8,802 research outputs found
Alchemical and structural distribution based representation for improved QML
We introduce a representation of any atom in any chemical environment for the
generation of efficient quantum machine learning (QML) models of common
electronic ground-state properties. The representation is based on scaled
distribution functions explicitly accounting for elemental and structural
degrees of freedom. Resulting QML models afford very favorable learning curves
for properties of out-of-sample systems including organic molecules,
non-covalently bonded protein side-chains, (HO)-clusters, as well as
diverse crystals. The elemental components help to lower the learning curves,
and, through interpolation across the periodic table, even enable "alchemical
extrapolation" to covalent bonding between elements not part of training, as
evinced for single, double, and triple bonds among main-group elements
Alchemical and structural distribution based representation for improved QML
We introduce a representation of any atom in any chemical environment for the
generation of efficient quantum machine learning (QML) models of common
electronic ground-state properties. The representation is based on scaled
distribution functions explicitly accounting for elemental and structural
degrees of freedom. Resulting QML models afford very favorable learning curves
for properties of out-of-sample systems including organic molecules,
non-covalently bonded protein side-chains, (HO)-clusters, as well as
diverse crystals. The elemental components help to lower the learning curves,
and, through interpolation across the periodic table, even enable "alchemical
extrapolation" to covalent bonding between elements not part of training, as
evinced for single, double, and triple bonds among main-group elements
MODNet -- accurate and interpretable property predictions for limited materials datasets by feature selection and joint-learning
In order to make accurate predictions of material properties, current
machine-learning approaches generally require large amounts of data, which are
often not available in practice. In this work, an all-round framework is
presented which relies on a feedforward neural network, the selection of
physically-meaningful features and, when applicable, joint-learning. Next to
being faster in terms of training time, this approach is shown to outperform
current graph-network models on small datasets. In particular, the vibrational
entropy at 305 K of crystals is predicted with a mean absolute test error of
0.009 meV/K/atom (four times lower than previous studies). Furthermore,
joint-learning reduces the test error compared to single-target learning and
enables the prediction of multiple properties at once, such as temperature
functions. Finally, the selection algorithm highlights the most important
features and thus helps understanding the underlying physics.Comment: 5 pages, 2 figure
Integrating remote sensing datasets into ecological modelling: a Bayesian approach
Process-based models have been used to simulate 3-dimensional complexities of
forest ecosystems and their temporal changes, but their extensive data
requirement and complex parameterisation have often limited their use for
practical management applications. Increasingly, information retrieved using
remote sensing techniques can help in model parameterisation and data
collection by providing spatially and temporally resolved forest information. In
this paper, we illustrate the potential of Bayesian calibration for integrating such
data sources to simulate forest production. As an example, we use the 3-PG
model combined with hyperspectral, LiDAR, SAR and field-based data to
simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and
SAR data are used to estimate LAI dynamics, tree height and above ground
biomass, respectively, while the Bayesian calibration provides estimates of
uncertainties to model parameters and outputs. The Bayesian calibration
contrasts with goodness-of-fit approaches, which do not provide uncertainties
to parameters and model outputs. Parameters and the data used in the
calibration process are presented in the form of probability distributions,
reflecting our degree of certainty about them. After the calibration, the
distributions are updated. To approximate posterior distributions (of outputs
and parameters), a Markov Chain Monte Carlo sampling approach is used (25
000 steps). A sensitivity analysis is also conducted between parameters and
outputs. Overall, the results illustrate the potential of a Bayesian framework for
truly integrative work, both in the consideration of field-based and remotely
sensed datasets available and in estimating parameter and model output uncertainties
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Singlet Oxygen Generation by Porphyrins and Metalloporphyrins Revisited: a Quantitative Structure-property Relationship (QSPR) Study
state followed by formation of singlet oxygen (1O2), which is a highly reactive species and mediates various oxidative processes. The design of advanced sensitizers based on porphyrin compounds have attracted significant attention in recent years. However, it is still difficult to predict the efficiency of singlet oxygen generation for a given structure. Our goal was to develop a quantitative structure-property relationship (QSPR) model for the fast virtual screening and prediction of singlet oxygen quantum yields for pophyrins and metalloporphyrins. We performed QSPR analysis of a dataset containing 32 compounds, including various porphyrins and their analogues (chlorins and bacteriochlorins). Quantum-chemical descriptors were calculated using Density Functional Theory (DFT), namely B3LYP and M062X functionals. Three different machine learning methods were used to develop QSPR models: random forest regression (RFR), support vector regression (SVR), and multiple linear regression (MLR). The optimal QSPR model «structure – singlet oxygen generation quantum yield» obtained using RFR method demonstrated high determination coefficient for the training set (R2 = 0.949) and the highest predicting ability for the test set (pred_R2 = 0.875). This proves that the developed QSPR method is realiable and can be directly applied in the studies of singlet oxygen generation both for free base porphyrins and their metal complexes. We believe that QSPR approach developed in this study can be useful for the search of new poprhyrin photosensitizers with enhanced singlet oxygen generation ability
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