jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fanbo Sun | JCS Kadupitiya | Vikram Jadhao
Source: Journal of Chemical Theory and Computation (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations The performance promise of machine learning surrogates of molecular dynamics simulations of soft materials is significant but generally comes at the cost of acquiring large training datasets to learn the complex relationships between input soft material attributes and output properties. Under the constraint of limited high-performance computing resources, optimizing the size of the training datasets becomes paramount. Using an artificial neural network based surrogate for molecular dynamics simulations of confined electrolytes, we explore the tradeoff between surrogate accuracy and computational gains. Accuracy is assessed by computing the root-mean-square errors between the surrogate predictions and the ground truth results obtained via molecular dynamics simulations. The computational performance is judged by evaluating the speedup which incorporates the training dataset creation time. Improvement in accuracy occurs with a loss of speedup, which scales as the inverse of the training dataset size. The link between surrogate generalizability and the accuracy-speedup tradeoff is assessed by examining the errors incurred in surrogate predictions on unseen, interpolated input variables and developing a net speedup metric to capture the associated gains. Speedup ; Computer science ; Surrogate model ; Generalizability theory ; Metric (unit) ; Machine learning ; Artificial intelligence ; Molecular dynamics ; Parallel computing ; Mathematics ; Statistics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Arash Sarhangi Fard | Joseph A. Moebus | George Rodríguez
Source: Journal of Advanced Manufacturing and Processing (article, 2023-05-11)
Field: Materials Science — Materials Chemistry

Machine learning prediction of mechanical and optical properties of uniaxially oriented polymer films Abstract Improving properties of polymers can bring about tremendous opportunities in developing new applications. However, the commonly used trial‐and‐error method cannot meet the current need for new materials. We demonstrate the utility of Machine Learning (ML) algorithms in creating structure‐process‐property models based on industrial data in polymer processing. In this study, ML algorithms were used to predict the optical and tensile strength of multi‐layer co‐extrusion polyethylene films as a function of material structures and process parameters. The input features to predict the mechanical and optical properties are the composition of five‐layer polyethylene film, polyethylene molecular properties like the amount of long chain branching , and the extrusion process conditions. Different data featuring steps are conducted to improve the quality of the input data: (1) feature importance scoring using an ensemble algorithm (XGBoost); (2) application of autoencoder to reduce the dimensionality; (3) replacing the categorical inputs with molecular characteristic properties. We then use this data to build an Artificial Neural Network. Finally, the prediction capability of the resulting model was investigated. This project demonstrates a successful end‐to‐end execution of a material data science project; from understanding material science, data engineering, algorithm development, and the model evaluation. Computer science ; Autoencoder ; Artificial neural network ; Process (computing) ; Materials science ; Categorical variable ; Extrusion ; Curse of dimensionality ; Machine learning ; Polyethylene ; Artificial intelligence ; Algorithm ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Statistical and Computational Modeling
Authors: Yogesh Khatri | Arti Kashyap
Source: APL Machine Learning (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Advancing magnetic material discovery through machine learning: Unveiling new manganese-based materials Magnetic materials are used in a variety of applications, such as electric generators, speakers, hard drives, MRI machines, etc. Discovery of new magnetic materials with desirable properties is essential for advancement in these applications. In this research article, we describe the development and validation of a machine-learning model to discover new manganese-based stable magnetic materials. The machine learning model is trained on the input data from the Materials Project database to predict the magnetization and formation energy of the materials. New hypothetical structures are made using the substitution method, and the properties are predicted using the machine learning model to select the materials with desired properties. Harnessing the power of machine learning allows us to intelligently narrow down the vast pool of potential candidates. By doing so, we deftly reduce the number of materials that warrant in-depth examination using density functional theory, rendering the task more manageable and efficient. The selected materials, seemingly promising with their magnetic potential, undergo a meticulous validation process using the Vienna Ab initio Simulation Package, grounded in density functional theory. Our results underscore the paramount significance of input data in the efficacy of the machine learning model. Particularly in the realm of magnetic materials, the proper initialization of atomic magnetic spins holds the key to converging upon the true magnetic state of each material. Computer science ; Machine learning ; Artificial intelligence ; Magnetization ; Physics ; Magnetic field Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Minjie Chen
Source:  (preprint, 2023-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics &lt;p&gt;This paper applies machine learning to power mag- netics modeling. We first introduce an open-source database – MagNet – which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500,000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and B–H loops. Machine learning allows multiple factors that may influence the magnetic characteristics to be mod- eled in a unified framework, while provides insights to quantify the complexity of magnetic characteristics and reduce the size of the measurement data required to build a precise model. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics. The behaviors of a typical power magnetic material (TDK N87) across a wide range of operating conditions (e.g., temperature, waveform, dc-bias) can be well described by a small-scale neural network (204 KB) which is 2,500 times smaller than the raw measured time-series data (512 MB), paving the way for “neural networks as datasheet” to assist power magnetics design. &lt;/p&gt; Datasheet ; Artificial neural network ; Magnet ; Power (physics) ; Waveform ; Computer science ; Electronic engineering ; Artificial intelligence ; Machine learning ; Engineering ; Electrical engineering ; Voltage ; Physics Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Magnetic properties of thin films
Authors: Le-Chi Lin | Sheng-Jer Chen | Hsiu-Yu Yu
Source: Polymers (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Connecting Structural Characteristics and Material Properties in Phase-Separating Polymer Solutions: Phase-Field Modeling and Physics-Informed Neural Networks The formed morphology during phase separation is crucial for determining the properties of the resulting product, e.g., a functional membrane. However, an accurate morphology prediction is challenging due to the inherent complexity of molecular interactions. In this study, the phase separation of a two-dimensional model polymer solution is investigated. The spinodal decomposition during the formation of polymer-rich domains is described by the Cahn-Hilliard equation incorporating the Flory-Huggins free energy description between the polymer and solvent. We circumvent the heavy burden of precise morphology prediction through two aspects. First, we systematically analyze the degree of impact of the parameters (initial polymer volume fraction, polymer mobility, degree of polymerization, surface tension parameter, and Flory-Huggins interaction parameter) in a phase-separating system on morphological evolution characterized by geometrical fingerprints to determine the most influential factor. The sensitivity analysis provides an estimate for the error tolerance of each parameter in determining the transition time, the spinodal decomposition length, and the domain growth rate. Secondly, we devise a set of physics-informed neural networks (PINN) comprising two coupled feedforward neural networks to represent the phase-field equations and inversely discover the value of the embedded parameter for a given morphological evolution. Among the five parameters considered, the polymer-solvent affinity is key in determining the phase transition time and the growth law of the polymer-rich domains. We demonstrate that the unknown parameter can be accurately determined by renormalizing the PINN-predicted parameter by the change of characteristic domain size in time. Our results suggest that certain degrees of error are tolerable and do not significantly affect the morphology properties during the domain growth. Moreover, reliable inverse prediction of the unknown parameter can be pursued by merely two separate snapshots during morphological evolution. The latter largely reduces the computational load in the standard data-driven predictive methods, and the approach may prove beneficial to the inverse design for specific needs. Spinodal decomposition ; Spinodal ; Flory–Huggins solution theory ; Materials science ; Polymer ; Phase (matter) ; Thermodynamics ; Statistical physics ; Biological system ; Physics ; Chemistry ; Organic chemistry Block Copolymer Self-Assembly ; Model Reduction and Neural Networks ; Lattice Boltzmann Simulation Studies
Authors: Zhen Fan | Zhixin Sun | Ai Wang | Yaohui Yin | Guangyong Jin | Chao Xin
Source: Journal of Electronic Materials (article, 2023-04-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Classification Model for Screening of Infrared Nonlinear Optical Crystals Oversampling ; Generalizability theory ; Nonlinear system ; Infrared ; Artificial intelligence ; Computer science ; Laser ; Materials science ; Nonlinear optical ; Crystal (programming language) ; Nonlinear optics ; Machine learning ; Classifier (UML) ; Pattern recognition (psychology) ; Optics ; Bandwidth (computing) ; Physics ; Mathematics ; Telecommunications ; Statistics Crystal Structures and Properties ; Nonlinear Optical Materials Research ; Nonlinear Optical Materials Studies
Authors: Elisabeth J. Schiessler | Tim Würger | Bahram Vaghefinazari | Sviatlana V. Lamaka | Robert H. Meißner | Christian J. Cyron | Mikhail L. Zheludkevich | Christian Feiler | Roland C. Aydin
Source: npj Materials Degradation (article, 2023-09-12)
Field: Materials Science — Materials Chemistry

Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features Abstract Small organic molecules can alter the degradation rates of the magnesium alloy ZE41. However, identifying suitable candidate compounds from the vast chemical space requires sophisticated tools. The information contained in only a few molecular descriptors derived from recursive feature elimination was previously shown to hold the potential for determining such candidates using deep neural networks. We evaluate the capability of these networks to generalise by blind testing them on 15 randomly selected, completely unseen compounds. We find that their generalisation ability is still somewhat limited, most likely due to the relatively small amount of available training data. However, we demonstrate that our approach is scalable; meaning deficiencies caused by data limitations can presumably be overcome as the data availability increases. Finally, we illustrate the influence and importance of well-chosen descriptors towards the predictive power of deep neural networks. Chemical space ; Computer science ; Scalability ; Artificial intelligence ; Artificial neural network ; Deep learning ; Deep neural networks ; Feature (linguistics) ; Machine learning ; Space (punctuation) ; Pattern recognition (psychology) ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Chunhui Li | Benjamin Gilbert | Steven Farrell | Piotr Zarzycki
Source: Journal of Chemical Information and Modeling (article, 2023-06-12)
Field: Materials Science — Materials Chemistry

Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning Molecular dynamics simulation is an indispensable tool for understanding the collective behavior of atoms and molecules and the phases they form. Statistical mechanics provides accurate routes for predicting macroscopic properties as time-averages over visited molecular configurations - microstates. However, to obtain convergence, we need a sufficiently long record of visited microstates, which translates to the high-computational cost of the molecular simulations. In this work, we show how to use a point cloud-based deep learning strategy to rapidly predict the structural properties of liquids from a single molecular configuration. We tested our approach using three homogeneous liquids with progressively more complex entities and interactions: Ar, NO, and H<sub>2</sub>O under varying pressure and temperature conditions within the liquid state domain. Our deep neural network architecture allows rapid insight into the liquid structure, here probed by the radial distribution function, and can be used with molecular/atomistic configurations generated by either simulation, first-principle, or experimental methods. Molecular dynamics ; Statistical physics ; Convergence (economics) ; Radial distribution function ; Work (physics) ; Domain (mathematical analysis) ; Computer science ; Homogeneous ; Function (biology) ; Chemical physics ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Thermodynamics ; Mathematics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Protein Structure and Dynamics
Authors: Yasufumi Sakai | Dang Duy Thang | Shigeki Fukuta | Koichi Shirahata | Atsushi Ishikawa | Atsuki Inoue | Hiroshi Kawaguchi | Árni Björn Höskuldsson | Egill Skúlason
Source: Procedia Computer Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Self-Supervised Learning with Atom Replacement for Catalyst Energy Prediction by Graph Neural Networks Recently, material search utilizing the material informatics (MI) technique is considered to be an efficient approach to discovering new materials. Energy or environment-related materials are deeply related to chemical reactions, and the reactivity of materials is determined by the electronic energy (or internal energy) of materials and reactant systems. The electronic energy is often calculated by first-principles calculation which is based on the Schrödinger equation. Among them, a method called density functional theory (DFT) is widely used nowadays. Since the computational cost of the DFT calculation is relatively high, many graph neural networks (GNNs) for energy prediction have been proposed to obtain the energy values quickly. However, it is difficult to obtain a large amount of labeled data for GNNs training, because a large amount of the DFT data is necessary for training. Although self-supervised learning (SSL) methods using unlabeled data, i.e., no DFT energy information is needed, have been proposed to improve the GNNs’ energy prediction accuracy, existing SSL methods use a non-existent atom of “mask”; this may lead to the inefficient GNNs training. In this work, we propose a mask-less SSL method by replacing some atoms in the target material systems, aiming to improve the energy prediction accuracy of GNNs. Our target systems are catalysts, and here the atoms in unlabeled catalysts are replaced with other actual existing atoms. Then, GNNs predict whether the atoms have been replaced or not for all atoms composing the material. After that, GNNs pre-trained by the proposed SSL are fine-tuned by downstream tasks. We demonstrate the superiority of our proposed method on the Open Catalyst dataset and Poisoned Catalyst dataset using the energy prediction GNNs, CGCNN and PaiNN. The GNNs pre-trained by the proposed SSL achieve the best mean absolute error of predicted energy rather than that of the existing mask-based SSL in all experimental conditions. Computer science ; Artificial neural network ; Atom (system on chip) ; Density functional theory ; Energy (signal processing) ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Computational chemistry ; Chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Topic Modeling
Authors: Jakub Kubečka | Vitus Besel | Ivo Neefjes | Yosef Knattrup | Theo Kurtén | Hanna Vehkamäki | Jonas Elm
Source: ChemRxiv (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning Computational modeling of atmospheric molecular clusters requires a comprehensive understanding of their complex configurational spaces, interaction patterns, stabilities against fragmentation, and even dynamic behaviors. To address these needs, we introduce the Jammy Key framework, a collection of automated scripts that facilitate and streamline molecular cluster modeling workflows. Jammy Key handles file manipulations between a variety of integrated 3rd party programs. The framework is divided into three main functionalities: (1) Jammy Key for Configurational Sampling (JKCS) to perform systematic configurational sampling of molecular clusters, (2) Jammy Key for Quantum Chemistry (JKQC) to analyze commonly used quantum chemistry output files and facilitate database construction, handling, and analysis, and (3) Jammy Key for Machine Learning (JKML) to manage machine learning methods in optimizing molecular cluster modeling. This automation and machine learning utilization significantly reduces manual labor, greatly speeds up the search for molecular cluster configurations, and thus increases the number of systems that can be studied. Following the example of the Atmospheric Cluster Database (ACDB) of Elm [ACS Omega, 4, 10965–10984, 2019], the molecular clusters modeled in our group using the Jammy Key framework have been stored in an improved online GitHub repository named ACDB 2.0. In this work, we present the Jammy Key package alongside its assorted applications, which underline its versatility. Using several illustrative examples, we discuss how to choose appropriate combinations of methodologies for treating particular cluster types, including reactive, multi-component, charged, or radical clusters, as well as clusters containing flexible or multi-conformer monomers or heavy atoms. Finally, we present a detailed example of using the tools for atmospheric acid–base clusters. Computer science ; Workflow ; Key (lock) ; Cluster (spacecraft) ; Component (thermodynamics) ; Automation ; Database ; Physics ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Junfei Zhang | Yueqi Li | Xinbo Zhou
Source:  (preprint, 2023-01-02)
Field: Materials Science — Materials Chemistry

Machine-Learning Prediction of the Computed Band Gaps of Double Perovskite Materials Prediction of the electronic structure of functional materials is essential for the engineering of new devices. Conventional electronic structure prediction methods based on density functional theory (DFT) suffer from not only high computational cost, but also limited accuracy arising from the approximations of the exchange-correlation functional. Surrogate methods based on machine learning have garnered much attention as a viable alternative to bypass these limitations, especially in the prediction of solid-state band gaps, which motivated this research study. Herein, we construct a random forest regression model for band gaps of double perovskite materials, using a dataset of 1306 band gaps computed with the GLLBSC (Gritsenko, van Leeuwen, van Lenthe, and Baerends solid correlation) functional. Among the 20 physical features employed, we find that the bulk modulus, superconductivity temperature, and cation electronegativity exhibit the highest importance scores, consistent with the physics of the underlying electronic structure. Using the top 10 features, a model accuracy of 85.6% with a root mean square error of 0.64 eV is obtained, comparable to previous studies. Our results are significant in the sense that they attest to the potential of machine learning regressions for the rapid screening of promising candidate functional materials. Electronegativity ; Density functional theory ; Machine learning ; Random forest ; Artificial intelligence ; Band gap ; Mean squared error ; Electronic structure ; Perovskite (structure) ; Computer science ; Electronic band structure ; Materials science ; Statistical physics ; Algorithm ; Condensed matter physics ; Mathematics ; Physics ; Chemistry ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications