jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexandra N. Filiatraut | Jaber Rezaei Mianroodi | Nima H. Siboni | Mehdi B. Zanjani
Source: Journal of Applied Physics (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Predicting micro/nanoscale colloidal interactions through local neighborhood graph neural networks Understanding interparticle interactions has been one of the most important topics of research in the field of micro/nanoscale materials. Many significant characteristics of such materials directly stem from the way their building blocks interact with each other. In this work, we investigate the efficacy of a specific category of Machine Learning (ML) methods known as interaction networks in predicting interparticle interactions within colloidal systems. We introduce and study Local Neighborhood Graph Neural Networks (LN-GNNs), defined according to the local environment of colloidal particles derived from particle trajectory data. The LN-GNN framework is trained for unique categories of particle neighborhood environments in order to predict interparticle interactions. We compare the performance of the LN-GNN to a baseline interaction network with a simpler architecture and to an Instance-Based ML algorithm, which is computationally more expensive. We find that the prediction performance of LN-GNN measured as an average normalized mean absolute error outperforms the baseline interaction network by a factor of 2–10 for different local neighborhood configurations. Furthermore, LN-GNN’s performance turns out to be very comparable to the instance-based ML framework while being an order of magnitude less expensive in terms of the required computation time. The results of this work can provide the foundations for establishing accurate models of colloidal particle interactions that are derived from real particle trajectory data. Computer science ; Computation ; Artificial neural network ; Graph ; Trajectory ; Particle (ecology) ; Nanoscopic scale ; Artificial intelligence ; Machine learning ; Statistical physics ; Theoretical computer science ; Nanotechnology ; Algorithm ; Materials science ; Physics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Valentin Delchevalerie | Nicolas de Moor | Louis Rassinfosse | Émile Haye | Benoît Frénay‬ | Stéphane Lucas
Source: Surface and Coatings Technology (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

When magnetron sputtering deposition meets machine learning: Application to process anomaly detection Sputter deposition ; Materials science ; Anomaly detection ; Sputtering ; Process (computing) ; Deposition (geology) ; Cavity magnetron ; Machine learning ; Artificial intelligence ; Thin film ; High-power impulse magnetron sputtering ; Computer science ; Nanotechnology ; Geology Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Fault Detection and Control Systems
Authors: Prakul Pandit | Rasul Abdusalamov | Mikhail Itskov | Barbara Milow | Ameya Rege
Source: PAMM (article, 2023-05-01)
Field: Chemistry — Spectroscopy

Data‐driven inverse design and optimisation of silica aerogel model networks Abstract Silica aerogels are highly porous ultralight materials with extremely low density and thermal conductivity. These exceptional properties of silica aerogels are often accounted to microstructure morphology, thus making them of keen research interest for analysing their structure‐property relationships. The classical approach for this involved the microstructure modelling of the silica aerogels with aggregation‐based modelling algorithm viz., diffusion‐limited cluster‐cluster aggregation (DLCA) and then performing finite element method (FEM) on the generated representative volume element (RVEs). However, the process often requires large computation time and resources. The objective of this work was thus to introduce an artificial intelligence approach based on neural networks and reinforcement learning to eliminate the necessity of generating and simulating 3D silica aerogel models for predicting their structural and mechanical properties. To this end for the forward prediction of the elastic modulus and fractal dimension of the silica aerogels from DLCA parameters, an artificial neural network was developed. Furthermore, to reverse engineer the material and perform inverse material design, a reinforcement learning framework was developed, that is shown to have learned to determine appropriate DLCA model parameters as actions for a desired fractal dimension and elastic modulus. Aerogel ; Fractal dimension ; Materials science ; Fractal ; Elastic modulus ; Artificial neural network ; Finite element method ; Microstructure ; Computation ; Porous medium ; Inverse ; Porosity ; Computer science ; Composite material ; Artificial intelligence ; Structural engineering ; Algorithm ; Geometry ; Mathematics ; Engineering Aerogels and thermal insulation ; Catalytic Processes in Materials Science
Authors: Yanxing Wang | Théo Jaffrelot Inizan | Chengwen Liu | Jean‐Philip Piquemal | Pengyu Ren
Source: ChemRxiv (preprint, 2023-11-16)
Field: Materials Science — Materials Chemistry

Incorporating Neural Networks into the AMOEBA Polarizable Force Field Neural network potentials (NNPs) have great potential to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. However, most of the NNPs remain restricted by the locality assumption that ensures the model's transferability and scalability but misses out the long-range interactions. Here we present an integrated non-reactive hybrid model, AMOEBA+NN, which employs the AMOEBA potential for the short- and long-range non-bonded interactions and an NNP to capture the remaining local (covalent) contributions. A first AMOEBA+NN model was trained on the conformational energy of ANI-1x dataset and tested on several external datasets ranging from small molecules to tetrapeptides. It was encouraging to see that the hybrid model becomes significantly advantageous over the baseline models in term of accuracy as the molecules get larger, offering perspectives for the development of a generalized and improved approach. Locality ; Transferability ; Scalability ; Ranging ; Force field (fiction) ; Amoeba (genus) ; Computer science ; Range (aeronautics) ; Artificial neural network ; Statistical physics ; Artificial intelligence ; Physics ; Machine learning ; Engineering ; Biology Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: P. Fowler | Timotej Bernat | Jacob Z. Pilgrim | G.A. Eiceman
Source: Analytica Chimica Acta (article, 2023-03-03)
Field: Chemistry — Spectroscopy

Neural network classification of mobility spectra for volatile organic compounds using tandem differential mobility spectrometry with field induced fragmentation Chemistry ; Ion-mobility spectrometry ; Fragmentation (computing) ; Spectral line ; Ion ; Analytical Chemistry (journal) ; Tandem ; Tandem mass spectrometry ; Protonation ; Software portability ; Pattern recognition (psychology) ; Mass spectrometry ; Artificial intelligence ; Chromatography ; Computer science ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography
Authors: Ravipat Lapcharoensuk | Noppadon Phannote | Dimae Kasetyangyunsapa
Source:  (article, 2023-03-03)
Field: Chemistry — Analytical Chemistry

Performance Comparison of Machine Learning Algorithms for Identification of Physiological Maturity of Pineapple using Optical Property Pineapple is important fruit of Thailand which is consumed in its fresh state or in processed products. Typically, harvested dates affected to quality of pineapple fresh. Identification of pineapple harvested dates at raw material receiving state in factory is very difficult. This research aims to determination of appropriate machine learning algorithm for Identifying maturity of pineapple using optical property. Color of pineapple fruits and fresh was measured by portable colorimeter on CIE system (L*, a* and b* values). The ten algorithms were fit to the training set including naive Bayes (NB), linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB) and adaptive boosting (AB). The best model for pineapple fruit were established from ANN while DT showed highest performance for pineapple fresh. The accuracy of ANN and DT for fruit and fresh models were 83 and 92% respectively. This finding point is novel technique for identification of pineapple according to harvested dates which it can apply to quality control and assurance in pineapple industries. Artificial intelligence ; Machine learning ; Naive Bayes classifier ; Decision tree ; Mathematics ; Support vector machine ; Algorithm ; Linear discriminant analysis ; Gradient boosting ; Artificial neural network ; Computer science ; Random forest Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Seifallah Fetni | Jocelyn Delahaye | Anne Habraken
Source: Software Impacts (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Python Data Driven framework for acceleration of Phase-Field simulations The passage describes the development of a numerical framework in Python to create and process a large dataset for time-series prediction using Deep Learning algorithms. The dataset is generated by solving the Cahn-Hilliard equation for spinodal decomposition of a binary alloy and is labeled to train the algorithms. Prior to training, dimensionality reduction is performed using Auto-encoders and Principal Component Analysis. The framework identifies three distinct latent dimensions/spaces for the datasets. The primary dataset was generated by running up to 10,000 High-Fidelity Phase-Field simulations in parallel using High-Performance Computing (HPC). The framework is compatible with all major operating systems and has been thoroughly tested on Python 3.7 and later versions. Python (programming language) ; Computer science ; Principal component analysis ; Dimensionality reduction ; Computational science ; Algorithm ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions
Authors: Fu-Qiang Wei | Zejian Huang | You Jian | Xinhua Dai | Xiang Fang | Shangzhong Jin
Source: Metrology and Measurement Systems (article, 2023-02-22)
Field: Chemistry — Spectroscopy

Quantitative analysis of reaction gases or exhaust using an online process mass spectrometer Online quantitative analysis of reaction gases or exhaust in industrial production is of great significance to improve the production capacity and process.Anovel method is developed for the online quantitative analysis of reaction gases or exhaust using quantitative mathematical models combined with the linear regression algorithm of machine learning. After accurately estimating the component gases and their contents in the reaction gases or exhaust, a ratio matrix is constructed to separate the relevant overlapping peaks. The ratio and calibration standard gases are detected, filtered, normalized, and linearly regressed with an online process mass spectrometer to correct the ratio matrices and obtain the relative sensitivity matrices. A quantitative mathematical model can be established to obtain the content of each component of the reaction gases or exhaust in real time. The maximum quantification error and relative standard deviation of the method are within 0.3% and 1%, after online quantification of the representative yeast fermenter tail gas. Exhaust gas ; Mass spectrometry ; Process (computing) ; Process engineering ; Gas analysis ; Spectrometer ; Environmental science ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Waste management ; Computer science ; Environmental chemistry ; Physics ; Engineering ; Chromatography Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Mass Spectrometry Techniques and Applications
Authors: Brayan Valenzuela | C.C. Viáfara | J.J. Penagos | Fabio Martínez
Source: Materials Today Communications (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Wear mechanisms and severity level classification in iron ore transfer chute linings by propagating regional labels coded as embedding deep learning vectors Random forest ; Transfer of learning ; Artificial intelligence ; Histogram ; Classifier (UML) ; Deep learning ; Pattern recognition (psychology) ; Materials science ; Support vector machine ; Computer science ; Machine learning ; Image (mathematics) Metal Alloys Wear and Properties ; Lubricants and Their Additives ; Tunneling and Rock Mechanics
Authors: Fatemeh Arab | Fariba Nazari | Francesc Illas
Source: Journal of Chemical Theory and Computation (article, 2023-02-03)
Field: Materials Science — Materials Chemistry

Artificial Neural Network-Derived Unified Six-Dimensional Potential Energy Surface for Tetra Atomic Isomers of the Biogenic [H, C, N, O] System Recognition of different structural patterns in different potential energy surface regions, such as in isomerizing quasilinear tetra atomic molecules, is important for understanding the details of underlying physics and chemistry. In this respect, using three variants of artificial neural networks (ANNs), we investigated the six-dimensional (6-D) singlet potential energy surfaces (PES) of tetra atomic isomers of the biogenic [H, C, N, O] system. At first, we constructed a separate ANN potential for each of the studied isomers. In the next step, a comparative assessment of the separate ANN models led to the setting up of a unified 6-D singlet PES equally and accurately describing all studied isomers. The constructed unified model yields relative energies comparable to those obtained either from the gold standard CCSD(T) method or from separate ANNs for each of the studied isomers. The accuracy of the unified singlet PES is on the order of 10<sup>-4</sup> Hartrees (0.1 kcal/mol). The developed PES in this work captures the main features of nonlinear and quasilinear tetra atomic isomers of this biogenic system. Tetra ; Artificial neural network ; Potential energy surface ; Surface (topology) ; Chemistry ; Computer science ; Nanotechnology ; Physics ; Computational chemistry ; Materials science ; Artificial intelligence ; Mathematics ; Organic chemistry ; Medicinal chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Anton Bochkarev | Yury Lysogorskiy | Ralf Drautz
Source: arXiv (Cornell University) (preprint, 2023-11-27)
Field: Materials Science — Materials Chemistry

Graph Atomic Cluster Expansion for semilocal interactions beyond equivariant message passing The Atomic Cluster Expansion provides local, complete basis functions that enable efficient parametrization of many-atom interactions. We extend the Atomic Cluster Expansion to incorporate graph basis functions. This naturally leads to representations that enable the efficient description of semilocal interactions in physically and chemically transparent form. Simplification of the graph expansion by tensor decomposition results in an iterative procedure that comprises current message-passing machine learning interatomic potentials. We demonstrate the accuracy and efficiency of the graph Atomic Cluster Expansion for a number of small molecules, clusters and a general-purpose model for carbon. We further show that the graph Atomic Cluster Expansion scales linearly with number of neighbors and layer depth of the graph basis functions. Cluster expansion ; Cluster (spacecraft) ; Graph ; Computer science ; Topology (electrical circuits) ; Mathematics ; Theoretical computer science ; Physics ; Combinatorics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Elena-Niculina Drăgoi | Irina Cârlescu | Răzvan Puf | Tudor Vasiliu | Elena‐Luiza Epure
Source: Crystals (article, 2023-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neuro-Evolutive Modeling of Transition Temperatures for Five-Ring Bent-Core Molecules Derived from Resorcinol Determining the phase transition temperature of different types of liquid crystals based on their structural parameters is a complex problem. The experimental work might be eliminated or reduced if prediction strategies could effectively anticipate the behavior of liquid crystalline systems. Neuro-evolutive modeling based on artificial neural networks (ANN) and a differential evolution (DE) algorithm was applied to predict the phase transition temperatures of bent-core molecules based on their resorcinol core. By these means, structural parameters such as the nature of the linking groups, the position, size and number of lateral substituents on the central core or calamitic wings and the length of the terminal chains were taken into account as factors that influence the liquid crystalline properties. A number of 172 bent-core compounds with symmetrical calamitic wings were selected from the literature. All corresponding structures were fully optimized using the DFT, and the molecular descriptors were calculated afterward. In the first step, the ANN-DE approach predicted the mesophase presence for the analyzed compounds. Next, ANN models were determined to predict the transition temperatures and whether or not the bent-core compounds were mesogenic. Simple structural, thermophysical and electronic structure descriptors were considered as inputs in the dataset. As a result, the models determined for each individual temperature have an R2 that varied from 0.89 to 0.98, indicating their capability to estimate the transition temperatures for the selected compounds. Moreover, the impact analysis of the inputs on the predicted temperatures showed that, in most cases, the presence or not of liquid crystalline properties represents the most influential feature. Bent molecular geometry ; Mesophase ; Molecule ; Phase transition ; Mesogen ; Core (optical fiber) ; Materials science ; Chemical physics ; Phase (matter) ; Glass transition ; Ring (chemistry) ; Chemistry ; Crystallography ; Thermodynamics ; Organic chemistry ; Polymer ; Physics ; Composite material ; Liquid crystalline Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality ; Surfactants and Colloidal Systems
Authors: Jingqi Sun | Xu‐Cai Wu | Zhi-Xiong Que | Wei‐Bing Zhang
Source: Acta Physica Sinica (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Prediction of ferromagnetic materials with high Curie temperature based on material composition information The search for ferromagnetic materials with high Curie temperature (&lt;i&gt;T&lt;/i&gt;&lt;sub&gt;c&lt;/sub&gt;) is a hot issue in condensed matter physics. In this work, an effective machine learning model of Curie temperature based on material component information is established to predict a variety of ferromagnetic materials with high Curie temperature. Based on the collected data of 1568 ferromagnetic materials, and taking the component information of ferromagnetic materials as descriptors, in this work four efficient machine learning models are constructed, namely support vector regression, kernel ridge regression, random forest and extremely randomized trees, through hyperparameter optimization and ten-break cross-validation. Of them, extremely randomized tree model has the best prediction performance, and its cross-validation &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; score can reach 81.48%. At the same time, the extremely randomized tree model is also used to predict 36949 materials in the materials project database, and 338 ferromagnetic materials with &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;c&lt;/sub&gt; greater than 600 K are found in this work. The method proposed in this paper can help obtain ferromagnetic materials with high Curie temperature and accelerate the process of ferromagnetic material design. Curie temperature ; Ferromagnetism ; Ferromagnetic material properties ; Materials science ; Condensed matter physics ; Hyperparameter ; Machine learning ; Curie ; Physics ; Computer science ; Magnetization Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Heusler alloys: electronic and magnetic properties
Authors: Karim Zongo | Hao Sun | Claudiane Ouellet‐Plamondon | Laurent Karim Béland
Source: Research Square (preprint, 2023-12-28)
Field: Chemistry — Spectroscopy

A unified moment tensor potential for silicon, oxygen, and silica Interatomic potential ; Tensor (intrinsic definition) ; Moment (physics) ; Density functional theory ; Silicon ; Point (geometry) ; Range (aeronautics) ; Materials science ; Charge (physics) ; Moment tensor ; Fidelity ; Computer science ; Chemical physics ; Statistical physics ; Physics ; Molecular dynamics ; Chemistry ; Computational chemistry ; Quantum mechanics ; Optoelectronics ; Mathematics Advanced NMR Techniques and Applications ; Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies
Authors: Sangni Xun | Fang Liu
Source: ChemRxiv (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Comparison of Machine-Learning and Classical ForceFields in Simulating the Solvation of Small OrganicMolecules in Acetonitrile Machine learning force fields (MLFFs) have emerged as a new method for molecular simulation that combines the accuracy of ab initio approaches with the computational efficiency of classical force fields. However, the performance of MLFFs in describing the solvation configuration has yet to be explored. Here, we compare and contrast the performance of ANI-1ccx MLFF, the GAFF classical force field, and the ab initio molecular dynamics (AIMD) in simulating nine organic solutes in acetonitrile solvents. We examine the solvent-solute interaction described by these methods from four aspects: the solute conformation landscape, the solvation shell structure, the structure and dynamics of the O-H⋯N hydrogen bond, and the dynamics of the first solvation shell. For solute conformation description, ANI-1ccx and GAFF both yield minima that agree with density functional theory optimization for rigid solutes. However, their results diverge for flexible solutes. For solvation shell structure description, ANI-1ccx agrees better with AIMD on the location of the first solvent shell than GAFF does. For the description of the O-H⋯N hydrogen bond formed between acetonitrile and the solute, ANI-1ccx generates stronger hydrogen bonds with shorter bond lengths, wider bond angles, and longer hydrogen bond lifetimes, agreeing better with DFT-optimized structure. ANI-1ccx also describes a more frequent exchange of acetonitrile molecules in and out of the first solvation shell than GAFF. Our study demonstrates the potential benefits of utilizing MLFF for simulating solution-phase dynamics and generating solvation configurations. Solvation ; Solvation shell ; Hydrogen bond ; Force field (fiction) ; Acetonitrile ; Chemistry ; Molecular dynamics ; Implicit solvation ; Ab initio ; Molecule ; Computational chemistry ; Chemical physics ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics