jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Himaghna Bhattacharjee | Nikolaos Anesiadis | Dionisios G. Vlachos
Source: Scientific Reports (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the 'ground truth values' obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement. Thermochemistry ; Density functional theory ; NIST ; Computer science ; Statistical physics ; Graph ; Graph theory ; Molecule ; Thermodynamics ; Computational chemistry ; Chemistry ; Mathematics ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Upendra Yadav | Shashank Pathrudkar | Susanta Ghosh
Source: Physical review. B./Physical review. B (article, 2021-01-11)
Field: Materials Science — Materials Chemistry

Interpretable machine learning model for the deformation of multiwalled carbon nanotubes We present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model that comprises a novel dimensionality reduction technique and a deep neural network-based learning in the reduced dimension. The proposed nonlinear dimensionality reduction technique extends the functional principal component analysis to satisfy the constraint of deformation. Its novelty lies in designing a function space that satisfies the constraint exactly, which is crucial for efficient dimensionality reduction. Owing to the dimensionality reduction and several other strategies adopted in the present paper, learning through deep neural networks is remarkably accurate. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials. Dimensionality reduction ; Interpretability ; Artificial intelligence ; Curse of dimensionality ; Bottleneck ; Artificial neural network ; Computer science ; Nonlinear dimensionality reduction ; Machine learning ; Constraint (computer-aided design) ; Dimension (graph theory) ; Principal component analysis ; Mathematics Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Advanced Electron Microscopy Techniques and Applications
Authors: Jing Dong | Lichao Peng | Xiaohui Yang | Zelin Zhang | Puyu Zhang
Source: Journal of Computational Chemistry (article, 2021-12-04)
Field: Materials Science — Materials Chemistry

<scp>XGBoost‐based</scp>intelligence yield prediction and reaction factors analysis of amination reaction Buchwald-Hartwig amination reaction catalyzed by palladium plays an important role in drug synthesis. In the last few years, machine learning-assisted strategies emerged and quickly gained attention. In this article, an importance and relevance-based integrated feature screening method is proposed to effectively filter high-dimensional feature descriptor data. Then, a regularized machine learning boosting tree model, eXtreme Gradient Boosting, is introduced to intelligently predict reaction performance in multidimensional chemistry space. Furthermore, convergence, interpretability, generalization, and the internal association between reaction conditions and yields are excavated, which provides intelligent assistance for the optimal design of coupling reaction system and evaluating the reaction conditions. Compared with recently published results, the proposed method requires fewer feature descriptors, takes less time, and achieves more accurate prediction accuracy. Interpretability ; Computer science ; Amination ; Artificial intelligence ; Boosting (machine learning) ; Machine learning ; Feature (linguistics) ; Reductive amination ; Chemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: Alexandre Finke | Julien Escobar | Julien Munoz | M.C. Petit
Source: Surface and Coatings Technology (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Prediction of salt spray test results of micro arc oxidation coatings on AA2024 alloys by combination of accelerated electrochemical test and artificial neural network Materials science ; Corrosion ; Electrochemistry ; Coating ; Polarization (electrochemistry) ; Metallurgy ; Artificial neural network ; Sulfuric acid ; Anodizing ; Electrode ; Composite material ; Computer science ; Artificial intelligence ; Chemistry ; Aluminium Corrosion Behavior and Inhibition ; Anodic Oxide Films and Nanostructures ; Concrete Corrosion and Durability
Authors: Andrew E. Sifain | Levi Lystrom | Richard A. Messerly | Justin S. Smith | Benjamin Nebgen | Kipton Barros | Sergei Tretiak | Nicholas Lubbers | Brendan J. Gifford
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Predicting phosphorescence energies and inferring wavefunction localization with machine learning Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on <i>ab initio</i> datasets of singlet-triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, we show that standard ML approaches for modeling potential energy surfaces inaccurately predict singlet-triplet energy gaps due to the failure to account for spatial localities of spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the energy gap, thereby allowing the model to isolate the most determinative chemical environments. Trained on the singlet-triplet energy gaps of organic molecules, we apply our method to an out-of-sample test set of large phosphorescent compounds and demonstrate the substantial improvement that localization layers have on predicting their phosphorescence energies. Remarkably, the inferred localization weights have a strong relationship with the <i>ab initio</i> spin density of the singlet-triplet transition, and thus infer localities of the molecule that determine the spin transition, despite the fact that no direct electronic information was provided during training. The use of localization layers is expected to improve the modeling of many localized, non-extensive phenomena and could be implemented in any atom-centered neural network model. Phosphorescence ; Singlet state ; Wave function ; Energy (signal processing) ; Molecule ; Physics ; Chemical physics ; Molecular physics ; Computer science ; Atomic physics ; Quantum mechanics ; Fluorescence ; Excited state Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electronic and Structural Properties of Oxides
Authors: Ni Zhan | John R. Kitchin
Source: AIChE Journal (article, 2021-11-02)
Field: Materials Science — Materials Chemistry

Uncertainty quantification in machine learning and nonlinear least squares regression models Abstract Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. The multiparameter delta method quantifies uncertainty for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show examples of the uncertainty method in applications of molecular simulations and neural networks. We further show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore, this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability. Hessian matrix ; Extrapolation ; Measure (data warehouse) ; Nonlinear regression ; Computer science ; Nonlinear system ; Artificial neural network ; Least-squares function approximation ; Regression ; Machine learning ; Artificial intelligence ; Reliability (semiconductor) ; Non-linear least squares ; Overfitting ; Regression analysis ; Mathematics ; Data mining ; Applied mathematics ; Statistics ; Explained sum of squares Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Siyue Chen | Sicong Ma | Zhi‐Pan Liu
Source: The Journal of Physical Chemistry Letters (article, 2021-03-26)
Field: Materials Science — Materials Chemistry

Zirconia-Supported ZnO Single Layer for Syngas Conversion Revealed from Machine-Learning Atomic Simulation ZnZrO ternary oxide represents a prominent catalytic system, identified recently for syngas conversion and CO<sub>2</sub> reduction via OX-ZEO technology. One intriguing observation of the ZnZrO catalyst is the very low amount of Zn required for achieving high activity, which challenges the current views on the active site of binary oxide catalysts. Herein, we demonstrate, via machine-learning-based atomic simulation, that the structure evolution of the ZnZrO system in synthesis can be traced from bulk to surface, which leads to the identification of the active site of the ZnZrO catalyst. Theory shows that an unprecedented single-layer Zn-O structure can adhere strongly to the monoclinic ZrO<sub>2</sub> minority (001) surface, forming a stable oxide-on-oxide interface Zn-O/M(001). The single-layer Zn-O can convert syngas to methanol with a high turnover frequency (7.38 s<sup>-1</sup>) from microkinetics simulation. Electron structure analyses reveal that the pentahedron [ZnO<sub>4</sub>] in Zn-O/M(001) enhances the surface electron donation to promote the catalytic activity. Syngas ; Catalysis ; Oxide ; Cubic zirconia ; Materials science ; Ternary operation ; Monoclinic crystal system ; Chemical engineering ; Layer (electronics) ; Methanol ; Inorganic chemistry ; Nanotechnology ; Crystallography ; Chemistry ; Crystal structure ; Metallurgy ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xinyu Lin | Changjiao Li | Hua Hao | Guanghui Zhao | Hanxing Liu
Source: Computational Materials Science (article, 2021-03-10)
Field: Materials Science — Materials Chemistry

Accelerated search for ABO3-type the electronic contribution of polycrystalline dielectric constants by machine learning Dielectric ; Crystallite ; Machine learning ; Materials science ; Artificial intelligence ; Boosting (machine learning) ; Condensed matter physics ; Computer science ; Optoelectronics ; Physics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Mengyao Lu | Shuwen Jiang | Cong Wang | Chen Dong | Tianen Chen
Source: Journal of the ASABE (article, 2021-12-29)
Field: Chemistry — Analytical Chemistry

Tobacco Leaf Grading Based on Deep Convolutional Neural Networks and Machine Vision Highlights A classification model for the front and back sides of tobacco leaves was developed for application in industry. A tobacco leaf grading method that combines a CNN with double-branch integration was proposed. The A-ResNet network was proposed and compared with other classic CNN networks. The grading accuracy of eight different grades was 91.30% and the testing time was 82.180 ms, showing a relatively high classification accuracy and efficiency. Abstract . Flue-cured tobacco leaf grading is a key step in the production and processing of Chinese-style cigarette raw materials, directly affecting cigarette blend and quality stability. At present, manual grading of tobacco leaves is dominant in China, resulting in unsatisfactory grading quality and consuming considerable material and financial resources. In this study, for fast, accurate, and non-destructive tobacco leaf grading, 2,791 flue-cured tobacco leaves of eight different grades in south Anhui Province, China, were chosen as the study sample, and a tobacco leaf grading method that combines convolutional neural networks and double-branch integration was proposed. First, a classification model for the front and back sides of tobacco leaves was trained by transfer learning. Second, two processing methods (equal-scaled resizing and cropping) were used to obtain global images and local patches from the front sides of tobacco leaves. A global image-based tobacco leaf grading model was then developed using the proposed A-ResNet-65 network, and a local patch-based tobacco leaf grading model was developed using the ResNet-34 network. These two networks were compared with classic deep learning networks, such as VGGNet, GoogLeNet-V3, and ResNet. Finally, the grading results of the two grading models were integrated to realize tobacco leaf grading. The tobacco leaf classification accuracy of the final model, for eight different grades, was 91.30%, and grading of a single tobacco leaf required 82.180 ms. The proposed method achieved a relatively high grading accuracy and efficiency. It provides a method for industrial implementation of the tobacco leaf grading and offers a new approach for the quality grading of other agricultural products. Keywords: Convolutional neural network, Deep learning, Image classification, Transfer learning, Tobacco leaf grading Grading (engineering) ; Convolutional neural network ; Tobacco leaf ; Curing of tobacco ; Computer science ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) ; Agricultural engineering ; Horticulture ; Engineering ; Biology Spectroscopy and Chemometric Analyses
Authors: Christian Emeka Okafor | Ezekiel Junior Okafor | Kingsley Okechukwu Ikebudu
Source: Engineering Science and Technology an International Journal (article, 2021-05-07)
Field: Materials Science — Polymers and Plastics

Evaluation of machine learning methods in predicting optimum tensile strength of microwave post-cured composite tailored for weight-sensitive applications The study evaluated the performance of various machine learning methods in predicting tensile strength of microwave post-cured composite tailored for weight-sensitive applications. Using forty six training data pairs from the Box-Behnken design plan, an Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were built to predict the optimum tensile strength of polyurethane wood ash composite. Numerical optimization was done using the graft of ANFIS-MOGA method. The process control factors considered were particle size, curing time, power level, volume fraction and curing angle. The predictive accuracy of the evaluated machine learning methods were assessed using Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Average Error (MAE) and Standard Error of Prediction (SEP). RSM (RMSE = 0.0339, MAE = 0.0002, SEP = 0.0295, R2 = 0.994) and ANFIS (RMSE = 0.0307, MAE = 0.0098, SEP = 0.0267, R2 = 0.995) models gave higher degree of accuracy than ANN model (RMSE = 0.0827, MAE = 0.0124, SEP = 0.0719, R2 = 0.988). The optimization exercise gave optimal tensile strength of 2.27 MPa at optimum process setting of 177µm particle size, 33 min of curing time, power level at 411 Watt, 42% volume fraction and 11° curing angle. Complementary trial at the named optimum process setting conveyed workable results. Furthermore, selecting microwave post-cured composite for weight-sensitive applications was justified considering that the desirability factor for polyurethane (42% wood ash) is fairly higher than other material in the same class, this points to the fact that deployment of the microwave post-cured composite in weigh-sensitive applications could benefit weight reduction. Mean squared error ; Ultimate tensile strength ; Composite number ; Materials science ; Adaptive neuro fuzzy inference system ; Artificial neural network ; Curing (chemistry) ; Composite material ; Coefficient of determination ; Particle swarm optimization ; Machine learning ; Mathematics ; Algorithm ; Computer science ; Fuzzy logic ; Artificial intelligence ; Statistics ; Fuzzy control system Natural Fiber Reinforced Composites ; Epoxy Resin Curing Processes ; Tribology and Wear Analysis
Authors: Tobias Gergs | Borislav Borislavov | Jan Trieschmann
Source: arXiv (Cornell University) (article, 2021-09-03)
Field: Materials Science — Materials Chemistry

An efficient plasma-surface interaction surrogate model for sputtering processes based on autoencoder neural networks Simulations of thin film sputter deposition require the separation of the plasma and material transport in the gas-phase from the growth/sputtering processes at the bounding surfaces. Interface models based on analytic expressions or look-up tables inherently restrict this complex interaction to a bare minimum. A machine learning model has recently been shown to overcome this remedy for Ar ions bombarding a Ti-Al composite target. However, the chosen network structure (i.e., a multilayer perceptron) provides approximately 4 million degrees of freedom, which bears the risk of overfitting the relevant dynamics and complicating the model to an unreliable extend. This work proposes a conceptually more sophisticated but parameterwise simplified regression artificial neural network for an extended scenario, considering a variable instead of a single fixed Ti-Al stoichiometry. A convolutional $\beta$-variational autoencoder is trained to reduce the high-dimensional energy-angular distribution of sputtered particles to a latent space representation of only two components. In addition to a primary decoder which is trained to reconstruct the input energy-angular distribution, a secondary decoder is employed to reconstruct the mean energy of incident Ar ions as well as the present Ti-Al composition. The mutual latent space is hence conditioned on these quantities. The trained primary decoder of the variational autoencoder network is subsequently transferred to a regression network, for which only the mapping to the particular latent space has to be learned. While obtaining a competitive performance, the number of degrees of freedom is drastically reduced to 15,111 and 486 parameters for the primary decoder and the remaining regression network, respectively. The underlying methodology is general and can easily be extended to more complex physical descriptions with a minimal amount of data required. Autoencoder ; Materials science ; Sputtering ; Artificial neural network ; Plasma ; Surface (topology) ; Surrogate model ; Artificial intelligence ; Nanotechnology ; Computer science ; Machine learning ; Physics ; Thin film ; Quantum mechanics Nuclear Materials and Properties ; Nuclear Engineering Thermal-Hydraulics ; Fusion materials and technologies
Authors: Lars Andersen Bratholm | W. Gerrard | Brandon Anderson | Shaojie Bai | Sunghwan Choi | Lam Dang | Pavel Hanchar | Addison Howard | Sanghoon Kim | J. Zico Kolter | Risi Kondor | Mordechai Kornbluth | Youhan Lee | Lee Youngsoo | Jonathan P. Mailoa | Thanh Tu Nguyen | Miloš R. Popović | Goran Rakočević | Walter Reade | Wonho Song | Luka Stojanović | Erik H. Thiede | Nebojša Tijanić | Andres Torrubia | Devin Willmott | Craig P. Butts | David R. Glowacki
Source: PLoS ONE (article, 2021-07-20)
Field: Materials Science — Materials Chemistry

A community-powered search of machine learning strategy space to find NMR property prediction models The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published 'in-house' efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. Computer science ; Pairwise comparison ; Machine learning ; Artificial intelligence ; Predictive modelling ; Data mining ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies