scholarly journals Multi-fidelity prediction of molecular optical peaks with deep learning

2022 ◽  
Author(s):  
Kevin P Greenman ◽  
William H. Green ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Optical Properties ◽  
Solar Cells ◽  
Deep Learning ◽  
Ab Initio ◽  
Statistical Methods ◽  
Biomedical Imaging ◽  
Molecular Design

Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction,...

scholarly journals Multi-fidelity prediction of molecular optical peaks with deep learning

2021 ◽  
Author(s):  
Kevin Greenman ◽  
William Green ◽  
Rafael Gómez-Bombarelli
Keyword(s):  
Optical Properties ◽  
Statistical Methods ◽  
Message Passing ◽  
Density Functional ◽  
Molecular Design ◽  
Chemical Space ◽  
Epistemic Uncertainty ◽  
Computational Cost ◽  
Regression Tree ◽  
Td Dft

Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28,000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties.

Structure and Optical Properties CdS and CdTe Films on Flexible Substrate Obtained by DC Magnetron Sputtering for Solar Cells

2017 ◽  
Vol 9 (5) ◽  
pp. 05035-1-05035-6 ◽  
Author(s):  
G. I. Kopach ◽  
◽  
R. P. Mygushchenko ◽  
G. S. Khrypunov ◽  
A. I. Dobrozhan ◽  
...  
Keyword(s):  
Optical Properties ◽  
Solar Cells ◽  
Magnetron Sputtering ◽  
Flexible Substrate ◽  
Dc Magnetron Sputtering ◽  
Cdte Films

The Effect of Donor Molecular Structure on Power Conversion Efficiency of Small-Molecule-Based Organic Solar Cells

2019 ◽  
Vol 16 (3) ◽  
pp. 236-243 ◽  
Author(s):  
Hui Zhang ◽  
Yibing Ma ◽  
Youyi Sun ◽  
Jialei Liu ◽  
Yaqing Liu ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Solar Cells ◽  
Power Conversion Efficiency ◽  
Conversion Efficiency ◽  
Small Molecule ◽  
Organic Solar Cells ◽  
Molecular Design ◽  
Power Conversion ◽  
The Relationship

In this review, small-molecule donors for application in organic solar cells reported in the last three years are highlighted. Especially, the effect of donor molecular structure on power conversion efficiency of organic solar cells is reported in detail. Furthermore, the mechanism is proposed and discussed for explaining the relationship between structure and power conversion efficiency. These results and discussions draw some rules for rational donor molecular design, which is very important for further improving the power conversion efficiency of organic solar cells based on the small-molecule donor.

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

First-principles study on the electronic and optical properties of the orthorhombic CsPbBr3 and CsPbI3 with Cmcm space group

2021 ◽  
Author(s):  
Xianhao Zhao ◽  
Tianyu Tang ◽  
Quan Xie ◽  
like gao ◽  
Limin Lu ◽  
...  
Keyword(s):  
Optical Properties ◽  
Solar Cells ◽  
Energy Conversion ◽  
Conversion Efficiency ◽  
First Principles ◽  
Energy Conversion Efficiency ◽  
Space Group ◽  
Absorbing Materials ◽  
Halide Perovskites ◽  
Lead Halide

The cesium lead halide perovskites are regarded as effective candidates for light-absorbing materials in solar cells, which have shown excellent performances in experiments such as promising energy conversion efficiency. In...

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