arXiv: Chemical Physics最新文献_第3页

Stereodynamic control of overlapping resonances in cold molecular collisions 冷分子碰撞中重叠共振的立体动力学控制

arXiv: Chemical Physics Pub Date : 2020-05-13 DOI: 10.1103/PHYSREVRESEARCH.2.032018

M. Morita, Q. Yao, Changjian Xie, Hua Guo, N. Balakrishnan

引用次数: 7

Coupled Cluster Theory for Molecular Polaritons: Changing Ground and Excited States 分子极化子的耦合簇理论:改变基态和激发态

arXiv: Chemical Physics Pub Date : 2020-05-09 DOI: 10.1103/physrevx.10.041043

Tor S. Haugland, Enrico Ronca, Eirik F. Kjønstad, Á. Rubio, H. Koch

引用次数: 83

Crystal structures of Fe-gluconate 葡萄糖酸铁的晶体结构

arXiv: Chemical Physics Pub Date : 2020-04-22 DOI: 10.1016/j.ica.2021.120607

L. Gondek, S. Dubiel

引用次数: 3

Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design 使用信号处理和机器学习的分子设计:类时频表示和正向设计

arXiv: Chemical Physics Pub Date : 2020-04-20 DOI: 10.21203/RS.3.RS-229094/V1

A. Tchagang, A. Tewfik, Julio J. Vald'es

{"title":"Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design","authors":"A. Tchagang, A. Tewfik, Julio J. Vald'es","doi":"10.21203/RS.3.RS-229094/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-229094/V1","url":null,"abstract":"\u0000 Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM↔ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM↔ML pipeline, we obtain a powerful machinery (QM↔SP↔ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 16 properties), the new QM↔SP↔ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM↔SP↔ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials. The QM↔SP↔ML is also housed at the following website: https://github.com/TABeau/QM-SP-ML.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91043574","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

Beyond the limits of conventional Stark deceleration 超出了传统斯塔克减速的极限

arXiv: Chemical Physics Pub Date : 2020-04-04 DOI: 10.1103/PHYSREVRESEARCH.2.033095

D. Reens, Hao Wu, A. Aeppli, Anna McAuliffe, P. Wcisło, T. Langen, Jun Ye

引用次数: 2

Design principles for biochemical oscillations with limited energy resources 有限能源条件下生化振荡的设计原则

arXiv: Chemical Physics Pub Date : 2020-04-01 DOI: 10.1103/physrevresearch.2.043331

Zhiyu Cao, Huijun Jiang, Z. Hou

引用次数: 2

Ultrafast electron diffraction imaging of gas-phase molecules 气相分子的超快电子衍射成像

arXiv: Chemical Physics Pub Date : 2020-03-06 DOI: 10.1016/BS.AAMOP.2020.04.001

K. Amini, J. Biegert

引用次数: 11

Learning the energy curvature versus particle number in approximate density functionals 学习近似密度泛函中能量曲率与粒子数的关系

arXiv: Chemical Physics Pub Date : 2020-02-17 DOI: 10.24435/MATERIALSCLOUD:2020.0031/V1

Alberto Fabrizio, Benjamin Meyer, C. Corminboeuf

{"title":"Learning the energy curvature versus particle number in approximate density functionals","authors":"Alberto Fabrizio, Benjamin Meyer, C. Corminboeuf","doi":"10.24435/MATERIALSCLOUD:2020.0031/V1","DOIUrl":"https://doi.org/10.24435/MATERIALSCLOUD:2020.0031/V1","url":null,"abstract":"The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory (DFT). Related to the lack of derivative discontinuity in approximate exchange-correlation potentials, the information about the curvature has been successfully used to restore the physical meaning of Kohn-Sham orbital eigenvalues and to develop non-empirical tuning and correction schemes for density functional approximations. In this work, we propose the construction of a machine-learning framework targeting the average energy curvature between the neutral and the radical cation state of thousands of small organic molecules (QM7 database). The applicability of the model is demonstrated in the context of system-specific gamma-tuning of the LC-ωPBE functional and validated against the molecular first ionization potentials at equation-of-motion (EOM) coupled-cluster references. In addition, we propose a local version of the non-linear regression model and demonstrate its transferability and predictive power by determining the optimal range-separation parameter for two large molecules relevant to the field of hole-transporting materials. Finally, we explore the underlying structure of the QM7 database with the t-SNE dimensionality-reduction algorithm and identify structural and compositional patterns that promote the deviation from the piecewise linearity condition.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80373100","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

Excited‐State Calculations with Quantum Monte Carlo 激发态计算与量子蒙特卡罗

arXiv: Chemical Physics Pub Date : 2020-02-10 DOI: 10.1002/9781119417774.ch8

J. Feldt, C. Filippi

引用次数: 10

Energetics of (H2O)20 isomers by means of F12 canonical and localized coupled cluster methods 用F12正则和局域耦合簇方法研究(H2O)20异构体的能量学

arXiv: Chemical Physics Pub Date : 2020-02-05 DOI: 10.1063/5.0049720

Nitai Sylvetsky, Jan M. L. Martin

引用次数: 3