scholarly journals Lumping of atmospheric organic chemical species by machine learning.

2006 ◽  
Author(s):  
Pruthvi Polam
Keyword(s):  
Machine Learning ◽  
Chemical Species ◽  
Organic Chemical

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

scholarly journals Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

2021 ◽  
Author(s):  
Jeremy Feinstein ◽  
ganesh sivaraman ◽  
Kurt Picel ◽  
Brian Peters ◽  
Alvaro Vazquez-Mayagoitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Organic Chemical ◽  
Learning Method ◽  
Toxicity Data ◽  
Chemical Structures ◽  
Machine Learning Methods ◽  
Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

Supplementary material to "Modeling the partitioning of organic chemical species in cloud phases with CLEPS (1.1)"

2017 ◽  
Author(s):  
Clémence Rose ◽  
Nadine Chaumerliac ◽  
Laurent Deguillaume ◽  
Hélène Perroux ◽  
Camille Mouchel-Vallon ◽  
...  
Keyword(s):  
Chemical Species ◽  
Organic Chemical ◽  
Supplementary Material

scholarly journals Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

2021 ◽  
Author(s):  
Jeremy Feinstein ◽  
ganesh sivaraman ◽  
Kurt Picel ◽  
Brian Peters ◽  
Alvaro Vazquez-Mayagoitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Organic Chemical ◽  
Learning Method ◽  
Toxicity Data ◽  
Chemical Structures ◽  
Machine Learning Methods ◽  
Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

scholarly journals Modeling the partitioning of organic chemical species in cloud phases with CLEPS (1.1)

2017 ◽  
Author(s):  
Clémence Rose ◽  
Nadine Chaumerliac ◽  
Laurent Deguillaume ◽  
Hélène Perroux ◽  
Camille Mouchel-Vallon ◽  
...  
Keyword(s):  
Gas Phase ◽  
Carboxylic Acids ◽  
Aqueous Phase ◽  
Chemical Reactivity ◽  
Chemical Species ◽  
Coupled Model ◽  
Water Soluble ◽  
Organic Chemical ◽  
Particulate Phase ◽  
Particle Dissolution

Abstract. The new detailed aqueous phase mechanism Cloud Explicit Physico-chemical Scheme (CLEPS 1.0), which describes the oxidation of isoprene-derived water-soluble organic compounds, is coupled with a warm microphysical module simulating the activation of aerosol particles into cloud droplets. CLEPS 1.0 was then extended to CLEPS 1.1 to include the chemistry of the newly added di-carboxylic acids dissolved from the particulate phase. The resulting coupled model allows for predicting the aqueous phase concentrations of chemical compounds originating from particle dissolution, mass transfer from the gas phase and in-cloud aqueous chemical reactivity. The aim of the present study was more particularly to investigate the effect of particle dissolution on cloud chemistry. Several simulations were performed to assess the influence of various parameters on model predictions and to interpret long-term measurements conducted at the top of the puy de Dôme (PUY, France) in marine air masses. Specific attention was paid to carboxylic acids, whose predicted concentrations are on average in the lower range of the observations, with the exception of formic acid, which is rather overestimated in the model. The different sensitivity runs highlight the fact that formic and acetic acids mainly originate from the gas phase and have highly variable aqueous-phase reactivity depending on the cloud acidity, whereas C3–C4 carboxylic acids mainly originate from the particulate phase and are supersaturated in the cloud.

scholarly journals Spotting Frozen Curd in PDO Buffalo Mozzarella Cheese Through Insights on Its Supramolecular Structure Acquired by 1H TD-NMR Relaxation Experiments

2021 ◽  
Vol 11 (4) ◽  
pp. 1466
Author(s):  
Carlo Mengucci ◽  
Davide Rabiti ◽  
Eleonora Urbinati ◽  
Gianfranco Picone ◽  
Raffaele Romano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supramolecular Structure ◽  
Chemical Species ◽  
Nmr Relaxation ◽  
Exponential Fitting ◽  
Protected Designation Of Origin ◽  
The Matrix ◽  
Relaxation Experiments ◽  
Latent Structures ◽  
Set Up

The addition of frozen curd (FC) during the production process of “Mozzarella di Bufala Campana”, an Italian cheese with Protected Designation of Origin (PDO), is a common fraud not involving modifications of the chemical composition in the final product. Its detection cannot thus be easily obtained by common analytical methods, which are targeted at changes in concentrations of diagnostic chemical species. In this work, the possibility of spotting this fraud by focusing on the modifications of the supramolecular structure of the food matrix, detected by time domain nuclear magnetic resonance (TD-NMR) experiments, was investigated. Cheese samples were manufactured in triplicate, according to the PDO disciplinary of production, except for using variable amounts of FC (i.e., 0, 15, 30, and 50% w/w). Relaxation data were analysed through different approaches: (i) Discrete multi-exponential fitting, (ii) continuous Laplace inverse fitting, and (iii) chemometrics approach. The strategy that lead to best detection results was the chemometrics analysis of raw Carr-Purcell-Meiboom-Gill (CPMG) decays, allowing to discriminate between compliant and adulterated samples, with as low as 15% of FC addition. The strategy is based on the use of machine learning for projection on latent structures of raw CPMG data and classification tasks for fraud detection, using quadratic discriminant analysis. By coupling TD-NMR raw decays with machine learning, this work opens the way to set up a system for detecting common food frauds modifying the matrix structure, for which no official authentication methods are yet available.

scholarly journals Modeling the partitioning of organic chemical species in cloud phases with CLEPS (1.1)

2018 ◽  
Vol 18 (3) ◽  
pp. 2225-2242 ◽  
Author(s):  
Clémence Rose ◽  
Nadine Chaumerliac ◽  
Laurent Deguillaume ◽  
Hélène Perroux ◽  
Camille Mouchel-Vallon ◽  
...  
Keyword(s):  
Gas Phase ◽  
Carboxylic Acids ◽  
Aqueous Phase ◽  
Chemical Reactivity ◽  
Chemical Species ◽  
Coupled Model ◽  
Water Soluble ◽  
Organic Chemical ◽  
Particulate Phase ◽  
Particle Scavenging

Abstract. The new detailed aqueous-phase mechanism Cloud Explicit Physico-chemical Scheme (CLEPS 1.0), which describes the oxidation of isoprene-derived water-soluble organic compounds, is coupled with a warm microphysical module simulating the activation of aerosol particles into cloud droplets. CLEPS 1.0 was then extended to CLEPS 1.1 to include the chemistry of the newly added dicarboxylic acids dissolved from the particulate phase. The resulting coupled model allows the prediction of the aqueous-phase concentrations of chemical compounds originating from particle scavenging, mass transfer from the gas-phase and in-cloud aqueous chemical reactivity. The aim of the present study was more particularly to investigate the effect of particle scavenging on cloud chemistry. Several simulations were performed to assess the influence of various parameters on model predictions and to interpret long-term measurements conducted at the top of Puy de Dôme (PUY, France) in marine air masses. Specific attention was paid to carboxylic acids, whose predicted concentrations are on average in the lower range of the observations, with the exception of formic acid, which is rather overestimated in the model. The different sensitivity runs highlight the fact that formic and acetic acids mainly originate from the gas phase and have highly variable aqueous-phase reactivity depending on the cloud acidity, whereas C3–C4 carboxylic acids mainly originate from the particulate phase and are supersaturated in the cloud.

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