jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Keita Kobayashi | Masahiko Okumura | Hiroki Nakamura | Mitsuhiro Itakura | Masahiko Machida | M. Cooper
Source: Scientific Reports (article, 2022-06-13)
Field: Materials Science — Materials Chemistry

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous [Formula: see text] transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide. Molecular dynamics ; Computer science ; Thorium ; Statistical physics ; Chemistry ; Physics ; Computational chemistry ; Nuclear physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Andrea Martini | Chiara Negri | Luca Bugarin | Gabriele Deplano | Reza K. Abasabadi | Kirill A. Lomachenko | Ton V. W. Janssens | Silvia Bordiga | Gloria Berlier | Elisa Borfecchia
Source: The Journal of Physical Chemistry Letters (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Assessing the Influence of Zeolite Composition on Oxygen-Bridged Diamino Dicopper(II) Complexes in Cu-CHA DeNO<sub><i>x</i></sub> Catalysts by Machine Learning-Assisted X-ray Absorption Spectroscopy Cu-exchanged chabazite is the catalyst of choice for NO<sub><i>x</i></sub> abatement in diesel vehicles aftertreatment systems via ammonia-assisted selective catalytic reduction (NH<sub>3</sub>-SCR). Herein, we exploit <i>in situ</i> X-ray absorption spectroscopy powered by wavelet transform analysis and machine learning-assisted fitting to assess the impact of the zeolite composition on NH<sub>3</sub>-mobilized Cu-complexes formed during the reduction and oxidation half-cycles in NH<sub>3</sub>-SCR at 200 °C. Comparatively analyzing well-characterized Cu-CHA catalysts, we show that the Si/Al ratio of the zeolite host affects the structure of mobile dicopper(II) complexes formed during the oxidation of the [Cu<sup>I</sup>(NH<sub>3</sub>)<sub>2</sub>]<sup>+</sup> complexes by O<sub>2</sub>. Al-rich zeolites promote a planar coordination motif with longer Cu-Cu interatomic distances, while at higher Si/Al values, a bent motif with shorter internuclear separations is also observed. This is paralleled by a more efficient oxidation at a given volumetric Cu density at lower Si/Al, beneficial for the NO<sub><i>x</i></sub> conversion under NH<sub>3</sub>-SCR conditions at 200 °C. Catalysis ; Zeolite ; Selective catalytic reduction ; NOx ; Chemistry ; Chabazite ; Spectroscopy ; Mössbauer spectroscopy ; Inorganic chemistry ; Analytical Chemistry (journal) ; Physical chemistry ; Crystallography ; Organic chemistry Catalytic Processes in Materials Science ; Nanocluster Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Jeffrey Hu | Yuqi Song
Source: Chemical Physics Letters (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

Piezoelectric modulus prediction using machine learning and graph neural networks Piezoelectricity ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Graph ; Feature (linguistics) ; Feature engineering ; Smart material ; Modulus ; Deep learning ; Materials science ; Nanotechnology ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Jiao Yang | Xiaodan Ma | Haiou Guan | Chen Yang | Yifei Zhang | Guibin Li | Zesong Li
Source: Infrared Physics & Technology (article, 2022-12-31)
Field: Chemistry — Analytical Chemistry

A recognition method of corn varieties based on spectral technology and deep learning model Artificial intelligence ; Pattern recognition (psychology) ; Convolutional neural network ; Computer science ; Support vector machine ; Artificial neural network ; Data set ; Fourier transform ; Set (abstract data type) ; Convolution (computer science) ; Partial least squares regression ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Nicholas B. Rego | Andrew L. Ferguson | Amish J. Patel
Source: Proceedings of the National Academy of Sciences (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Learning the relationship between nanoscale chemical patterning and hydrophobicity The hydrophobicity of proteins and similar surfaces, which display chemical heterogeneity at the nanoscale, drives countless aqueous interactions and assemblies. However, predicting how surface chemical patterning influences hydrophobicity remains a challenge. Here, we address this challenge by using molecular simulations and machine learning to characterize and model the hydrophobicity of a diverse library of patterned surfaces, spanning a wide range of sizes, shapes, and chemical compositions. We find that simple models, based only on polar content, are inaccurate, whereas complex neural network models are accurate but challenging to interpret. However, by systematically incorporating chemical correlations between surface groups into our models, we are able to construct a series of minimal models of hydrophobicity, which are both accurate and interpretable. Our models highlight that the number of proximal polar groups is a key determinant of hydrophobicity and that polar neighbors enhance hydrophobicity. Although our minimal models are trained on particular patch size and shape, their interpretability enables us to generalize them to rectangular patches of all shapes and sizes. We also demonstrate how our models can be used to predict hot-spot locations with the largest marginal contributions to hydrophobicity and to design chemical patterns that have a fixed polar content but vary widely in their hydrophobicity. Our data-driven models and the principles they furnish for modulating hydrophobicity could facilitate the design of novel materials and engineered proteins with stronger interactions or enhanced solubilities. Interpretability ; Polar ; Biological system ; Nanoscopic scale ; Contact angle ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Artificial intelligence ; Physics ; Biology Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Theoretical and Computational Physics
Authors: Prabudhya Roy Chowdhury | Xiulin Ruan
Source: npj Computational Materials (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

Unexpected thermal conductivity enhancement in aperiodic superlattices discovered using active machine learning Abstract While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in discovering new physics remains challenging due to its interpolative nature. In this work, we demonstrate a general-purpose adaptive ML-accelerated search process that can discover unexpected lattice thermal conductivity ( κ l ) enhancement in aperiodic superlattices (SLs) as compared to periodic superlattices, with implications for thermal management of multilayer-based electronic devices. We use molecular dynamics simulations for high-fidelity calculations of κ l , along with a convolutional neural network (CNN) which can rapidly predict κ l for a large number of structures. To ensure accurate prediction for the target unknown SLs, we iteratively identify aperiodic SLs with structural features leading to locally enhanced thermal transport and include them as additional training data for the CNN. The identified structures exhibit increased coherent phonon transport owing to the presence of closely spaced interfaces. Aperiodic graph ; Superlattice ; Thermal conductivity ; Convolutional neural network ; Phonon ; Lattice (music) ; Computer science ; Thermal ; Materials science ; Artificial intelligence ; Statistical physics ; Physics ; Condensed matter physics ; Optoelectronics ; Mathematics ; Thermodynamics ; Acoustics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Adem R.N. Aouichaoui | Seyed Soheil Mansouri | Jens Abildskov | Gürkan Sin
Source: AIChE Journal (article, 2022-03-23)
Field: Materials Science — Materials Chemistry

Uncertainty estimation in deep learning‐based property models: Graph neural networks applied to the critical properties Abstract Deep learning and graph‐based models have gained popularity in various life science applications such as property modeling, achieving state‐of‐the‐art performance. However, the quantification of prediction uncertainty in these models is less studied and is not applied in the low dataset size regime, which characterizes many chemical engineering‐related molecular properties. In this work, we apply two graph‐based models to model the critical‐ temperature, pressure, and volume and apply three techniques (the bootstrap, the ensemble, and the dropout) to quantify the prediction uncertainty. The overall model confidence is evaluated using the coverage. The results suggest that graph‐based models perform better compared with current group‐contribution‐based property modeling techniques while eliminating the tedious task of developing molecular descriptors. Property (philosophy) ; Artificial neural network ; Artificial intelligence ; Computer science ; Graph ; Estimation ; Deep learning ; Machine learning ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Andrew S. Lee | Suchismita Sarker | James E. Saal | Logan Ward | Christopher K. H. Borg | Apurva Mehta | Christopher Wolverton
Source: Communications Materials (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

Machine learned synthesizability predictions aided by density functional theory Abstract A grand challenge of materials science is predicting synthesis pathways for novel compounds. Data-driven approaches have made significant progress in predicting a compound’s synthesizability; however, some recent attempts ignore phase stability information. Here, we combine thermodynamic stability calculated using density functional theory with composition-based features to train a machine learning model that predicts a material’s synthesizability. Our model predicts the synthesizability of ternary 1:1:1 compositions in the half-Heusler structure, achieving a cross-validated precision of 0.82 and recall of 0.82. Our model shows improvement in predicting non-half-Heuslers compared to a previous study’s model, and identifies 121 synthesizable candidates out of 4141 unreported ternary compositions. More notably, 39 stable compositions are predicted unsynthesizable while 62 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using density functional theory stability alone. This study presents a new approach for accurately predicting synthesizability, and identifies new half-Heuslers for experimental synthesis. Ternary operation ; Stability (learning theory) ; Density functional theory ; Recall ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Chemistry ; Computational chemistry ; Psychology ; Cognitive psychology Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: François Vasseur | Denis Cornet | Grégory Beurier | Julie Messier | Lauriane Rouan | Justine Bresson | Martin Ecarnot | Mark Stahl | Simon Heumos | Marianne Gérard | Hans Reijnen | Pascal Tillard | Benoı̂t Lacombe | Amélie Emanuel | Justine Floret | Aurélien Estarague | Stefania Przybylska | Kévin Sartori | Lauren Gillespie | Étienne Baron | Elena Kazakou | Denis Vile | Cyrille Violle
Source: Frontiers in Plant Science (article, 2022-05-20)
Field: Chemistry — Analytical Chemistry

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases. Phenomics ; Trait ; Artificial intelligence ; Computer science ; Deep learning ; Perspective (graphical) ; Machine learning ; Ecology ; Diversity (politics) ; Data science ; Biochemical engineering ; Biology ; Genomics ; Engineering ; Sociology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Shiyan Fang | Yanru Zhao | Yan Wang | Junmeng Li | Fengle Zhu | Keqiang Yu
Source: Frontiers in Plant Science (article, 2022-03-04)
Field: Chemistry — Analytical Chemistry

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases. Hyperspectral imaging ; Chemical imaging ; Materials science ; Biological system ; Computer science ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Date Palm Research Studies
Authors: Luis Itza Vazquez-Salazar | Eric D. Boittier | Markus Meuwly
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Uncertainty quantification for predictions of atomistic neural networks The value of uncertainty quantification on predictions for trained neural networks (NNs) on quantum chemical reference data is quantitatively explored. For this, the architecture of the PhysNet NN was suitably modified and the resulting model (PhysNet-DER) was evaluated with different metrics to quantify its calibration, the quality of its predictions, and whether prediction error and the predicted uncertainty can be correlated. Training on the QM9 database and evaluating data in the test set within and outside the distribution indicate that error and uncertainty are not linearly related. However, the observed variance provides insight into the quality of the data used for training. Additionally, the influence of the chemical space covered by the training data set was studied by using a biased database. The results clarify that noise and redundancy complicate property prediction for molecules even in cases for which changes - such as double bond migration in two otherwise identical molecules - are small. The model was also applied to a real database of tautomerization reactions. Analysis of the distance between members in feature space in combination with other parameters shows that redundant information in the training dataset can lead to large variances and small errors whereas the presence of similar but unspecific information returns large errors but small variances. This was, <i>e.g.</i>, observed for nitro-containing aliphatic chains for which predictions were difficult although the training set contained several examples for nitro groups bound to aromatic molecules. The finding underlines the importance of the composition of the training data and provides chemical insight into how this affects the prediction capabilities of a ML model. Finally, the presented method can be used for information-based improvement of chemical databases for target applications through active learning optimization. Chemical space ; Artificial neural network ; Computer science ; Redundancy (engineering) ; Training set ; Set (abstract data type) ; Data mining ; Variance (accounting) ; Test set ; Artificial intelligence ; Biological system ; Machine learning ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Huanbo Weng | Cheng Luo | Huang Yuan
Source: International Journal of Mechanical Sciences (article, 2022-07-23)
Field: Materials Science — Materials Chemistry

ANN-aided evaluation of dual-phase microstructural fabric tensors for continuum plasticity representation Anisotropy ; Materials science ; Isotropy ; Microstructure ; Continuum mechanics ; Plasticity ; Tensor (intrinsic definition) ; Phase (matter) ; Constitutive equation ; Composite material ; Mechanics ; Geometry ; Structural engineering ; Mathematics ; Finite element method ; Physics ; Engineering ; Optics Microstructure and mechanical properties ; Composite Material Mechanics ; Metal Forming Simulation Techniques
Authors: Anandvinod Dalmiya | Manaf Sheyyab | Jai M. Mehta | Kenneth Brezinsky | Patrick T. Lynch
Source: Proceedings of the Combustion Institute (article, 2022-11-11)
Field: Chemistry — Analytical Chemistry

Derived cetane number prediction of jet fuels and their functional group surrogates using liquid phase infrared absorption Cetane number ; Jet fuel ; Ignition system ; Absorption (acoustics) ; Diesel fuel ; Infrared ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Biological system ; Thermodynamics ; Optics ; Organic chemistry ; Physics ; Composite material ; Biodiesel Spectroscopy and Chemometric Analyses ; Advanced Combustion Engine Technologies ; Combustion and flame dynamics