scholarly journals Optimized sampling approach for intelligent searching molecular simulation space

2020 ◽  
Author(s):  
Sarah Lotfikatouli ◽  
Leila Lotfikatooli
Keyword(s):  
Molecular Simulation ◽  
Intelligent Searching ◽  
Sampling Approach

KEEFEKTIFAN SEKOLAHDILIHAT DARI DELAPAN STANDART NASIONAL PENDIDIKAN DI WILAYAH EKS KARESIDENAN SURAKARTA

2018 ◽  
Vol 2 (2) ◽  
pp. 199
Author(s):  
Parwanto
Keyword(s):  
School Effectiveness ◽  
Junior High Schools ◽  
Survey Method ◽  
Content Standards ◽  
Educational Standards ◽  
Probability Sampling ◽  
National Education ◽  
Management Standards ◽  
High Level ◽  
Sampling Approach

Abstrak:Penelitian ini bertujuan untuk mengetahui gambaran keefektifan sekolah dilihat dari delapan standar nasional pendidikan. mengetahui tingkat ketecapaian keefektifan sekolahdilihat dari delapan standar nasional pendidikan dan mengetahui dari kedelapan standartnasional pendidikan butir mana disetiap standart yang masih perlu mendapatkan perhatiansecara serius. Metode penelitian yang digunakan adalahmetode survai yakni upayamengumpulkan informasi dari responden yang merupakan contoh dengan menggunakankuesioner yang terstruktur. Populasi dari penelitian ini adalah jumlah satuan pendidikanSekolah Mengengah Pertama (SMP) sebanyak 349 sekolah yang bersatatus sekolah negeriyang menyebar di wilayah eks karesidenan Surakarta. Sampel diambil sebanyak 172 sekolahdengan pendekatan area probability sampling. Instrumen yang digunakan dalam penelitian inimerupakan kuesioner tertutup dengan skala likert. Setelah data terkumpul kemudian dianalisisdengan pendekatan kuantitatif secara deskriptif. Hasil penelitian menunjukkan bahwa dilihatdari standar isi; standar proses; standar kompetensi kelulusan; standar pendidikan dan tenagakependididkan; standar sarana dan prasarana ; standar pengelolaan; standar pembiayaan; danstandar penilaian sudah cukup baik. Ketercapaian delapan standar nasional pendidikan seluruhsekolah sampel sudah mencapai tingkat yang cukup tinggi yakni di atas 90%, kendati masihada beberapa dari sub butir standart yang masih perlu lebih diperbaiki Abstract:The aim of this research is to discover the school effectiveness seen from eightcomponents of standards of national education. From these eight components, we will find outwhich components still need to be regenerated. This research is using survey method bystructured questionnaire to gather information from respondents. The population is 349Government Junior High Schools in a region of ex Surakarta Residence. Total of samples frompopulation is 172 schools, using area probability sampling approach. To collect the data, weused closed questionnaire with Likert scale as the instrument. After all data collected, then weanalyze it descriptively with quantitative approach. The result shown that all the componentsof standards of national education, including content standards; process standards;competence of graduates standards; educational standards and human resource standards;facilities standards; management standards; funding standards; and assesment standards arefairly good. The achievement of eight standards of national education from all sample schoolsalready achieved high level, i.e. above than 90%. But still there are several sub componentsneeds to be regenerated.

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>

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