression algorithm to predict double perovskite structure materials and the machine learning algorithm to predict the band gap value of chalcopyrite structure materials, have demonstrated excellent performance in predicting the band gap value of some specific material structures. The performance value of other structural materials cannot be predicted directly by this targeted prediction model; it can only forecast the band gap value of a single structural material. This study presents two model techniques for dividing data sets into element kinds using regression models and dividing data sets into clusters using regression models, both of which are based on the fundamental theory of physical properties, band gap theory. This plan is more efficient than the classification-regression model. The MAE dropped by 0.0455, the MSE dropped by 0.0425, and the R2 rose by 0.022. The effectiveness of machine learning in forecasting the material band gap value has increased, and the model trained by this design strategy to predict the material band gap value is more reliable than previously. Computer science ; Component (thermodynamics) ; Machine learning ; Artificial intelligence ; Process (computing) ; Industrial engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Semiconductor Detectors and Materials
Authors: Gihan Panapitiya | Emily Saldanha
Source: ChemRxiv (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how well they will perform on previously unseen types of materials. Because machine learning model predictions depend on the quality of the available training data, different domains of the material feature space are predicted with different accuracy levels by such models. The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors. In this work, we propose a method to find domains of applicability using a large feature space and also introduce analysis techniques to gain more insight into the detected domains and subdomains. Computer science ; Outlier ; Property (philosophy) ; Identification (biology) ; Machine learning ; Feature (linguistics) ; Artificial intelligence ; Domain (mathematical analysis) ; Data mining ; Feature vector ; Space (punctuation) ; Predictive modelling ; Anomaly detection ; Mathematics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: R. Gowri | P. Vasanthi | R. Swathi Rekha | B. Vijaya | S. Karthikeyan | V S Harinishrii
Source:  (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Machine Learning - Based Prediction of Compressive Strength Analysis This study’s main goal is to assess concrete’s compressive strength using the soft computing technique in a way that uses less time, money, and material resources. Using machine learning (ML) in the creation of concrete mixes is expanding swiftly because of its revolutionary potential and ability to address difficult challenges. To forecast the water absorption and compressive strength of concrete at 28 days of age, a comparative study was carried out utilizing the “JUPITER” software environment and the ML method Artificial Neural Network (ANN). The dataset was produced in a controlled laboratory setting. It is demonstrated that the suggested ANN model predicts the compressive strength of concrete ratio with a high degree of accuracy using the coefficient of determination (R2) and root-mean-square error (RMSE) for validation. Computer science ; Compressive strength ; Artificial intelligence ; Machine learning ; Materials science ; Composite material High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Fatigue and fracture mechanics
Authors: William M. Baldwin | Xia Liang | Johan Klarbring | Miloš Dubajić | David Dell’Angelo | Christopher Sutton | Claudia Caddeo | Samuel D. Stranks | Alessandro Mattoni | Aron Walsh | Gábor Csányi
Source: arXiv (Cornell University) (preprint, 2023-04-10)
Field: Materials Science — Materials Chemistry

Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains Metal halide perovskites are multifunctional semiconductors with tunable structures and properties. They are highly dynamic crystals with complex octahedral tilting patterns and strongly anharmonic atomic behaviour. In the higher temperature, higher symmetry phases of these materials, several complex structural features have been observed. The local structure can differ greatly from the average structure and there is evidence that dynamic two-dimensional structures of correlated octahedral motion form. An understanding of the underlying complex atomistic dynamics is, however, still lacking. In this work, the local structure of the inorganic perovskite CsPbI$_3$ is investigated using a new machine learning force field based on the atomic cluster expansion framework. Through analysis of the temporal and spatial correlation observed during large-scale simulations, we reveal that the low frequency motion of octahedral tilts implies a double-well effective potential landscape, even well into the cubic phase. Moreover, dynamic local regions of lower symmetry are present within both higher symmetry phases. These regions are planar and we report the length and timescales of the motion. Finally, we investigate and visualise the spatial arrangement of these features and their interactions, providing a comprehensive picture of local structure in the higher symmetry phases. Anharmonicity ; Local symmetry ; Perovskite (structure) ; Cluster (spacecraft) ; Symmetry (geometry) ; Octahedron ; Planar ; Chemical physics ; Phase (matter) ; Atomic units ; Condensed matter physics ; Materials science ; Physics ; Molecular physics ; Chemistry ; Crystal structure ; Crystallography ; Geometry ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices