scholarly journals Combining large-scale screening and machine learning to predict the metal-organic frameworks for organosulfurs removal from high-sour natural gas

APL Materials ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 091101 ◽  
Author(s):  
Hong Liang ◽  
Wenyuan Yang ◽  
Feng Peng ◽  
Zili Liu ◽  
Jie Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Gas ◽  
Large Scale ◽  
Metal Organic Frameworks ◽  
Metal Organic ◽  
Large Scale Screening

Large‐Scale Screening and Design of Metal–Organic Frameworks for CH 4 /N 2 Separation

2019 ◽  
Vol 14 (20) ◽  
pp. 3688-3693 ◽  
Author(s):  
Tongan Yan ◽  
Youshi Lan ◽  
Dahuan Liu ◽  
Qingyuan Yang ◽  
Chongli Zhong
Keyword(s):  
Large Scale ◽  
Metal Organic Frameworks ◽  
Metal Organic ◽  
Large Scale Screening

scholarly journals Large-scale screening of hypothetical metal–organic frameworks

2011 ◽  
Vol 4 (2) ◽  
pp. 83-89 ◽  
Author(s):  
Christopher E. Wilmer ◽  
Michael Leaf ◽  
Chang Yeon Lee ◽  
Omar K. Farha ◽  
Brad G. Hauser ◽  
...  
Keyword(s):  
Large Scale ◽  
Metal Organic Frameworks ◽  
Metal Organic ◽  
Large Scale Screening

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

scholarly journals Synthesis of Metal Organic Frameworks by Ball-Milling

Crystals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Cheng-An Tao ◽  
Jian-Fang Wang
Keyword(s):  
Ball Milling ◽  
Large Scale ◽  
Raw Materials ◽  
Metal Organic Frameworks ◽  
Research Topic ◽  
Mass Ratio ◽  
Time Consumption ◽  
Metal Organic ◽  
Short Time ◽  
Made In

Metal-organic frameworks (MOFs) have been used in adsorption, separation, catalysis, sensing, photo/electro/magnetics, and biomedical fields because of their unique periodic pore structure and excellent properties and have become a hot research topic in recent years. Ball milling is a method of small pollution, short time-consumption, and large-scale synthesis of MOFs. In recent years, many important advances have been made. In this paper, the influencing factors of MOFs synthesized by grinding were reviewed systematically from four aspects: auxiliary additives, metal sources, organic linkers, and reaction specific conditions (such as frequency, reaction time, and mass ratio of ball and raw materials). The prospect for the future development of the synthesis of MOFs by grinding was proposed.

scholarly journals Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Matter ◽  
2021 ◽  
Author(s):  
Andrew S. Rosen ◽  
Shaelyn M. Iyer ◽  
Debmalya Ray ◽  
Zhenpeng Yao ◽  
Alán Aspuru-Guzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Chemical ◽  
Metal Organic Frameworks ◽  
Chemical Properties ◽  
Metal Organic

scholarly journals Targeted classification of metal–organic frameworks in the Cambridge structural database (CSD)

2020 ◽  
Vol 11 (32) ◽  
pp. 8373-8387 ◽  
Author(s):  
Peyman Z. Moghadam ◽  
Aurelia Li ◽  
Xiao-Wei Liu ◽  
Rocio Bueno-Perez ◽  
Shu-Dong Wang ◽  
...  
Keyword(s):  
Surface Chemistry ◽  
Specific Surface ◽  
Large Scale ◽  
Metal Cluster ◽  
Metal Organic Frameworks ◽  
Cambridge Structural Database ◽  
Metal Organic ◽  
Structural Database

Large-scale targeted exploration of metal–organic frameworks (MOFs) with characteristics such as specific surface chemistry or metal-cluster family has not been investigated so far.

Mechanochemical synthesis of three-component metal-organic frameworks for large scale production

2021 ◽  
Vol 303 ◽  
pp. 122547
Author(s):  
Tong Gao ◽  
Hui-Juan Tang ◽  
Shu-Yi Zhang ◽  
Jian-Wei Cao ◽  
Yi-Nong Wu ◽  
...  
Keyword(s):  
Mechanochemical Synthesis ◽  
Large Scale ◽  
Metal Organic Frameworks ◽  
Scale Production ◽  
Large Scale Production ◽  
Metal Organic
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