Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Machine Learning for Absorption Cross Sections.

ABSTRACT: We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points.

SUBMITTER: Xue BX

PROVIDER: S-EPMC7511037 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Machine Learning for Absorption Cross Sections.

Xue Bao-Xin BX Barbatti Mario M Dral Pavlo O PO

The journal of physical chemistry. A 20200825 35

We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-b ...[more]

PMID: 32786977

Similar Datasets

Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds.

Project description:Molecular ionization potentials (IP) and photoionization cross sections (σ) can affect the sensitivity of photoionization detectors (PIDs) and other sensors for gaseous species. This study employs several methods of machine learning (ML) to predict IP and σ values at 10.6 eV (117 nm) for a dataset of 1251 gaseous organic species. The explicitness of the treatment of the species electronic structure progressively increases among the methods. The study compares the ML predictions of the IP and σ values to those obtained by quantum chemical calculations. The ML predictions are comparable in performance to those of the quantum calculations when evaluated against measurements. Pretraining further reduces the mean absolute errors (ε) compared to the measurements. The graph-based attentive fingerprint model was most accurate, for which εIP = 0.23 ± 0.01 eV and εσ = 2.8 ± 0.2 Mb compared to measurements and computed cross sections, respectively. The ML predictions for IP correlate well with both the measured IPs (R 2 = 0.88) and with IPs computed at the level of M06-2X/aug-cc-pVTZ (R 2 = 0.82). The ML predictions for σ correlated reasonably well with computed cross sections (R 2 = 0.66). The developed ML methods for IP and σ values, representing the properties of a generalizable set of volatile organic compounds (VOCs) relevant to industrial applications and atmospheric chemistry, can be used to quantitatively describe the species-dependent sensitivity of chemical sensors that use ionizing radiation as part of the sensing mechanism, such as photoionization detectors.

| S-EPMC10152554 | biostudies-literature

Neuroanatomy Learning: Augmented Reality vs. Cross-Sections.

Project description:Neuroanatomy education is a challenging field which could benefit from modern innovations, such as augmented reality (AR) applications. This study investigates the differences on test scores, cognitive load, and motivation after neuroanatomy learning using AR applications or using cross-sections of the brain. Prior to two practical assignments, a pretest (extended matching questions, double-choice questions and a test on cross-sectional anatomy) and a mental rotation test (MRT) were completed. Sex and MRT scores were used to stratify students over the two groups. The two practical assignments were designed to study (1) general brain anatomy and (2) subcortical structures. Subsequently, participants completed a posttest similar to the pretest and a motivational questionnaire. Finally, a focus group interview was conducted to appraise participants' perceptions. Medical and biomedical students (n = 31); 19 males (61.3%) and 12 females (38.7%), mean age 19.2 ± 1.7 years participated in this experiment. Students who worked with cross-sections (n = 16) showed significantly more improvement on test scores than students who worked with GreyMapp-AR (P = 0.035) (n = 15). Further analysis showed that this difference was primarily caused by significant improvement on the cross-sectional questions. Students in the cross-section group, moreover, experienced a significantly higher germane (P = 0.009) and extraneous cognitive load (P = 0.016) than students in the GreyMapp-AR group. No significant differences were found in motivational scores. To conclude, this study suggests that AR applications can play a role in future anatomy education as an add-on educational tool, especially in learning three-dimensional relations of anatomical structures.

| S-EPMC7317366 | biostudies-literature

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites.

Project description:Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8-2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.

| S-EPMC9165597 | biostudies-literature

Ultraviolet Absorption Cross-Sections of Ammonia at Elevated Temperatures for Nonintrusive Quantitative Detection in Combustion Environments.

Project description:Ammonia (NH3) is regarded as an important nitrogen oxides (NOx) precursor and also as an effective reductant for NOx removal in energy utilization through combustion, and it has recently become an attractive non-carbon alternative fuel. To have a better understanding of thermochemical properties of NH3, accurate in situ detection of NH3 in high temperature environments is desirable. Ultraviolet (UV) absorption spectroscopy is a feasible technique. To achieve quantitative measurements, spectrally resolved UV absorption cross-sections of NH3 in hot gas environments at different temperatures from 295 K to 590 K were experimentally measured for the first time. Based on the experimental results, vibrational constants of NH3 were determined and used for the calculation of the absorption cross-section of NH3 at high temperatures above 590 K using the PGOPHER software. The investigated UV spectra covered the range of wavelengths from 190 nm to 230 nm, where spectral structures of the A∼ 1A″2X∼ 1A'1 transition of NH3 in the umbrella bending mode, v2, were recognized. The absorption cross-section was found to decrease at higher temperatures. For example, the absorption cross-section peak of the (6, 0) vibrational band of NH3 decreases from ∼2 × 10-17 to ∼0.5 × 10-17 cm2/molecule with the increase of temperature from 295 K to 1570 K. Using the obtained absorption cross-section, in situ nonintrusive quantification of NH3 in different hot gas environments was achieved with a detection limit varying from below 10 parts per million (ppm) to around 200 ppm as temperature increased from 295 K to 1570 K. The quantitative measurement was applied to an experimental investigation of NH3 combustion process. The concentrations of NH3 and nitric oxide (NO) in the post flame zone of NH3-methane (CH4)-air premixed flames at different equivalence ratios were measured.

| S-EPMC8446901 | biostudies-literature

Measuring the Optical Absorption Cross-sections of Au-Ag Nanocages and Au Nanorods by Photoacoustic Imaging.

Project description:This paper presents a method for measuring the optical absorption cross-sections (σ(a)) of Au-Ag nanocages and Au nanorods. The method is based on photoacoustic (PA) imaging, where the detected signal is directly proportional to the absorption coefficient (μ(a)) of the nanostructure. For each type of nanostructure, we firstly obtained μ(a) from the PA signal by benchmarking against a linear calibration curve (PA signal vs. μ(a)) derived from a set of methylene blue solutions with different concentrations. We then calculated σ(a) by dividing the μ(a) by the corresponding concentration of the Au nanostructure. Additonally, we obtained the extinction cross-section (σ(e), sum of absorption and scattering) from the extinction spectrum recorded using a conventional UV-vis-NIR spectrometer. From the measurements of σ(a) and σ(e), we were able to easily derive both the absorption and scattering cross-sections for each type of gold nanostructure. The ratios of absorption to extinction obtained from experimental and theoretical approaches agreed well, demonstrating the potential use of this method in determining the optical absorption and scattering properties of gold nanostructures and other types of nanomaterials.

| S-EPMC2695596 | biostudies-literature

Two-Photon Absorption Cross-Sections in Fluorescent Proteins Containing Non-canonical Chromophores Using Polarizable QM/MM.

Project description:Multi-photon absorption properties, particularly two-photon absorption (2PA), of fluorescent proteins (FPs) have made them attractive tools in deep-tissue clinical imaging. Although the diversity of photophysical properties for FPs is wide, there are some caveats predominant among the existing FP variants that need to be overcome, such as low quantum yields and small 2PA cross-sections. From a computational perspective, Salem et al. (2016) suggested the inclusion of non-canonical amino acids in the chromophore of the red fluorescent protein DsRed, through the replacement of the tyrosine amino acid. The 2PA properties of these new non-canonical chromophores (nCCs) were determined in vacuum, i.e., without taking into account the protein environment. However, in the computation of response properties, such as 2PA cross-sections, the environment plays an important role. To account for environment and protein-chromophore coupling effects, quantum mechanical/molecular mechanical (QM/MM) schemes can be useful. In this work, the polarizable embedding (PE) model is employed along with time-dependent density functional theory to describe the 2PA properties of a selected set of chromophores made from non-canonical amino acids as they are embedded in the DsRed protein matrix. The objective is to provide insights to determine whether or not the nCCs could be developed and, thus, generate a new class of FPs. Results from this investigation show that within the DsRed environment, the nCC 2PA cross-sections are diminished relative to their values in vacuum. However, further studies toward understanding the 2PA limit of these nCCs using different protein environments are needed.

| S-EPMC7303285 | biostudies-literature

Intraoperative Assessment of Tumor Margins in Tissue Sections with Hyperspectral Imaging and Machine Learning.

Project description:The intraoperative assessment of tumor margins of head and neck cancer is crucial for complete tumor resection and patient outcome. The current standard is to take tumor biopsies during surgery for frozen section analysis by a pathologist after H&E staining. This evaluation is time-consuming, subjective, methodologically limited and underlies a selection bias. Optical methods such as hyperspectral imaging (HSI) are therefore of high interest to overcome these limitations. We aimed to analyze the feasibility and accuracy of an intraoperative HSI assessment on unstained tissue sections taken from seven patients with oral squamous cell carcinoma. Afterwards, the tissue sections were subjected to standard histopathological processing and evaluation. We trained different machine learning models on the HSI data, including a supervised 3D convolutional neural network to perform tumor detection. The results were congruent with the histopathological annotations. Therefore, this approach enables the delineation of tumor margins with artificial HSI-based histopathological information during surgery with high speed and accuracy on par with traditional intraoperative tumor margin assessment (Accuracy: 0.76, Specificity: 0.89, Sensitivity: 0.48). With this, we introduce HSI in combination with ML hyperspectral imaging as a potential new tool for intraoperative tumor margin assessment.

| S-EPMC9818424 | biostudies-literature

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites

Project description:High-throughput IM-MS analysis of drug metabolism incubations prepared from the MicroSource Spectrum Discovery Collection using pooled human liver microsomes and S9 fraction

2021-12-09 | MSV000088549 | MassIVE

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts.

Project description:The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.

| S-EPMC6137445 | biostudies-other

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data