jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter Eastman | Pavan Kumar Behara | David Dotson | Raimondas Galvelis | John E. Herr | Josh T. Horton | Yuezhi Mao | John D. Chodera | Benjamin P. Pritchard | Yuanqing Wang | Gianni De Fabritiis | Thomas E. Markland
Source: arXiv (Cornell University) (preprint, 2022-09-21)
Field: Materials Science — Materials Chemistry

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations. Economic shortage ; Chemical space ; Molecule ; Computer science ; Set (abstract data type) ; Covalent bond ; Drug discovery ; Computational chemistry ; Multipole expansion ; Range (aeronautics) ; Biomolecule ; Resource (disambiguation) ; Machine learning ; Chemistry ; Artificial intelligence ; Chemical physics ; Physics ; Nanotechnology ; Materials science ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Chemical Synthesis and Analysis
Authors: Jialong Guo | Ziyi Chen | Zhiwei Liu | Xianwei Li | Z. Y. Xie | Zongguo Wang | Yangang Wang
Source: Scientific Reports (article, 2022-09-12)
Field: Materials Science — Materials Chemistry

Neural network training method for materials science based on multi-source databases The fourth paradigm of science has achieved great success in material discovery and it highlights the sharing and interoperability of data. However, most material data are scattered among various research institutions, and a big data transmission will consume significant bandwidth and tremendous time. At the meanwhile, some data owners prefer to protect the data and keep their initiative in the cooperation. This dilemma gradually leads to the "data island" problem, especially in material science. To attack the problem and make full use of the material data, we propose a new strategy of neural network training based on multi-source databases. In the whole training process, only model parameters are exchanged and no any external access or connection to the local databases. We demonstrate its validity by training a model characterizing material structure and its corresponding formation energy, based on two and four local databases, respectively. The results show that the obtained model accuracy trained by this method is almost the same to that obtained from a single database combining all the local ones. Moreover, different communication frequencies between the client and server are also studied to improve the model training efficiency, and an optimal frequency is recommended. Interoperability ; Computer science ; Dilemma ; Database ; Artificial neural network ; Bandwidth (computing) ; Process (computing) ; Data mining ; Data science ; Machine learning ; World Wide Web ; Computer network Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Advanced Graph Neural Networks
Authors: Masaya Kumagai | Yuki Ando | Atsumi Tanaka | Koji Tsuda | Yukari Katsura | Ken Kurosaki
Source: Science and Technology of Advanced Materials Methods (article, 2022-08-04)
Field: Materials Science — Materials Chemistry

Effects of data bias on machine-learning–based material discovery using experimental property data Materials informatics (MI) research, which is the discovery of new materials through machine learning (ML) using large-scale material data, has attracted considerable attention in recent years. However, in general, the large-scale material data used in MI are biased owing to differences in the targeted material domains. Moreover, most studies on MI have not clearly demonstrated the influence of data bias on ML models. In this study, we clarify the influence of data bias on ML models by combining the concept of the applicability domain and clustering for large-scale experimental property data in the Starrydata2 material database previously developed by our group. The results show that data bias influences the error and reliability of the predictions made by the ML model. The predictions of the ML model within the applicability domain are highly reliable compared to those made outside the domain. This indicates that the material space that can be reliably discovered by the constructed ML model is limited. Nonetheless, we apply the ML model to a large dataset comprising various material classes and find that new materials similar to known materials can be proposed within a limited space. Thus, our findings demonstrate the importance of considering data bias when constructing and evaluating ML models in MI. Property (philosophy) ; Computer science ; Cluster analysis ; Experimental data ; Domain (mathematical analysis) ; Reliability (semiconductor) ; Data mining ; Scale (ratio) ; Machine learning ; Artificial intelligence ; Statistics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Sathish Kumar Mudedla | A. Braka | Sangwook Wu
Source: Frontiers in Molecular Biosciences (article, 2022-10-11)
Field: Materials Science — Materials Chemistry

Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules Force fields for drug-like small molecules play an essential role in molecular dynamics simulations and binding free energy calculations. In particular, the accurate generation of partial charges on small molecules is critical to understanding the interactions between proteins and drug-like molecules. However, it is a time-consuming process. Thus, we generated a force field for small molecules and employed a machine learning (ML) model to rapidly predict partial charges on molecules in less than a minute of time. We performed density functional theory (DFT) calculation for 31770 small molecules that covered the chemical space of drug-like molecules. The partial charges for the atoms in a molecule were predicted using an ML model trained on DFT-based atomic charges. The predicted values were comparable to the charges obtained from DFT calculations. The ML model showed high accuracy in the prediction of atomic charges for external test data sets. We also developed neural network (NN) models to assign atom types, phase angles and periodicities. All the models performed with high accuracy on test data sets. Our code calculated all the descriptors that were needed for the prediction of force field parameters and produced topologies for small molecules by combining results from ML and NN models. To assess the accuracy of the predicted force field parameters, we calculated solvation free energies for small molecules, and the results were in close agreement with experimental free energies. The AI-generated force field was effective in the fast and accurate generation of partial charges and other force field parameters for small drug-like molecules. Partial charge ; Force field (fiction) ; Solvation ; Molecule ; Field (mathematics) ; Density functional theory ; Atom (system on chip) ; Chemistry ; Implicit solvation ; Statistical physics ; Molecular dynamics ; Small molecule ; Computational chemistry ; Chemical physics ; Physics ; Computer science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ümit H. Kaynar | İkbal Gözde Kaptanoğlu | Sermin Çam-Kaynar | Onur Uğurlu | Sabriye Yuşan | Şule Aytaş | Aysh Y. Madkhli | Nursel Can
Source: Applied Radiation and Isotopes (article, 2022-12-07)
Field: Chemistry — Inorganic Chemistry

Adsorption of thorium (IV) ions using a novel borate-based nano material Ca3Y2B4O12: Application of response surface methodology and Artificial Neural Network Adsorption ; Response surface methodology ; Thorium ; Boron ; Aqueous solution ; Materials science ; Nuclear chemistry ; Nano- ; Chemistry ; Uranium ; Chromatography ; Composite material ; Metallurgy ; Organic chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Nuclear materials and radiation effects
Authors: Yu‐Yen Chen | M. Ross Kunz | Xiaolong He | Rebecca Fushimi
Source: Current Opinion in Chemical Engineering (article, 2022-06-11)
Field: Materials Science — Materials Chemistry

Recent progress toward catalyst properties, performance, and prediction with data-driven methods Computer science ; Materials science ; Biochemical engineering ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Chaoyi Huang | Wen‐Yang Gao | Yingdie Zheng | Wei Wang | Yue Zhang | Kai Liu
Source: The Science of The Total Environment (article, 2022-11-16)
Field: Materials Science — Materials Chemistry

Universal machine-learning algorithm for predicting adsorption performance of organic molecules based on limited data set: Importance of feature description Adsorption ; Molecule ; Molecular descriptor ; Organic molecules ; Artificial neural network ; Feature (linguistics) ; Biological system ; Chemistry ; Set (abstract data type) ; Aqueous solution ; Algorithm ; Computer science ; Artificial intelligence ; Computational chemistry ; Machine learning ; Quantitative structure–activity relationship ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Umer Masood Chaudry | Russlan Jaafreh | Abdul Malik | Tea‐Sung Jun | Kotiba Hamad | Tamer Abuhmed
Source: Mathematics (article, 2022-02-27)
Field: Materials Science — Biomaterials

A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation In this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s−1) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluated at elevated temperatures. In addition, a strain-dependent constitutive model based on the Arrhenius equation and machine learning (ML) were constructed to evaluate the stress–strain flow behavior. To build the ML model, experimental data containing temperature, strain, and strain rate were used to train various ML algorithms. The results show that under lower temperatures and higher strain rates, the curves exhibited strain hardening, which is due to the higher activation energy, while when increasing the temperature at a fixed strain rate, the strain hardening decreased and curves were divided into two regimes. In the first regime, a slight increase in strain hardening occurred, while in the second regime, dynamic recrystallization and dynamic recovery controlled the deformation mechanism. Our ML results demonstrate that the ML model outperformed the strain-dependent constitutive model. Dynamic recrystallization ; Materials science ; Strain rate ; Flow stress ; Isothermal process ; Constitutive equation ; Strain hardening exponent ; Deformation (meteorology) ; Arrhenius equation ; Strain (injury) ; Hardening (computing) ; Activation energy ; Composite material ; Plasticity ; Metallurgy ; Hot working ; Alloy ; Thermodynamics ; Finite element method ; Chemistry ; Physics Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Aluminum Alloys Composites Properties
Authors: Vishwesh Venkatraman | P.A. Carvalho
Source: Acta Materialia (article, 2022-09-10)
Field: Materials Science — Materials Chemistry

On the value of popular crystallographic databases for machine learning prediction of space groups Predicting crystal structure information is a challenging problem in materials science that clearly benefits from artificial intelligence approaches. The leading strategies in machine learning are notoriously data-hungry and although a handful of large crystallographic databases are currently available, their predictive quality has never been assessed. In this article, we have employed composition-driven machine learning models, as well as deep learning, to predict space groups from well known experimental and theoretical databases. The results generated by comprehensive testing indicate that data-abundant repositories such as COD (Crystallography Open Database) and OQMD (Open Quantum Materials Database) do not provide the best models even for heavily populated space groups. Classification models trained on databases such as the Pearson Crystal Database and ICSD (Inorganic Crystal Structure Database), and to a lesser extent the Materials Project, generally outperform their data-richer counterparts due to more balanced distributions of the representative classes. Experimental validation with novel high entropy compounds was used to confirm the predictive value of the different databases and showcase the scope of the machine learning approaches employed. Database ; Machine learning ; Artificial intelligence ; Space (punctuation) ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Eleftherios Christofi | Antonis Chazirakis | Charalambos Chrysostomou | Mihalis A. Nicolaou | Wei Li | Manolis Doxastakis | Vagelis Harmandaris
Source: The Journal of Chemical Physics (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

Deep convolutional neural networks for generating atomistic configurations of multi-component macromolecules from coarse-grained models Despite the modern advances in the available computational resources, the length and time scales of the physical systems that can be studied in full atomic detail, via molecular simulations, are still limited. To overcome such limitations, coarse-grained (CG) models have been developed to reduce the dimensionality of the physical system under study. However, to study such systems at the atomic level, it is necessary to re-introduce the atomistic details into the CG description. Such an ill-posed mathematical problem is typically treated via numerical algorithms, which need to balance accuracy, efficiency, and general applicability. Here, we introduce an efficient and versatile method for backmapping multi-component CG macromolecules of arbitrary microstructures. By utilizing deep learning algorithms, we train a convolutional neural network to learn structural correlations between polymer configurations at the atomistic and their corresponding CG descriptions, obtained from atomistic simulations. The trained model is then utilized to get predictions of atomistic structures from input CG configurations. As an illustrative example, we apply the convolutional neural network to polybutadiene copolymers of various microstructures, in which each monomer microstructure (i.e., cis-1,4, trans-1,4, and vinyl-1,2) is represented as a different CG particle type. The proposed methodology is transferable over molecular weight and various microstructures. Moreover, starting from a specific single CG configuration with a given microstructure, we show that by modifying its chemistry (i.e., CG particle types), we are able to obtain a set of well equilibrated polymer configurations of different microstructures (chemistry) than the one of the original CG configuration. Curse of dimensionality ; Component (thermodynamics) ; Microstructure ; Polybutadiene ; Computer science ; Convolutional neural network ; Statistical physics ; Set (abstract data type) ; Molecular dynamics ; Particle (ecology) ; Biological system ; Polymer ; Materials science ; Algorithm ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence ; Thermodynamics ; Copolymer Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Fuel Cells and Related Materials
Authors: Amulya K. Pervaje | Melissa A. Pasquinelli | Saad A. Khan | Erik E. Santiso
Source: Macromolecules (article, 2022-01-19)
Field: Materials Science — Polymers and Plastics

Multiscale Constitutive Modeling of the Mechanical Properties of Polypropylene Fibers from Molecular Simulation Data We present a multiscale approach to create a constitutive model that predicts the mechanical properties of polypropylene fibers based on chemical and physical characteristics. The development of this method relies on validation with experimental stress–strain curves from nine different isotactic polypropylene (iPP) fibers with their varying molecular weight characteristics, Hermans orientation factors, and crystallinity. Complementary molecular models were built by using molecular dynamics (MD) simulations with united atom models. Tensile deformation simulations adapting a quasi-static procedure resulted in stress–strain curves that aligned well with the experimentally measured ones. A neural network model was trained on the MD simulation data to create correlations that predict parameters for a chosen constitutive model that describes the mechanical properties of the polypropylene fibers. This computational approach is amenable to be applied to polymer fiber systems and aims to aid in the design of polymeric materials to achieve targeted mechanical properties. Materials science ; Polypropylene ; Tacticity ; Crystallinity ; Constitutive equation ; Composite material ; Deformation (meteorology) ; Molecular dynamics ; Multiscale modeling ; Ultimate tensile strength ; Fiber ; Polymer ; Stress (linguistics) ; Thermodynamics ; Computational chemistry ; Physics ; Chemistry ; Finite element method Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Rheology and Fluid Dynamics Studies
Authors: Christian Tantardini | Alexander G. Kvashnin | Davide Ceresoli
Source: Materials (article, 2022-05-06)
Field: Chemistry — Spectroscopy

GIPAW Pseudopotentials of d Elements for Solid-State NMR Computational methods are increasingly used to support interpreting, assigning and predicting the solid-state nuclear resonance magnetic spectra of materials. Currently, density functional theory is seen to achieve a good balance between efficiency and accuracy in solid-state chemistry. To be specific, density functional theory allows the assignment of signals in nuclear resonance magnetic spectra to specific sites and can help identify overlapped or missing signals from experimental nuclear resonance magnetic spectra. To avoid the difficulties correlated to all-electron calculations, a gauge including the projected augmented wave method was introduced to calculate nuclear resonance magnetic parameters with great success in organic crystals in the last decades. Thus, we developed a gauge including projected augmented pseudopotentials of 21 <i>d</i> elements and tested them on, respectively, oxides or nitrides (semiconductors), calculating chemical shift and quadrupolar coupling constant. This work can be considered the first step to improving the ab initio prediction of nuclear magnetic resonance parameters, and leaves open the possibility for inorganic compounds to constitute an alternative standard compound, with respect to tetramethylsilane, to calculate the chemical shift. Furthermore, this work represents the possibility to obtain results from first-principles calculations, to train a machine-learning model to solve or refine structures using predicted nuclear magnetic resonance spectra. Density functional theory ; Solid-state nuclear magnetic resonance ; Tetramethylsilane ; Chemical shift ; Chemistry ; Spectral line ; Coupling constant ; Resonance (particle physics) ; NMR spectra database ; Nuclear magnetic resonance ; Computational chemistry ; Condensed matter physics ; Atomic physics ; Physics ; Physical chemistry ; Quantum mechanics Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Crystallography and Radiation Phenomena
Authors: Gulzar Hussain | Fahad Rehman | Ishfaq Ahmed | Yasir Jamil | Hafeez Anwar
Source: Materials Research Bulletin (article, 2022-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis, characterization and machine learning assisted optical emission studies of dysprosium doped bismuth ferrites Bismuth ferrite ; Dysprosium ; Crystallite ; Materials science ; Analytical Chemistry (journal) ; Multiferroics ; Doping ; Bismuth ; Band gap ; Perovskite (structure) ; Grain size ; Crystallography ; Chemistry ; Inorganic chemistry ; Ferroelectricity ; Dielectric ; Metallurgy Multiferroics and related materials ; Magneto-Optical Properties and Applications ; Magnetic Properties and Synthesis of Ferrites
Authors: Yeyong Yu | Xing Wu | Quan Qian
Source: Engineering Applications of Artificial Intelligence (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

Better utilization of materials’ compositions for predicting their properties: Material composition visualization network Computer science ; Visualization ; Amorphous solid ; Composition (language) ; Material properties ; Process (computing) ; Compositional data ; Artificial intelligence ; Data mining ; Materials science ; Machine learning ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metallurgical Processes and Thermodynamics
Authors: Di‐Fan Liu | Qi‐Kun Feng | Yongxin Zhang | Shao‐Long Zhong | Zhi‐Min Dang
Source: Journal of Applied Physics (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Prediction of high-temperature polymer dielectrics using a Bayesian molecular design model Machine learning has shown its great potential in the accelerated discovery of advanced materials in the field of computational molecular design. High-temperature polymer dielectrics are urgently required with the emerging applications of energy-storage dielectric film capacitors under high-temperature conditions. Here, we demonstrate the successful prediction of polymers with a high dielectric constant (ɛ) and high glass transition temperature (Tg) using a Bayesian molecular design model. The model is trained on a joint data set containing 382 computed ɛ values using density functional perturbation theory and experimentally measured Tg values of ∼7000 polymers to build relative quantitative structure–property relationships and identify the promising polymers with specific desired range of dielectric constant and glass transition temperature. From the hypothetical polymer candidates, ten promising polymers are proposed based on their predicted properties and synthetic accessibility score for high-temperature dielectric film capacitors’ application. Moreover, 250k novel polymer structures are generated with the model to support future polymer informatics research. This work contributes to the successful prediction of high-temperature polymer dielectrics using machine learning models. Dielectric ; Polymer ; Materials science ; Glass transition ; Capacitor ; Optoelectronics ; Composite material ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Dielectric materials and actuators