jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tong Gao | Cameron J. Shock | Mark J. Stevens | Amalie L. Frischknecht | Issei Nakamura
Source: MRS Communications (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Surrogate molecular dynamics simulation model for dielectric constants with ensemble neural networks Normalization (sociology) ; Molecular dynamics ; Dielectric ; Artificial neural network ; Materials science ; Polar ; Statistical physics ; Biological system ; Experimental data ; Artificial intelligence ; Computational chemistry ; Computer science ; Statistics ; Mathematics ; Physics ; Chemistry Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Model Reduction and Neural Networks
Authors: Arsenios Gkourras | Leonidas N. Gergidis
Source: The Journal of Physical Chemistry B (article, 2022-07-18)
Field: Chemistry — Inorganic Chemistry

Molecular and Artificial Neural Networks Modeling of Sorption and Diffusion of Small Alkanes, Alkenes and Their Ternary Mixtures in ZIF-8 at Different Temperatures Numerical computations comprising of Grand Canonical-Monte Carlo (GCMC) and Canonical Statistical Ensemble (NVT) Molecular Dynamics (MD) simulations were used to study the diffusion and sorption characteristics primarily for methane, ethane, and ethene molecules and for their ternary mixtures at different temperatures in ZIF-8 porous material. Methane as pure component or in mixture proved to be the sorbed hydrocarbon with the higher molecular mobility at the temperature range of 273-373 K among alkanes and alkenes. In addtion, alkenes were the hydrocarbons with the higher self-diffusion coefficients compared to the respective alkanes. In the ternary mixtures ethane was preferentially sorbed in ZIF-8 at all temperatures studied. Direct comparisons of the self-diffusivity data obtained from the NVT-MD simulations with recently reported Magic Angle Spinning Pulsed Field Gradient Nuclear Magnetic Resonance (MAS PFG NMR) measurements showed reasonable agreement. Furthermore, the NVT-MD self-diffusivity coefficients in conjunction with the aforementioned MAS PFG NMR experimental measurements, and sorption thermodynamic data obtained from the present GCMC simulations were utilized for the development of individual Artificial Neural Networks (ANNs) predictive modeling procedures in order to provide additional quantitative and qualitative information regarding the diffusion and sorption of small alkanes, alkenes in ZIF-8. The ANNs predictions were in good agreement with the experimental measurements and with the molecular simulation data. The modeling and analysis capabilities of ANNs along with their fast computations using moderate computer resources can significantly assist the irreplaceable molecular simulation and experimental approaches to cope with complicated problems at the molecular level. Ternary operation ; Molecular dynamics ; Sorption ; Thermal diffusivity ; Chemistry ; Diffusion ; Thermodynamics ; Pulsed field gradient ; Methane ; Alkane ; Molecule ; Hydrocarbon ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Adsorption ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis ; NMR spectroscopy and applications
Authors: Florent Purnode | François Henrotte | Francois Caire | Joaquim da Silva | Gilles Louppe | Christophe Geuzaine
Source: IEEE Transactions on Magnetics (article, 2022-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime Electromagnetic fields and eddy currents in thin electrical steel laminations are governed by the laws of magnetodynamics with hysteresis. If the lamination is large with respect to its thickness, field and current distributions are accurately resolved by solving a 1-D finite element magnetodynamic problem with hysteresis across half the lamination thickness. This 1-D model is able to deliver mesoscopic information to be used, after appropriate homogenization, in the macroscopic modeling of an electrical machine or transformer. As each evaluation of such a homogenized model implies a finite element simulation at the mesoscale, a monolithic coupling might be very time-consuming. This article proposes an alternative approach, assuming a periodic excitation of the system, where the parameters of a parametric homogenized material law are determined in each finite element with a neural network. The local material law can then be used as a conventional constitutive relationship in a 2-D or 3-D modeling, with a massive speed-up with respect to the monolithic coupling. Homogenization (climate) ; Finite element method ; Eddy current ; Mesoscopic physics ; Mechanics ; Lamination ; Parametric statistics ; Materials science ; Magnetic hysteresis ; Transformer ; Magnetic field ; Constitutive equation ; Magnetization ; Physics ; Condensed matter physics ; Voltage ; Mathematics ; Composite material Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Magnetic properties of thin films
Authors: Diego A. Delgadillo-Duran | Cesar A. Vargas-García | Viviana Marcela Varón‐Ramírez | Francisco Calderón | Andrea C. Montenegro | Paula H. Reyes‐Herrera
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Vis-NIR Spectroscopy and Machine Learning Methods to Diagnose Chemical Properties in Colombian Sugarcane Soils Soil water ; Spectroscopy ; Near-infrared spectroscopy ; Machine learning ; Chemistry ; Agroforestry ; Environmental science ; Computer science ; Soil science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Mineral Processing and Grinding
Authors: Sayanjit Singha Roy | Ashish Paramane | Jiwanjot Singh | Soumya Chatterjee
Source: 2022 IEEE Power &amp; Energy Society General Meeting (PESGM) (article, 2022-07-17)
Field: Materials Science — Materials Chemistry

Accurate Hydrophobicity Grade Detection of Polymeric Insulators in Extremely Wetted and Humid Environments Using Bi-LSTM Neural Network Classifier An accurate detection of hydrophobicity grade (HG) is essential for reliable condition monitoring of polymeric outdoor insulators in wetted and humid environments and for increasing their service life as well. With the above context, this paper proposes a novel HG detection methodology by incorporating a local binary pattern texture feature infused with bi-directional long short-term memory (bi-LSTM) neural network classifier. Different experiments were carried out on 11 kV silicone rubber (SiR) polymeric insulators to emulate various hydrophobic conditions, and the images of the water droplets on the insulator surface were captured. After that, texture features were extracted from the images using a suitable pre-processing of the acquired images and the LBP technique. The extracted features were subsequently fed to a bi-LSTM classifier for HG classification, which returned high recognition accuracies in classifying different hydrophobicity grades. The proposed HG detection technique is suitable and can be implemented for remote condition monitoring purposes. Artificial intelligence ; Classifier (UML) ; Artificial neural network ; Pattern recognition (psychology) ; Computer science ; Silicone rubber ; Feature extraction ; Local binary patterns ; Materials science ; Computer vision ; Composite material ; Histogram ; Image (mathematics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Image Enhancement Techniques
Authors: Dimitris Nasikas | Eleonora Ricci | George Giannakopoulos | Vangelis Karkaletsis | Doros N. Theodorou | Niki Vergadou
Source:  (preprint, 2022-09-07)
Field: Materials Science — Materials Chemistry

Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules Due to the wide range of timescales that are present in macromolecular systems, hierarchical multiscale strategies are necessary for their computational study. Coarse-graining (CG) allows to establish a link between different system resolutions and provides the backbone for the development of robust multiscale simulations and analyses. The CG mapping process is typically system- and application-specific, and it relies on chemical intuition. In this work, we explored the application of a Machine Learning strategy, based on Variational Autoencoders, for the development of suitable mapping schemes from the atomistic to the coarse-grained space of molecules with increasing chemical complexity. An extensive evaluation of the effect of the model hyperparameters on the training process and on the final output was performed, and an existing method was extended with the definition of different loss functions and the implementation of a selection criterion that ensures physical consistency of the output. The relationship between the input feature choice and the reconstruction accuracy was analyzed, supporting the need to introduce rotational invariance into the system. Strengths and limitations of the approach, both in the mapping and in the backmapping steps, are highlighted and critically discussed. Computer science ; Hyperparameter ; Granularity ; Intuition ; Artificial intelligence ; Consistency (knowledge bases) ; Machine learning ; Process (computing) ; Range (aeronautics) ; Algorithm ; Theoretical computer science Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: Xiang Li | Zhe Li | Ji Chen
Source: Research Square (preprint, 2022-05-31)
Field: Chemistry — Physical and Theoretical Chemistry

Ab initio calculation of real solids via neural network ansatz Abstract Neural networks have been applied to tackle many-body electron correlations for small molecules and physical models in recent years. Here we propose a new architecture that extends molecular neural networks with the inclusion of periodic boundary conditions to enable ab initio calculation of real solids. The accuracy of our approach is demonstrated in four different types of systems, namely the one-dimensional periodic hydrogen chain, the two-dimensional graphene, the three-dimensional lithium hydride crystal, and the homogeneous electron gas, where the obtained results, e.g. total energies, dissociation curves, and cohesive energies, outperform many traditional ab initio methods and reach the level of the most ac- curate approaches. Moreover, electron densities of typical systems are also calculated to provide physical intuition of various solids. Our method of extending a molecular neural network to periodic systems can be easily integrated into other neural network structures, highlighting a promising future of ab initio solution of more complex solid systems using neural net- work ansatz, and more generally endorsing the application of machine learning in materials simulation and condensed matter physics. Ansatz ; Artificial neural network ; Ab initio ; Ab initio quantum chemistry methods ; Statistical physics ; Physics ; Computer science ; Computational chemistry ; Artificial intelligence ; Mathematical physics ; Chemistry ; Quantum mechanics Advanced Physical and Chemical Molecular Interactions ; X-ray Diffraction in Crystallography ; Zeolite Catalysis and Synthesis
Authors: Henrik Levämäki | Florian Bock | Davide G. Sangiovanni | L.J.S. Johnson | Ferenc Tasnádi | Rickard Armiento | Igor A. Abrikosov
Source: Thin Solid Films (article, 2022-12-14)
Field: Materials Science — Materials Chemistry

HADB: A materials-property database for hard-coating alloys Data-driven approaches are becoming increasingly valuable for modern science, and they are making their way into industrial research and development (R&D). Supervised machine learning of statistical models can utilize databases of materials parameters to speed up the exploration of candidate materials for experimental synthesis and characterization. In this paper we introduce the HADB database, which contains properties of industrially relevant chemically disordered hard-coating alloys, focusing on their thermodynamic, elastic and mechanical properties. We present the technical implementations of the database infrastructure including support for browse, query, retrieval, and API access through the OPTIMADE API to make this data findable, accessible, interoperable, and reusable (FAIR). Finally, we demonstrate the usefulness of the database by training a graph-based machine learning (ML) model to predict elastic properties of hard-coating alloys. The ML model is shown to predict bulk and shear moduli for out out-of-sample alloys with less than 6 GPa mean average error. Interoperability ; Computer science ; Coating ; Moduli ; Database ; Property (philosophy) ; Graph ; Materials science ; Nanotechnology ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Fuel Cells and Related Materials
Authors: Hyunwook Jung | Lena Sauerland | Sina Stocker | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2022-09-23)
Field: Materials Science — Materials Chemistry

Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of possible binding motifs leads to complex potential energy surfaces (PES), however. This implies that finding the optimal structure is a difficult global optimization problem, which leads to significant uncertainty about the stability of many intermediates. To tackle this issue, we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential on-the-fly. The approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the algorithm. We demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111) and (211) surfaces. Surface (topology) ; Adsorption ; Global optimization ; Potential energy surface ; Stability (learning theory) ; Computer science ; Set (abstract data type) ; Catalysis ; Materials science ; Chemistry ; Chemical physics ; Molecule ; Mathematics ; Algorithm ; Machine learning ; Geometry ; Physical chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Topic Modeling
Authors: Frank Hu | Hanqing He | David Yaron
Source: arXiv (Cornell University) (preprint, 2022-10-21)
Field: Materials Science — Materials Chemistry

Semiempirical Hamiltonians learned from data can have accuracy comparable to Density Functional Theory Quantum chemistry provides chemists with invaluable information, but the high computational cost limits the size and type of systems that can be studied. Machine learning (ML) has emerged as a means to dramatically lower cost while maintaining high accuracy. However, ML models often sacrifice interpretability by using components, such as the artificial neural networks of deep learning, that function as black boxes. These components impart the flexibility needed to learn from large volumes of data but make it difficult to gain insight into the physical or chemical basis for the predictions. Here, we demonstrate that semiempirical quantum chemical (SEQC) models can learn from large volumes of data without sacrificing interpretability. The SEQC model is that of Density Functional based Tight Binding (DFTB) with fixed atomic orbital energies and interactions that are one-dimensional functions of interatomic distance. This model is trained to ab initio data in a manner that is analogous to that used to train deep learning models. Using benchmarks that reflect the accuracy of the training data, we show that the resulting model maintains a physically reasonable functional form while achieving an accuracy, relative to coupled cluster energies with a complete basis set extrapolation (CCSD(T)*/CBS), that is comparable to that of density functional theory (DFT). This suggests that trained SEQC models can achieve low computational cost and high accuracy without sacrificing interpretability. Use of a physically-motivated model form also substantially reduces the amount of ab initio data needed to train the model compared to that required for deep learning models. Interpretability ; Extrapolation ; Computer science ; Density functional theory ; Ab initio ; Flexibility (engineering) ; Artificial intelligence ; Deep learning ; Artificial neural network ; Basis (linear algebra) ; Function (biology) ; Machine learning ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Ismaeel Ramzan | Jas Kalayan | Linghan Kong | Richard A. Bryce | Neil A. Burton
Source: International Journal of Quantum Chemistry (article, 2022-08-04)
Field: Materials Science — Materials Chemistry

Machine learning of atomic forces from quantum mechanics: An approach based on pairwise interatomic forces Abstract In this work, we explore a machine learning approach based on pairwise interatomic forces to directly predict atomic forces in a molecule to quantum chemical accuracy. The scheme, denoted pairF‐Net, uses a neural network designed and trained to predict Cartesian forces as a linear combination of a set of force components in an interatomic basis; these pairwise forces exhibit chemically intuitive profiles and implicitly maintain rotational and translational invariance. Application of the approach to model small molecule systems indicates an accuracy in reconstructed Cartesian atomic forces of ~1 kcal mol −1 Å −1 from the reference force values obtained via density functional theory. The pairF‐Net scheme predicts atomic forces at a quantum mechanical level but at a fraction of the cost, enabling molecular dynamics simulations via a chemically intuitive machine learning model. Pairwise comparison ; Cartesian coordinate system ; Work (physics) ; Force field (fiction) ; Central force ; Quantum ; Density functional theory ; Molecule ; Statistical physics ; Molecular dynamics ; Classical mechanics ; Chemistry ; Physics ; Quantum mechanics ; Computer science ; Computational chemistry ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hao Xu | Jinglong Lin | Dongxiao Zhang | Fanyang Mo
Source: arXiv (Cornell University) (preprint, 2022-11-07)
Field: Chemistry — Spectroscopy

Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network A new research framework is proposed to incorporate machine learning techniques into the field of experimental chemistry to facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times (CMRT dataset) in high-performance liquid chromatography is established to handle the challenge of data acquisition. Based on the CMRT dataset, a quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. Experiments confirm that the proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery. Retention time ; Artificial neural network ; Quantile ; Computer science ; Artificial intelligence ; Enantiomer ; Machine learning ; Chromatography ; Chemistry ; Mathematics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Gary Tom | Riley J. Hickman | Aniket Zinzuwadia | Afshan Mohajeri | Benjamín Sánchez-Lengeling | Alán Aspuru‐Guzik
Source: arXiv (Cornell University) (preprint, 2022-12-03)
Field: Materials Science — Materials Chemistry

Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties. When the datasets are smaller (&lt; 2000 molecules), it is not clear that deep learning approaches are the right modelling tool. In this work we perform an extensive study of the calibration and generalizability of probabilistic machine learning models on small chemical datasets. Using different molecular representations and models, we analyse the quality of their predictions and uncertainties in a variety of tasks (binary, regression) and datasets. We also introduce two simulated experiments that evaluate their performance: (1) Bayesian optimization guided molecular design, (2) inference on out-of-distribution data via ablated cluster splits. We offer practical insights into model and feature choice for modelling small chemical datasets, a common scenario in new chemical experiments. We have packaged our analysis into the DIONYSUS repository, which is open sourced to aid in reproducibility and extension to new datasets. Generalizability theory ; Computer science ; Leverage (statistics) ; Machine learning ; Artificial intelligence ; Probabilistic logic ; Inference ; Calibration ; Data mining ; Bayesian inference ; Deep learning ; Bayesian probability ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Gyoung S. Na | Chanyoung Park
Source: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

Nonlinearity Encoding for Extrapolation of Neural Networks Extrapolation to predict unseen data outside the training distribution is a common challenge in real-world scientific applications of physics and chemistry. However, the extrapolation capabilities of neural networks have not been extensively studied in machine learning. Although it has been recently revealed that neural networks become linear regression in extrapolation problems, a universally applicable method to support the extrapolation of neural networks in general regression settings has not been investigated. In this paper, we propose automated nonlinearity encoder (ANE) that is a data-agnostic embedding method to improve the extrapolation capabilities of neural networks by conversely linearizing the original input-to-target relationships without architectural modifications of prediction models. ANE achieved state-of-the-art extrapolation accuracies in extensive scientific applications of various data formats. As a real-world application, we applied ANE for high-throughput screening to discover novel solar cell materials, and ANE significantly improved the screening accuracy. Extrapolation ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Encoding (memory) ; Regression ; Autoencoder ; Embedding ; Nonlinear system ; Encoder ; Nonlinear regression ; Linear regression ; Data mining ; Regression analysis ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM