Pub Date : 2020-05-13DOI: 10.1103/PHYSREVRESEARCH.2.032018
M. Morita, Q. Yao, Changjian Xie, Hua Guo, N. Balakrishnan
Stereodynamic control of resonant molecular collisions has emerged as a new frontier in cold molecule research. Recent experimental studies have focused on weakly interacting molecular systems such as HD collisions with H$_2$, D$_2$ and He. We report here the possibility of such control in strongly interacting systems taking rotational relaxation in cold collisions of HCl and H$_2$. Using explicit quantum scattering calculations in full six dimensions it is shown that robust control of the collision dynamics is possible even when multiple (overlapping) shape-resonances coexist in a narrow energy range, indicating that cold stereochemistry offers great promise for many molecules beyond simple systems. We demonstrate a striking case where two prominent peaks in overlapping resonances are switched-off simultaneously by suitable alignment of the HCl molecule.
{"title":"Stereodynamic control of overlapping resonances in cold molecular collisions","authors":"M. Morita, Q. Yao, Changjian Xie, Hua Guo, N. Balakrishnan","doi":"10.1103/PHYSREVRESEARCH.2.032018","DOIUrl":"https://doi.org/10.1103/PHYSREVRESEARCH.2.032018","url":null,"abstract":"Stereodynamic control of resonant molecular collisions has emerged as a new frontier in cold molecule research. Recent experimental studies have focused on weakly interacting molecular systems such as HD collisions with H$_2$, D$_2$ and He. We report here the possibility of such control in strongly interacting systems taking rotational relaxation in cold collisions of HCl and H$_2$. Using explicit quantum scattering calculations in full six dimensions it is shown that robust control of the collision dynamics is possible even when multiple (overlapping) shape-resonances coexist in a narrow energy range, indicating that cold stereochemistry offers great promise for many molecules beyond simple systems. We demonstrate a striking case where two prominent peaks in overlapping resonances are switched-off simultaneously by suitable alignment of the HCl molecule.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89634533","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}
Pub Date : 2020-05-09DOI: 10.1103/physrevx.10.041043
Tor S. Haugland, Enrico Ronca, Eirik F. Kjønstad, Á. Rubio, H. Koch
We present an ab initio correlated approach to study molecules that interact strongly with quantum fields in an optical cavity. Quantum electrodynamics coupled cluster theory provides a non-perturbative description of cavity-induced effects in ground and excited states. Using this theory, we show how quantum fields can be used to manipulate charge transfer and photochemical properties of molecules. We propose a strategy to lift electronic degeneracies and induce modifications in the ground state potential energy surface close to a conical intersection.
{"title":"Coupled Cluster Theory for Molecular Polaritons: Changing Ground and Excited States","authors":"Tor S. Haugland, Enrico Ronca, Eirik F. Kjønstad, Á. Rubio, H. Koch","doi":"10.1103/physrevx.10.041043","DOIUrl":"https://doi.org/10.1103/physrevx.10.041043","url":null,"abstract":"We present an ab initio correlated approach to study molecules that interact strongly with quantum fields in an optical cavity. Quantum electrodynamics coupled cluster theory provides a non-perturbative description of cavity-induced effects in ground and excited states. Using this theory, we show how quantum fields can be used to manipulate charge transfer and photochemical properties of molecules. We propose a strategy to lift electronic degeneracies and induce modifications in the ground state potential energy surface close to a conical intersection.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84967685","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}
Pub Date : 2020-04-22DOI: 10.1016/j.ica.2021.120607
L. Gondek, S. Dubiel
{"title":"Crystal structures of Fe-gluconate","authors":"L. Gondek, S. Dubiel","doi":"10.1016/j.ica.2021.120607","DOIUrl":"https://doi.org/10.1016/j.ica.2021.120607","url":null,"abstract":"","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79773531","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}
Pub Date : 2020-04-20DOI: 10.21203/RS.3.RS-229094/V1
A. Tchagang, A. Tewfik, Julio J. Vald'es
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.
{"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":"63 1","pages":""},"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}
Pub Date : 2020-04-04DOI: 10.1103/PHYSREVRESEARCH.2.033095
D. Reens, Hao Wu, A. Aeppli, Anna McAuliffe, P. Wcisło, T. Langen, Jun Ye
Stark deceleration enables the production of cold and dense molecular beams with applications in trapping, collisional studies, and precision measurement. Improving the efficiency of Stark deceleration, and hence the achievable molecular densities, is central to unlock the full potential of such studies. One of the chief limitations arises from the transverse focusing properties of Stark decelerators. We introduce a new operation strategy that circumvents this limit without any hardware modifications, and experimentally verify our results for hydroxyl radicals. Notably, improved focusing results in significant gains in molecule yield with increased operating voltage, formerly limited by transverse-longitudinal coupling. At final velocities sufficiently small for trapping, molecule flux improves by a factor of four, and potentially more with increased voltage. The improvement is more significant for less readily polarized species, thereby expanding the class of candidate molecules for Stark deceleration.
{"title":"Beyond the limits of conventional Stark deceleration","authors":"D. Reens, Hao Wu, A. Aeppli, Anna McAuliffe, P. Wcisło, T. Langen, Jun Ye","doi":"10.1103/PHYSREVRESEARCH.2.033095","DOIUrl":"https://doi.org/10.1103/PHYSREVRESEARCH.2.033095","url":null,"abstract":"Stark deceleration enables the production of cold and dense molecular beams with applications in trapping, collisional studies, and precision measurement. Improving the efficiency of Stark deceleration, and hence the achievable molecular densities, is central to unlock the full potential of such studies. One of the chief limitations arises from the transverse focusing properties of Stark decelerators. We introduce a new operation strategy that circumvents this limit without any hardware modifications, and experimentally verify our results for hydroxyl radicals. Notably, improved focusing results in significant gains in molecule yield with increased operating voltage, formerly limited by transverse-longitudinal coupling. At final velocities sufficiently small for trapping, molecule flux improves by a factor of four, and potentially more with increased voltage. The improvement is more significant for less readily polarized species, thereby expanding the class of candidate molecules for Stark deceleration.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80614790","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}
Pub Date : 2020-04-01DOI: 10.1103/physrevresearch.2.043331
Zhiyu Cao, Huijun Jiang, Z. Hou
As biochemical systems may frequently suffer from limited energy resources so that internal molecular fluctuation has to be utilized to induce random rhythm, it is still a great theoretical challenge to understand the elementary principles for biochemical systems with limited energy resources to maintain phase accuracy and phase sensitivity. Here, we address the issue by deriving the energy accuracy and the sensitivity-accuracy trade-off relations for a general biochemical model, analytically and numerically. We find that, biochemical systems consume much lower energy cost by noise-induced oscillations to keep almost equal efficiency to maintain precise processes than that by normal oscillations, elucidating clearly the survival mechanism when energy resources are limited. Moreover, an optimal system size is predicted where both the highest sensitivity and accuracy can be reached at the same time, providing a new strategy for the design of biological networks with limited energy sources.
{"title":"Design principles for biochemical oscillations with limited energy resources","authors":"Zhiyu Cao, Huijun Jiang, Z. Hou","doi":"10.1103/physrevresearch.2.043331","DOIUrl":"https://doi.org/10.1103/physrevresearch.2.043331","url":null,"abstract":"As biochemical systems may frequently suffer from limited energy resources so that internal molecular fluctuation has to be utilized to induce random rhythm, it is still a great theoretical challenge to understand the elementary principles for biochemical systems with limited energy resources to maintain phase accuracy and phase sensitivity. Here, we address the issue by deriving the energy accuracy and the sensitivity-accuracy trade-off relations for a general biochemical model, analytically and numerically. We find that, biochemical systems consume much lower energy cost by noise-induced oscillations to keep almost equal efficiency to maintain precise processes than that by normal oscillations, elucidating clearly the survival mechanism when energy resources are limited. Moreover, an optimal system size is predicted where both the highest sensitivity and accuracy can be reached at the same time, providing a new strategy for the design of biological networks with limited energy sources.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83537399","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}
Pub Date : 2020-03-06DOI: 10.1016/BS.AAMOP.2020.04.001
K. Amini, J. Biegert
{"title":"Ultrafast electron diffraction imaging of gas-phase molecules","authors":"K. Amini, J. Biegert","doi":"10.1016/BS.AAMOP.2020.04.001","DOIUrl":"https://doi.org/10.1016/BS.AAMOP.2020.04.001","url":null,"abstract":"","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"57 1-2","pages":"163-231"},"PeriodicalIF":0.0,"publicationDate":"2020-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91475437","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}
Pub Date : 2020-02-17DOI: 10.24435/MATERIALSCLOUD:2020.0031/V1
Alberto Fabrizio, Benjamin Meyer, C. Corminboeuf
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.
{"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":"236 1","pages":""},"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}
Pub Date : 2020-02-10DOI: 10.1002/9781119417774.ch8
J. Feldt, C. Filippi
Quantum Monte Carlo methods are first-principle approaches that approximately solve the Schrodinger equation stochastically. As compared to traditional quantum chemistry methods, they offer important advantages such as the ability to handle a large variety of many-body wave functions, the favorable scaling with the number of particles, and the intrinsic parallelism of the algorithms which are particularly suitable to modern massively parallel computers. In this chapter, we focus on the two quantum Monte Carlo approaches most widely used for electronic structure problems, namely, the variational and diffusion Monte Carlo methods. We give particular attention to the recent progress in the techniques for the optimization of the wave function, a challenging and important step to achieve accurate results in both the ground and the excited state. We conclude with an overview of the current status of excited-state calculations for molecular systems, demonstrating the potential of quantum Monte Carlo methods in this field of applications.
{"title":"Excited‐State Calculations with Quantum Monte Carlo","authors":"J. Feldt, C. Filippi","doi":"10.1002/9781119417774.ch8","DOIUrl":"https://doi.org/10.1002/9781119417774.ch8","url":null,"abstract":"Quantum Monte Carlo methods are first-principle approaches that approximately solve the Schrodinger equation stochastically. As compared to traditional quantum chemistry methods, they offer important advantages such as the ability to handle a large variety of many-body wave functions, the favorable scaling with the number of particles, and the intrinsic parallelism of the algorithms which are particularly suitable to modern massively parallel computers. In this chapter, we focus on the two quantum Monte Carlo approaches most widely used for electronic structure problems, namely, the variational and diffusion Monte Carlo methods. We give particular attention to the recent progress in the techniques for the optimization of the wave function, a challenging and important step to achieve accurate results in both the ground and the excited state. We conclude with an overview of the current status of excited-state calculations for molecular systems, demonstrating the potential of quantum Monte Carlo methods in this field of applications.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84161528","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}
We consider the performance of combined PNO-F12 approaches for the dissociation energy of water clusters as large as (H2O)20 by comparison to canonical CCSD(T)/CBS reference values obtained through n-body decomposition of post-MP2 corrections. We find that PNO-LCCSD(T)-F12b approaches with "Tight" cutoffs are generally capable of reproducing canonical CCSD(T) interaction energies to within ~0.25% and isomerization energies to ~1.5%, while requiring only a fraction of the canonical computational cost. However, basis set convergence patterns and effect of counterpoise corrections are more erratic than for canonical calculations, highlighting the need for canonical benchmarks on closely related systems.
{"title":"Energetics of (H2O)20 isomers by means of F12 canonical and localized coupled cluster methods","authors":"Nitai Sylvetsky, Jan M. L. Martin","doi":"10.1063/5.0049720","DOIUrl":"https://doi.org/10.1063/5.0049720","url":null,"abstract":"We consider the performance of combined PNO-F12 approaches for the dissociation energy of water clusters as large as (H2O)20 by comparison to canonical CCSD(T)/CBS reference values obtained through n-body decomposition of post-MP2 corrections. We find that PNO-LCCSD(T)-F12b approaches with \"Tight\" cutoffs are generally capable of reproducing canonical CCSD(T) interaction energies to within ~0.25% and isomerization energies to ~1.5%, while requiring only a fraction of the canonical computational cost. However, basis set convergence patterns and effect of counterpoise corrections are more erratic than for canonical calculations, highlighting the need for canonical benchmarks on closely related systems.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90805195","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：