jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gordana Marković | Vaso Manojlović | Jovana Ružić | Miroslav Sokić
Source: Materials (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys with a low Young's modulus, and without the presence of cytotoxic alloying elements. Machine learning was used with aim to analyze biocompatible titanium alloys and predict the composition of Ti alloys with a low Young's modulus. A database was created using experimental data for alloy composition, Young's modulus, and mechanical and thermal properties of biocompatible titanium alloys. The Extra Tree Regression model was built to predict the Young's modulus of titanium alloys. By processing data of 246 alloys, the specific heat was discovered to be the most influential parameter that contributes to the lowering of the Young's modulus of titanium alloys. Further, the Monte Carlo method was used to predict the composition of future alloys with the desired properties. Simulation results of ten million samples, with predefined conditions for obtaining titanium alloys with a Young's modulus lower than 70 GPa, show that it is possible to obtain several multicomponent alloys, consisting of five main elements: titanium, zirconium, tin, manganese and niobium. Biocompatible material ; Materials science ; Titanium ; Titanium alloy ; Modulus ; Young's modulus ; Composite material ; Elastic modulus ; Metallurgy ; Biomedical engineering ; Alloy ; Engineering Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Additive Manufacturing Materials and Processes
Authors: Yunjian Li | Hui Pan | Zongjin Li
Source: Cement and Concrete Research (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Unravelling the dissolution dynamics of silicate minerals by deep learning molecular dynamics simulation: A case of dicalcium silicate Dissolution ; Silicate ; Molecular dynamics ; Silicate minerals ; Chemical physics ; Mineralogy ; Calcium silicate hydrate ; Kinetics ; Calcium silicate ; Materials science ; Chemistry ; Thermodynamics ; Cement ; Computational chemistry ; Physical chemistry ; Physics ; Metallurgy Electronic and Structural Properties of Oxides ; Concrete and Cement Materials Research ; Building materials and conservation
Authors: K. Song | Silvan Käser | Kai Töpfer | Luis Itza Vazquez-Salazar | Markus Meuwly
Source: The Journal of Chemical Physics (article, 2023-07-12)
Field: Materials Science — Materials Chemistry

PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations Full-dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide a means for accurate and efficient molecular simulations in the gas and condensed phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced into the newly developed pyCHARMM application programming interface. To illustrate the conception, validation, refining, and use of a typical workflow, para-chloro-phenol is considered as an example. The main focus is on how to approach a concrete problem from a practical perspective and applications to spectroscopic observables and the free energy for the -OH torsion in solution are discussed in detail. For the computed IR spectra in the fingerprint region, the computations for para-chloro-phenol in water are in good qualitative agreement with experiment carried out in CCl4. Moreover, relative intensities are largely consistent with experimental findings. The barrier for rotation of the -OH group increases from ∼3.5 kcal/mol in the gas phase to ∼4.1 kcal/mol from simulations in water due to favorable H-bonding interactions of the -OH group with surrounding water molecules. Observable ; Molecular dynamics ; Water model ; Chemistry ; Computer science ; Workflow ; Molecule ; Statistical physics ; Computational chemistry ; Thermodynamics ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Dexin Zhu | Kunming Pan | Yuan Wu | Xiaoye Zhou | X.W. Li | Yongpeng Ren | Sairu Shi | Hua Yu | Shizhong Wei | Hong‐Hui Wu | Xusheng Yang
Source: Rare Metals (article, 2023-05-12)
Field: Materials Science — Materials Chemistry

Improved material descriptors for bulk modulus in intermetallic compounds via machine learning Abstract Bulk modulus is an important mechanical property in the optimal design and selection of intermetallic compounds. In this study, bulk modulus datasets of intermetallic compounds were collected, and the features affecting the bulk modulus of intermetallics were screened via feature engineering. Three features B cal , d B avg , and TIE (corresponding to calculated bulk modulus, mean bulk modulus, and third ionization energy, respectively) were found to be the dominant factors influencing bulk modulus and can be extended to other multi‐component alloys. Particularly, we predicted the bulk modulus with an accuracy of 95% using surrogate machine learning models with the selected features, and these features were also demonstrated to be effective for high‐entropy alloys. Moreover, symbolic regression provided an expression for the relationship between bulk modulus and the screened features. The machine learning models provide a new approach for optimizing and predicting the bulk moduli of intermetallic compounds. Intermetallic ; Materials science ; Bulk modulus ; Modulus ; Elastic modulus ; Moduli ; Young's modulus ; Composite material ; Thermodynamics ; Physics ; Alloy Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties ; High Entropy Alloys Studies
Authors: Alexey S. Kotykhov | Konstantin Gubaev | Max Hodapp | Christian Tantardini | Alexander V. Shapeev | Ivan S. Novikov
Source: Scientific Reports (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al We propose a machine-learning interatomic potential for multi-component magnetic materials. In this potential we consider magnetic moments as degrees of freedom (features) along with atomic positions, atomic types, and lattice vectors. We create a training set with constrained DFT (cDFT) that allows us to calculate energies of configurations with non-equilibrium (excited) magnetic moments and, thus, it is possible to construct the training set in a wide configuration space with great variety of non-equilibrium atomic positions, magnetic moments, and lattice vectors. Such a training set makes possible to fit reliable potentials that will allow us to predict properties of configurations in the excited states (including the ones with non-equilibrium magnetic moments). We verify the trained potentials on the system of bcc Fe-Al with different concentrations of Al and Fe and different ways Al and Fe atoms occupy the supercell sites. Here, we show that the formation energies, the equilibrium lattice parameters, and the total magnetic moments of the unit cell for different Fe-Al structures calculated with machine-learning potentials are in good correspondence with the ones obtained with DFT. We also demonstrate that the theoretical calculations conducted in this study qualitatively reproduce the experimentally-observed anomalous volume-composition dependence in the Fe-Al system. Magnetic moment ; Excited state ; Lattice (music) ; Supercell ; Physics ; Atomic physics ; Statistical physics ; Condensed matter physics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xinyu Li | Raymond Chiong | Zhongyi Hu | Alister J. Page
Source: Computational and Theoretical Chemistry (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

A graph neural network model with local environment pooling for predicting adsorption energies Pooling ; Chemistry ; Adsorption ; Catalysis ; Artificial neural network ; Convolutional neural network ; Graph ; Artificial intelligence ; Physical chemistry ; Computer science ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Sree Harsha Bhimineni | Tianhang Zhou | Saeed Mahmoodpour | Mrityunjay Singh | Wei Li | Saientan Bag | Ingo Sass | Florian Müller‐Plathe
Source: Industrial & Engineering Chemistry Research (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Investigation of the Diffusion of Hydrogen in Brine by Performing Molecular Dynamics Simulation Deep saline aquifers are some of the best options for large-scale and long-term hydrogen storage. Predicting the diffusion coefficient of hydrogen molecules at the conditions of saline aquifers is critical for the modeling of hydrogen storage. The diffusion coefficient of hydrogen molecules in chloride brine with different cations (Na+, K+, and Ca2+) containing up to 5 mol/kgH2O concentration is numerically investigated using molecular dynamics (MD) simulation. A wide range of pressure (1–218 atm) and temperature (298–648 K) conditions are applied to cover the realistic operational conditions of the aquifers. We find that the temperature, pressure, and properties of ions (compositions and concentrations) affect the hydrogen diffusion coefficient. An Arrhenius behavior of the effect of temperature on the diffusion coefficient has been observed with the temperature-independent parameters fitted by using the ion concentration under constant pressure. However, it is noted that the pressure strongly affects the diffusive behavior of hydrogen at the high temperature (≥400 K) regime, indicating the inaccuracy of the Arrhenius model. Hence, we combine the obtained MD results with four models of machine learning (ML), including linear regression (LR), random forest (RF), extra tree (ET), and gradient boosting (GB) to provide effective predictions on the hydrogen diffusion. The resultant combination of the GB model with MD data predicts the diffusion of hydrogen more effectively as compared to the Arrhenius model and other ML models. Moreover, a post hoc analysis (feature importance rank) has been performed to extract the correlation between physical descriptors and simulation results from ML models. Our work provides a promising route for a quick and cost-effective diffusion coefficient determination for multiple and complex brine solutions with a wide range of temperature, pressure, and ion concentration by the combination of MD simulations and ML techniques. Arrhenius equation ; Thermodynamics ; Hydrogen ; Chemistry ; Arrhenius plot ; Diffusion ; Brine ; Effective diffusion coefficient ; Analytical Chemistry (journal) ; Activation energy ; Physical chemistry ; Chromatography ; Organic chemistry ; Physics Hydrogen Storage and Materials ; CO2 Sequestration and Geologic Interactions ; Groundwater flow and contamination studies
Authors: Aakash Ashok Naik | Christina Ertural | Nidal Dhamrait | Philipp Benner | Janine George
Source: Scientific Data (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

A Quantum-Chemical Bonding Database for Solid-State Materials An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features. Workflow ; Computer science ; Heuristics ; Density functional theory ; Chemical bond ; Benchmark (surveying) ; Software ; Materials science ; Database ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Henning Otto Brinkhaus | Kohulan Rajan | Jonas Schaub | Achim Zielesny | Christoph Steinbeck
Source: Current Opinion in Structural Biology (review, 2023-02-17)
Field: Materials Science — Materials Chemistry

Open data and algorithms for open science in AI-driven molecular informatics Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future. Computer science ; Informatics ; Data science ; Deep learning ; Big data ; Artificial intelligence ; Open data ; Open research ; Open science ; Cloud computing ; World Wide Web ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Harsh Bhatia | Fikret Aydin | Timothy S. Carpenter | Felice C. Lightstone | Peer‐Timo Bremer | Helgi I. Ingólfsson | Dwight V. Nissley | Frederick H. Streitz
Source: Current Opinion in Structural Biology (review, 2023-03-24)
Field: Materials Science — Materials Chemistry

The confluence of machine learning and multiscale simulations Computer science ; Artificial intelligence ; Scale (ratio) ; Multiscale modeling ; Field (mathematics) ; Deep learning ; Data science ; Confluence ; Machine learning ; Science and engineering ; Bioinformatics ; Biology ; Mathematics ; Engineering ; Engineering ethics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuxing Zhou | Stephen R. Elliott | Volker L. Deringer
Source: Angewandte Chemie International Edition (article, 2023-03-14)
Field: Materials Science — Materials Chemistry

Structure and Bonding in Amorphous Red Phosphorus** Amorphous red phosphorus (a-P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bonding in a-P from first principles, combining machine-learning and density-functional theory (DFT) methods. We show that a-P structures exist with a range of energies slightly higher than those of phosphorus nanorods, to which they are closely related, and that the stability of a-P is linked to the degree of structural relaxation and medium-range order. We thus complete the stability range of phosphorus allotropes [Angew. Chem. Int. Ed. 2014, 53, 11629] by now including the previously poorly understood amorphous phase, and we quantify the covalent and van der Waals interactions in all main phases of phosphorus. We also study the electronic densities of states, including those of hydrogenated a-P. Beyond the present study, our structural models are expected to enable wider-ranging first-principles investigations-for example, of a-P-based battery materials. van der Waals force ; Amorphous solid ; Phosphorus ; Structural stability ; Covalent bond ; Chemical physics ; Density functional theory ; Materials science ; Relaxation (psychology) ; Crystallography ; Nanotechnology ; Computational chemistry ; Chemistry ; Molecule ; Organic chemistry ; Biology Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Vishu Gupta | Kamal Choudhary | Yuwei Mao | Kewei Wang | Francesca Tavazza | Carelyn E. Campbell | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Journal of Chemical Information and Modeling (article, 2023-03-27)
Field: Materials Science — Materials Chemistry

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available data and accelerate materials discovery and design for future applications. In an attempt to assist with this process, we deploy predictive models for multiple material properties, given the composition of the material. The deep learning models described here are built using a cross-property deep transfer learning technique, which leverages source models trained on large data sets to build target models on small data sets with different properties. We deploy these models in an online software tool that takes a number of material compositions as input, performs preprocessing to generate composition-based attributes for each material, and feeds them into the predictive models to obtain up to 41 different material property values. The material property predictor is available online at http://ai.eecs.northwestern.edu/MPpredictor. Computer science ; Property (philosophy) ; Preprocessor ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Deep learning ; Process (computing) ; Predictive modelling ; Data pre-processing Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Sefater Gbashi | Tintswalo Lindi Maselesele | Patrick Berka Njobeh | Tumisi Beiri Jeremiah Molelekoa | Samson A. Oyeyinka | Rhulani Makhuvele | Oluwafemi Ayodeji Adebo
Source: Scientific Reports (article, 2023-07-20)
Field: Chemistry — Analytical Chemistry

Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd–grape beverage production Artificial neural networks (ANNs) have in recent times found increasing application in predictive modelling of various food processing operations including fermentation, as they have the ability to learn nonlinear complex relationships in high dimensional datasets, which might otherwise be outside the scope of conventional regression models. Nonetheless, a major limiting factor of ANNs is that they require quite a large amount of training data for better performance. Obtaining such an amount of data from biological processes is usually difficult for many reasons. To resolve this problem, methods are proposed to inflate existing data by artificially synthesizing additional valid data samples. In this paper, we present a generative adversarial network (GAN) able to synthesize an infinite amount of realistic multi-dimensional regression data from limited experimental data (n = 20). Rigorous testing showed that the synthesized data (n = 200) significantly conserved the variances and distribution patterns of the real data. Further, the synthetic data was used to generalize a deep neural network. The model trained on the artificial data showed a lower loss (2.029 ± 0.124) and converged to a solution faster than its counterpart trained on real data (2.1614 ± 0.117). Artificial neural network ; Bitter gourd ; Computer science ; Artificial intelligence ; Generative adversarial network ; Adversarial system ; Production (economics) ; Machine learning ; Deep learning ; Traditional medicine ; Momordica Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Erin C. Day | Supraja S. Chittari | Matthew P. Bogen | Abigail Knight
Source: ACS Polymers Au (review, 2023-12-05)
Field: Materials Science — Materials Chemistry

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials. Workflow ; Computer science ; Expansive ; Selection (genetic algorithm) ; Throughput ; Representation (politics) ; Characterization (materials science) ; Data science ; Artificial intelligence ; Nanotechnology ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation