ChemRxiv : the preprint server for chemistry最新文献_第8页

Photophysical property and optical nonlinearity of cyclo[18]carbon (C18) precursors, C18-(CO)n (n = 2, 4, and 6): Focusing on the effect of carbonyl (-CO) groups 环[18]碳（C18）前体C18-（CO）n（n=2、4和6）的光物理性质和光学非线性：关注羰基（-CO）的影响

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-20 DOI: 10.33774/chemrxiv-2021-mkfdj

Xia Wang, Zeyu Liu, Xiufen Yan, T. Lu, Haowei Wang, Weiwei Xiong

{"title":"Photophysical property and optical nonlinearity of cyclo[18]carbon (C18) precursors, C18-(CO)n (n = 2, 4, and 6): Focusing on the effect of carbonyl (-CO) groups","authors":"Xia Wang, Zeyu Liu, Xiufen Yan, T. Lu, Haowei Wang, Weiwei Xiong","doi":"10.33774/chemrxiv-2021-mkfdj","DOIUrl":"https://doi.org/10.33774/chemrxiv-2021-mkfdj","url":null,"abstract":"Considering their remarkable chemical stability, the precursors of cyclo[18]carbon (C18), C18-(CO)n (n = 2, 4, and 6), have more practical significance than the elusive C18 ring. In the present paper, the electronic spectrum and (hyper)polarizability of the C18-(CO)n (n = 2, 4, and 6) are studied by theoretical calculations and analyses for revealing the utility of introduction of carbonyl (-CO) groups on molecular optical properties. The analysis results show that the successive introduction of -CO groups leads to red-shift of the absorption spectrum, but maximum absorption of all molecules is mainly due to the charge redistribution caused by electron transition within C18 ring. Except for the vanishing first hyperpolarizability of C18-(CO)6 results from its octupolar character, the (hyper)polarizabilities of the precursors present an ascending trend with the increase of -CO groups in the molecule, and the higher-order response properties are more sensitive to the number of -CO groups. By means of (hyper)polarizability density analysis and (hyper)polarizability contribution decomposition, the fundamental reasons for the difference of (hyper)polarizability of different molecules were systematically discussed from the perspectives of physical and structural origins, respectively. As to the frequency dispersions under the incident light, the significant optical resonances were found on the hyperpolarizability of molecules C18-(CO)n (n = 2, 4, and 6), which contrast with the fact that it has inconspicuous influences on molecular polarizability.","PeriodicalId":72565,"journal":{"name":"ChemRxiv : the preprint server for chemistry","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42550044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Adding colour to mass spectra: Charge Determination Analysis (CHARDA) assigns charge state to every ion peak 为质谱添加颜色:电荷测定分析(CHARDA)为每个离子峰分配电荷状态

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-17 DOI: 10.33774/chemrxiv-2021-f6n9j

Y. Lyutvinskiy, K. Nagornov, Anton N. Kozhinov, N. Gasilova, L. Menin, Zhaowei Meng, Xuepei Zhang, A. Saei, Y. Tsybin, A. Makarov, R. Zubarev

引用次数: 0

Generative Pre-Training from Molecules 分子生成预训练

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-16 DOI: 10.33774/chemrxiv-2021-5fwjd

S. Adilov

{"title":"Generative Pre-Training from Molecules","authors":"S. Adilov","doi":"10.33774/chemrxiv-2021-5fwjd","DOIUrl":"https://doi.org/10.33774/chemrxiv-2021-5fwjd","url":null,"abstract":"SMILES is a line notation for entering and representing molecules. Being inherently a language construct, it allows estimating molecular data in a self-supervised fashion by employing machine learning methods for natural language processing (NLP). The recent success of attention-based neural networks in NLP has made large-corpora transformer pretraining a de facto standard for learning representations and transferring knowledge to downstream tasks. In this work, we attempt to adapt transformer capabilities to a large SMILES corpus by constructing a GPT-2-like language model. We experimentally show that a pretrained causal transformer captures general knowledge that can be successfully transferred to such downstream tasks as focused molecule generation and single-/multi-output molecular-property prediction. For each task, we freeze model parameters and attach trainable lightweight networks between attention blocks—adapters—as alternative to fine-tuning. With a relatively modest setup, our transformer outperforms the recently proposed ChemBERTa transformer and approaches state-of-the-art MoleculeNet and Chemprop results. Overall, transformers pretrained on SMILES corpora are promising alternatives that do not require handcrafted feature engineering, make few assumptions about structure of data, and scale well with the pretraining data size.","PeriodicalId":72565,"journal":{"name":"ChemRxiv : the preprint server for chemistry","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43178020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A General Method for Synthesis of Isomaleimide Derivatives 一种合成异马来酰亚胺衍生物的通用方法

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-16 DOI: 10.33774/chemrxiv-2021-hb4dq

S. Gondi

引用次数: 0

Synthesis of Chain Transfer Agents for RAFT Polymerization by using DBU 用DBU合成RAFT聚合链转移剂

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-16 DOI: 10.33774/chemrxiv-2021-sf0t0

S. Gondi

引用次数: 0

Shaping student confidence and their perception of learning in undergraduate chemistry and biochemistry courses 在本科化学和生物化学课程中培养学生的自信心和学习观念

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-16 DOI: 10.33774/chemrxiv-2021-80skt-v2

Timothy Gould

{"title":"Shaping student confidence and their perception of learning in undergraduate chemistry and biochemistry courses","authors":"Timothy Gould","doi":"10.33774/chemrxiv-2021-80skt-v2","DOIUrl":"https://doi.org/10.33774/chemrxiv-2021-80skt-v2","url":null,"abstract":"This study examined factors influencing student confidence and their perception of learning in the context of undergraduate chemistry and biochemistry courses. Anonymous online surveys were used to measure the extent to which small group work influenced student confidence in solving problems compared to working individually, as well as how various course factors and the amount of effort and pleasure students experienced during their coursework influenced their perception of learning. Upon examining over 2500 student responses to more than 150 unique problems/exercises across 3 different courses, student confidence in solving problems improved to an average of approximately 8 out of 10 when they worked in small groups, compared to that of 6.5 out of 10 when working alone. Students ranked (i) opportunities to revisit assessments, (ii) the class environment, and (iii) instructor feedback as course factors that were most influential on their learning. There was little if any correlation between student effort and their perception of learning, and their perception of learning only slightly correlated with the amount of pleasure they experienced during their coursework. Taken together, these data highlight the importance of facilitating regular small group work, multistage assessments, good feedback and a pleasurable learning environment for students.","PeriodicalId":72565,"journal":{"name":"ChemRxiv : the preprint server for chemistry","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43681720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerated Design of Near-Infrared-II Molecular Fluorophores via First-Principles Understanding and Machine Learning 基于第一性原理理解和机器学习的近红外II分子荧光团加速设计

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-15 DOI: 10.33774/chemrxiv-2021-6617l

Shidang Xu, Pengfei Cai, Jiali Li, Xianhe Zhang, Xianglong Liu, Xiaonan Wang, Bin Liu

{"title":"Accelerated Design of Near-Infrared-II Molecular Fluorophores via First-Principles Understanding and Machine Learning","authors":"Shidang Xu, Pengfei Cai, Jiali Li, Xianhe Zhang, Xianglong Liu, Xiaonan Wang, Bin Liu","doi":"10.33774/chemrxiv-2021-6617l","DOIUrl":"https://doi.org/10.33774/chemrxiv-2021-6617l","url":null,"abstract":"Organic molecular fluorophores in the second near-infrared window (NIR-II) have attracted much attention in the recent decade due to their great potentials in both fundamental research and practical applications. This is especially true for biomedical research, owing to their deep light penetration depth and low bioluminescence background at the long wavelength. However, the fluorescence quantum yields (QY) of most NIR-II materials are very low, which are not ideal for practical applications. Although there is a growing need to discover new NIR-II fluorophores, most of them were designed based on experience, and the structures were limited to few molecular motifs. Herein, we report the design of high QY NIR-II fluorophores in solutions based on enhancing the rigidity of the conjugated backbones, which could be quantified by the Seminario method. A deep neural network was trained to predict the HOMO-LUMO energy gaps for a chemical library of NIR-II backbone structures. Hundreds of new NIR-II cores with low energy gap were discovered, and eight of them across different acceptor cores are found to have relatively rigid conjugated backbones. With further molecular processing or formulation, the proposed new fluorophores should boost the development of NIR-II materials for applications in a wide range of fields.","PeriodicalId":72565,"journal":{"name":"ChemRxiv : the preprint server for chemistry","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43463254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wittig olefination “baking powder”: a hexameric halogen-bonded phosphonium salt cage for encapsulation and mechanochemical transformation of small-molecule carbonyl compounds Wittig油化“发酵粉”：用于小分子羰基化合物封装和机械化学转化的六元卤素键合鏻盐笼

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-15 DOI: 10.33774/chemrxiv-2021-0fg9k

J. Marrett, H. Titi, T. Friščić

引用次数: 0

Mechanistic insights into pH-dependent hydrogen electrocatalysis by flipping interfacial water 翻转界面水对pH依赖性氢电催化的机理研究

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-13 DOI: 10.33774/chemrxiv-2021-gks2k

Qiang Sun, N. Oliveira, S. Tyukhtenko, J. Guo, Nathalie Myrthil, Steven A. Lopez, Ian Kendrick, S. Mukerjee, Lu Ma, S. Ehrlich, Jingkun Li, Yushan Yan, Q. Jia

引用次数: 0

Lignin Biorefinery Optimization Through Machine Learning 基于机器学习的木质素生物精炼厂优化

ChemRxiv : the preprint server for chemistry Pub Date : 2021-09-07 DOI: 10.33774/chemrxiv-2021-6r888

Joakim Löfgren, Dmitry E. Tarasov, Taru Koitto, P. Rinke, M. Balakshin, M. Todorović

{"title":"Lignin Biorefinery Optimization Through Machine Learning","authors":"Joakim Löfgren, Dmitry E. Tarasov, Taru Koitto, P. Rinke, M. Balakshin, M. Todorović","doi":"10.33774/chemrxiv-2021-6r888","DOIUrl":"https://doi.org/10.33774/chemrxiv-2021-6r888","url":null,"abstract":"Lignin is an abundant biomaterial that currently emerges as a low value by-product in the pulp and paper industry but could be repurposed for high-value products as part of the ongoing global transition to a sustainable society. To increase lignins value, rational and efficient approaches to optimizing lignin biorefineries to produce high value bioproducts are required. Here, we report the optimization of the AquaSolv Omni (AqSO) Biorefinery, a newly introduced biorefinery concept based on hydrothermal pretreatment and solvent extraction. We employ a machine-learning framework based on Bayesian optimization, to provide sample-efficient and guided data collection as well as surrogate model building. The surrogate models allow us to map multiple experimental outputs, including the extracted lignin yield and main structural properties obtained by 2D NMR, as functions of the hydrothermal pretreatment reaction severity and temperature. Our results show that with Bayesian optimization, predictive models can be converged with only 21 data points to within a margin of error comparable to the underlying experimental error. By applying a Pareto point analysis, we demonstrate how the predictive models can be used in tandem to identify optimal extraction conditions for concrete applications in lignin valorization.","PeriodicalId":72565,"journal":{"name":"ChemRxiv : the preprint server for chemistry","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43178107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2