Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels

2018 ◽  
Vol 148 (24) ◽  
pp. 241738 ◽  
Author(s):  
GuanYa Yang ◽  
Jiang Wu ◽  
ShuGuang Chen ◽  
WeiJun Zhou ◽  
Jian Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
First Principles ◽  
Heat Of Formation ◽  
Accurate Prediction ◽  
First Principles Method

Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers

2017 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
First Principles ◽  
Data Modeling ◽  
Accurate Prediction ◽  
Index Of Refraction ◽  
Structure Theory ◽  
Organic Polymers ◽  
Number Density ◽  
Lorenz Equation ◽  
In Silico Studies ◽  
Wide Range

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3–1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that an guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and highly economical path to determining the RI values for a wide range of organic polymers.

scholarly journals Solubility prediction from first principles: a density of states approach

2018 ◽  
Vol 20 (32) ◽  
pp. 20981-20987 ◽  
Author(s):  
Simon Boothroyd ◽  
Andy Kerridge ◽  
Anders Broo ◽  
David Buttar ◽  
Jamshed Anwar
Keyword(s):  
Density Of States ◽  
First Principles ◽  
Fundamental Property ◽  
Accurate Prediction ◽  
Solubility Prediction ◽  
Efficient Approach

Solubility is a fundamental property of widespread significance. Its accurate prediction remains a major challenge. We present a novel, efficient approach to solubility prediction for molecules over a range of conditions based on density of states.

Integrating Neural Networks with First Principles Models for Dynamic Modeling

1992 ◽  
Vol 25 (5) ◽  
pp. 327-332 ◽  
Author(s):  
Hong-Te Su ◽  
N. Bhat ◽  
P.A. Minderman ◽  
T.J. McAvoy
Keyword(s):  
Neural Networks ◽  
Dynamic Modeling ◽  
First Principles

scholarly journals How to Predict Seawater Temperature for Sustainable Marine Aquaculture (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13887-13888
Author(s):  
Masahito Okuno ◽  
Takanobu Otsuka
Keyword(s):  
Neural Networks ◽  
Seawater Temperature ◽  
Prediction Method ◽  
Accurate Prediction ◽  
Average Error ◽  
Marine Aquaculture ◽  
Global Demand ◽  
Environment Control ◽  
Marine Products ◽  
Using Data

The increasing global demand for marine products has turned attention to marine aquaculture. In marine aquaculture, appropriate environment control is important for a stable supply. The influence of seawater temperature on this environment is significant and accurate prediction is therefore required. In this paper, we propose and describe the implementation of a seawater prediction method using data acquired from real aquaculture areas and neural networks. Our evaluation experiment showed that hourly next-day prediction has an average error of about 0.2 to 0.4 ◦C and daily prediction of up to one week has an average error of about 0.2 to 0.5 ◦C. This is enough to meet actual worker need, which is within 1 ◦C error, thus confirming that our seawater prediction method is suitable for actual sites.

scholarly journals Accurate Prediction of Band Structure of FeS2: A Hard Quest of Advanced First-Principles Approaches

2021 ◽  
Vol 9 ◽  
Author(s):  
Min-Ye Zhang ◽  
Hong Jiang
Keyword(s):  
Band Structure ◽  
First Principles ◽  
High Energy ◽  
Accurate Prediction ◽  
Band Gaps ◽  
Full Potential ◽  
Band Structures ◽  
Electronic Band ◽  
Fundamental Band ◽  
Hybrid Functionals

The pyrite and marcasite polymorphs of FeS2 have attracted considerable interests for their potential applications in optoelectronic devices because of their appropriate electronic and optical properties. Controversies regarding their fundamental band gaps remain in both experimental and theoretical materials research of FeS2. In this work, we present a systematic theoretical investigation into the electronic band structures of the two polymorphs by using many-body perturbation theory with the GW approximation implemented in the full-potential linearized augmented plane waves (FP-LAPW) framework. By comparing the quasi-particle (QP) band structures computed with the conventional LAPW basis and the one extended by high-energy local orbitals (HLOs), denoted as LAPW + HLOs, we find that one-shot or partially self-consistent GW (G0W0 and GW0, respectively) on top of the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation with a converged LAPW + HLOs basis is able to remedy the artifact reported in the previous GW calculations, and leads to overall good agreement with experiment for the fundamental band gaps of the two polymorphs. Density of states calculated from G0W0@PBE with the converged LAPW + HLOs basis agrees well with the energy distribution curves from photo-electron spectroscopy for pyrite. We have also investigated the performances of several hybrid functionals, which were previously shown to be able to predict band gaps of many insulating systems with accuracy close or comparable to GW. It is shown that the hybrid functionals considered in general fail badly to describe the band structures of FeS2 polymorphs. This work indicates that accurate prediction of electronic band structure of FeS2 poses a stringent test on state-of-the-art first-principles approaches, and the G0W0 method based on semi-local approximation performs well for this difficult system if it is practiced with well-converged numerical accuracy.

High catalytic activity for CO oxidation on single Fe atom stabilized in graphene vacancies

RSC Advances ◽  
2016 ◽  
Vol 6 (96) ◽  
pp. 93985-93996 ◽  
Author(s):  
Yanan Tang ◽  
Jincheng Zhou ◽  
Zigang Shen ◽  
Weiguang Chen ◽  
Chenggang Li ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Co Oxidation ◽  
First Principles ◽  
High Catalytic Activity ◽  
First Principles Method

The geometric, electronic and catalytic characters of Fe atom embedded graphene (including monovacancy and divacancy) are investigated using the first-principles method, which gives a reference on designing graphene-based catalysts for CO oxidation.

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