Li Zhou

Dr. Li Zhou

School of Chemical Engineering, Sichuan University, Chengdu 610065, China

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 September 2021 in ACS Omega
Verified
Journal Article
Open access
AEAndreas Eibeck
Andreas Eibeck
DNDaniel Nurkowski
Daniel Nurkowski
AMAngiras Menon
Angiras Menon
JBJiaru Bai
Jiaru Bai
JWJinkui Wu
Jinkui Wu
MKMarkus Kraft
Markus Kraft
In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.071. The experimental dataset proved much harder to fit, with all of the models exhibiting a rather poor performance. Contrary to the computational dataset, the baseline models were found to perform better than the neural models. To improve the ability of machine learning models to predict PCEs for OPVs, either better computational results that correlate well with experiments or more experimental data at well-controlled conditions are likely required.

6 January 2023 in Industrial & Engineering Chemistry Research
Verified
Journal Article
Closed access
XXXiaoyu Xue
Xiaoyu Xue
MCMin Cheng
Min Cheng
SWShihui Wang
Shihui Wang
SCShaochen Chen
Shaochen Chen
Li ZhouLi Zhou
Li Zhou
Xu JiXu Ji
Xu Ji

30 December 2020 in Industrial & Engineering Chemistry Research
Verified
Journal Article
Closed access
GHGe He
Ge He
CZChenglin Zhou
Chenglin Zhou
TLTao Luo
Tao Luo
Li ZhouLi Zhou
Li Zhou
Yiyang DaiYiyang Dai
Yiyang Dai
XJXu Ji
Xu Ji

25 February 2021 in Materials
Verified
Journal Article
Open access
PDF file
JXJiajia Xu
Jiajia Xu
Li ZhouLi Zhou
Li Zhou
GHGe He
Ge He
Xu JiXu Ji
Xu Ji
Yiyang DaiYiyang Dai
Yiyang Dai
YDYagu Dang
Yagu Dang
Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected influential factors. The effectiveness of the proposed model was tested on a case study, and the high Pearson correlation coefficient (0.9821) and the low mean absolute percentage error and delta (0.0394 and 0.395, respectively) indicate that the proposed model can deliver accurate and reliable results.

21 May 2020 in Industrial & Engineering Chemistry Research
Verified
Journal Article
Closed access
CCChen Chen
Chen Chen
Li ZhouLi Zhou
Li Zhou
XJXu Ji
Xu Ji
GHGe He
Ge He
YDYiyang Dai
Yiyang Dai
YDYagu Dang
Yagu Dang

24 April 2024 in Industrial & Engineering Chemistry Research
Verified
Journal Article
Closed access
BXBingru Xin
Bingru Xin
MFMinggao Feng
Minggao Feng
MCMin Cheng
Min Cheng
ZDZhongde Dai
Zhongde Dai
SYShiyang Ye
Shiyang Ye
Xu JiXu Ji
Xu Ji