This chapter aims at emphasizing the crucial role of partial reactivity of a catalytic surface or a target molecule in diffusion-controlled reactions. We discuss various microscopic mechanisms that lead to imperfect reactions, the Robin boundary condition accounting for eventual failed reaction events, and the construction of the underlying stochastic process, the so-called partially reflected Brownian motion. We show that the random path to the reaction event can naturally be separated into the transport step toward the target, and the exploration step near the target surface until reaction. While most studies are focused exclusively on the transport step (describing perfect reactions), the exploration step, consisting is an intricate combination of diffusion-mediated jumps between boundary points, and its consequences for chemical reactions remain poorly understood. We discuss the related mathematical difficulties and recent achievements. In particular , we derive a general representation of the propagator, show its relation to the Dirichlet-to-Neumann operator, and illustrate its properties in the case of a flat surface.
{"title":"Chemical Kinetics","authors":"Denis S. Grebenkov","doi":"10.1142/q0209","DOIUrl":"https://doi.org/10.1142/q0209","url":null,"abstract":"This chapter aims at emphasizing the crucial role of partial reactivity of a catalytic surface or a target molecule in diffusion-controlled reactions. We discuss various microscopic mechanisms that lead to imperfect reactions, the Robin boundary condition accounting for eventual failed reaction events, and the construction of the underlying stochastic process, the so-called partially reflected Brownian motion. We show that the random path to the reaction event can naturally be separated into the transport step toward the target, and the exploration step near the target surface until reaction. While most studies are focused exclusively on the transport step (describing perfect reactions), the exploration step, consisting is an intricate combination of diffusion-mediated jumps between boundary points, and its consequences for chemical reactions remain poorly understood. We discuss the related mathematical difficulties and recent achievements. In particular , we derive a general representation of the propagator, show its relation to the Dirichlet-to-Neumann operator, and illustrate its properties in the case of a flat surface.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78977795","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 report the observation and analysis of spectra in part of the near-infrared spectrum of C$_2$H, originating in rotational levels in the ground and lowest two excited bending vibrational levels of the ground $tilde{X},^2Sigma^+$ state. In the analysis, we have combined present and previously reported high resolution spectroscopic data for the lower levels involved in the transitions to determine significantly improved molecular constants to describe the fine and hyperfine split rotational levels of the radical in the zero point, $v_2=1$ and the $^2Sigma^+$ component of $v_2=2$. Two of the upper state vibronic levels involved had not been observed previously. The data and analysis indicate the electronic wavefunction character changes with bending vibrational excitation in the ground state and provide avenues for future measurements of reactivity of the radical as a function of vibrational excitation.
{"title":"Data for: Analysis of the $tilde{A}-tilde{X}$ bands of the Ethynyl Radical near 1.48$mu$m and Re-evaluation of $tilde{X}$ State Energies","authors":"T. Sears, G. Hall, Anh T. Le, Eisen C. Gross","doi":"10.17632/56HD9D2ZNK.1","DOIUrl":"https://doi.org/10.17632/56HD9D2ZNK.1","url":null,"abstract":"We report the observation and analysis of spectra in part of the near-infrared spectrum of C$_2$H, originating in rotational levels in the ground and lowest two excited bending vibrational levels of the ground $tilde{X},^2Sigma^+$ state. In the analysis, we have combined present and previously reported high resolution spectroscopic data for the lower levels involved in the transitions to determine significantly improved molecular constants to describe the fine and hyperfine split rotational levels of the radical in the zero point, $v_2=1$ and the $^2Sigma^+$ component of $v_2=2$. Two of the upper state vibronic levels involved had not been observed previously. The data and analysis indicate the electronic wavefunction character changes with bending vibrational excitation in the ground state and provide avenues for future measurements of reactivity of the radical as a function of vibrational excitation.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86251554","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 discuss the interrelations between various basis set extrapolation formulas and show that for the nZaPa and aug-cc-pVnZ basis set formulas, for n=4--6 their behavior closely resembles the Petersson (L+a)^{-3} formula with a shift a specific to the basis set family and level of theory. This is functionally equivalent to the Pansini-Varandas extrapolation for large L. This naturally leads to a simple way to extend these extrapolations to n=7 and higher. The formula is validated by comparison with newly optimized extrapolation factors for the AV{6,7}Z basis set pairs and literature values for {6,7}ZaPa. For Lgeq5, the CCSD extrapolations of both the Schwenke and Varandas type are functionally equivalent to E(L)=E_infty+A.(L-0.30)^{-3}, i.e., E(infty)=E(L)+[E(L)-E(L-1)]/([(L-0.30)/(L-1.30)]^3-1)
{"title":"A simple ‘range extender’ for basis set extrapolation methods for MP2 and coupled cluster correlation energies","authors":"Jan M. L. Martin","doi":"10.1063/1.5079050","DOIUrl":"https://doi.org/10.1063/1.5079050","url":null,"abstract":"We discuss the interrelations between various basis set extrapolation formulas and show that for the nZaPa and aug-cc-pVnZ basis set formulas, for n=4--6 their behavior closely resembles the Petersson (L+a)^{-3} formula with a shift a specific to the basis set family and level of theory. This is functionally equivalent to the Pansini-Varandas extrapolation for large L. This naturally leads to a simple way to extend these extrapolations to n=7 and higher. The formula is validated by comparison with newly optimized extrapolation factors for the AV{6,7}Z basis set pairs and literature values for {6,7}ZaPa. For Lgeq5, the CCSD extrapolations of both the Schwenke and Varandas type are functionally equivalent to E(L)=E_infty+A.(L-0.30)^{-3}, i.e., E(infty)=E(L)+[E(L)-E(L-1)]/([(L-0.30)/(L-1.30)]^3-1)","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87362653","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 : 2018-04-13DOI: 10.1142/S0219633618400072
Dilnoza Amirkulova, A. White
Experiment directed simulation is a technique to minimally bias molecular dynamics simulations to match experimentally observed results. The method improves accuracy but does not address the sampling problem of molecular dynamics simulations of large systems. This work combines experiment directed simulation with both the parallel-tempering and parallel-tempering well-tempered ensemble replica-exchange methods to enhance sampling of experiment directed simulations. These methods are demonstrated on the GYG tripeptide in explicit water. The collective variables biased by experiment directed simulation are chemical shifts, where the set-points are determined by NMR experiments. The results show that it is possible to enhance sampling with either parallel-tempering and parallel-tempering well-tempered ensemble in the experiment directed simulation method. This combination of methods provides a novel approach for both accurately and exhaustively simulating biological systems.
{"title":"Combining Enhanced Sampling with Experiment Directed Simulation of the GYG peptide","authors":"Dilnoza Amirkulova, A. White","doi":"10.1142/S0219633618400072","DOIUrl":"https://doi.org/10.1142/S0219633618400072","url":null,"abstract":"Experiment directed simulation is a technique to minimally bias molecular dynamics simulations to match experimentally observed results. The method improves accuracy but does not address the sampling problem of molecular dynamics simulations of large systems. This work combines experiment directed simulation with both the parallel-tempering and parallel-tempering well-tempered ensemble replica-exchange methods to enhance sampling of experiment directed simulations. These methods are demonstrated on the GYG tripeptide in explicit water. The collective variables biased by experiment directed simulation are chemical shifts, where the set-points are determined by NMR experiments. The results show that it is possible to enhance sampling with either parallel-tempering and parallel-tempering well-tempered ensemble in the experiment directed simulation method. This combination of methods provides a novel approach for both accurately and exhaustively simulating biological systems.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87859553","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}
N. Yoshikawa, Kei Terayama, T. Honma, Kenta Oono, Koji Tsuda
Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates. Current models, however, still have problems in simulation concurrency and molecular diversity. Most methods generate one molecule at a time and do not allow multiple simulators to run simultaneously. Additionally, better molecular diversity could boost the success rate in the subsequent drug discovery process. We propose a new population-based approach using grammatical evolution named ChemGE. In our method, a large population of molecules are updated concurrently and evaluated by multiple simulators in parallel. In docking experiments with thymidine kinase, ChemGE succeeded in generating hundreds of high-affinity molecules whose diversity is better than that of known inding molecules in DUD-E.
{"title":"Population-based de novo molecule generation, using grammatical evolution","authors":"N. Yoshikawa, Kei Terayama, T. Honma, Kenta Oono, Koji Tsuda","doi":"10.1246/cl.180665","DOIUrl":"https://doi.org/10.1246/cl.180665","url":null,"abstract":"Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates. Current models, however, still have problems in simulation concurrency and molecular diversity. Most methods generate one molecule at a time and do not allow multiple simulators to run simultaneously. Additionally, better molecular diversity could boost the success rate in the subsequent drug discovery process. We propose a new population-based approach using grammatical evolution named ChemGE. In our method, a large population of molecules are updated concurrently and evaluated by multiple simulators in parallel. In docking experiments with thymidine kinase, ChemGE succeeded in generating hundreds of high-affinity molecules whose diversity is better than that of known inding molecules in DUD-E.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82740242","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 implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matern kernel and the squared exponential kernel. The Matern kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the L-BFGS optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.
{"title":"Gaussian Process Regression for Geometry Optimization","authors":"A. Denzel, J. Kastner","doi":"10.1063/1.5017103","DOIUrl":"https://doi.org/10.1063/1.5017103","url":null,"abstract":"We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matern kernel and the squared exponential kernel. The Matern kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the L-BFGS optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84222265","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}
Artificial neural networks are used to fit a potential energy surface. We demonstrate the benefits of using not only energies, but also their first and second derivatives as training data for the neural network. This ensures smooth and accurate Hessian surfaces, which are required for rate constant calculations using instanton theory. Our aim was a local, accurate fit rather than a global PES, because instanton theory requires information on the potential only in the close vicinity of the main tunneling path. Elongations along vibrational normal modes at the transition state are used as coordinates for the neural network. The method is applied to the hydrogen abstraction reaction from methanol, calculated on a coupled-cluster level of theory. The reaction is essential in astrochemistry to explain the deuteration of methanol in the interstellar medium.
{"title":"Potential energy surface interpolation with neural networks for instanton rate calculations","authors":"April M. Cooper, Philipp P. Hallmen, J. Kastner","doi":"10.1063/1.5015950","DOIUrl":"https://doi.org/10.1063/1.5015950","url":null,"abstract":"Artificial neural networks are used to fit a potential energy surface. We demonstrate the benefits of using not only energies, but also their first and second derivatives as training data for the neural network. This ensures smooth and accurate Hessian surfaces, which are required for rate constant calculations using instanton theory. Our aim was a local, accurate fit rather than a global PES, because instanton theory requires information on the potential only in the close vicinity of the main tunneling path. Elongations along vibrational normal modes at the transition state are used as coordinates for the neural network. The method is applied to the hydrogen abstraction reaction from methanol, calculated on a coupled-cluster level of theory. The reaction is essential in astrochemistry to explain the deuteration of methanol in the interstellar medium.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"154 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79765288","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 : 2018-02-08DOI: 10.1109/BIYOMUT.2016.7849376
M. Şen
In this study, we investigated the ionic current rectification of glass nanopipettes modified with bovine serum albumin - glutaraldehyde (BSA-GA) artificial membrane using solutions with various pHs. Ionic current rectification is a phenomenon that is observed with nanopores as asymmetric I-V curves, where the ionic currents recorded through a nanopore differ at the same magnitude of applied electrical potentials biased with opposite polarities. The results clearly showed that modifying the tip of a nanopipette results in a pH dependent ionic current behavior. The proposed strategy is a facile method for fabrication of a pH responsive nanoprobe that has a potential for intracellular pH measurement.
{"title":"Fabrication and characterization of pH responsive nanoprobes based on ion current rectification","authors":"M. Şen","doi":"10.1109/BIYOMUT.2016.7849376","DOIUrl":"https://doi.org/10.1109/BIYOMUT.2016.7849376","url":null,"abstract":"In this study, we investigated the ionic current rectification of glass nanopipettes modified with bovine serum albumin - glutaraldehyde (BSA-GA) artificial membrane using solutions with various pHs. Ionic current rectification is a phenomenon that is observed with nanopores as asymmetric I-V curves, where the ionic currents recorded through a nanopore differ at the same magnitude of applied electrical potentials biased with opposite polarities. The results clearly showed that modifying the tip of a nanopipette results in a pH dependent ionic current behavior. The proposed strategy is a facile method for fabrication of a pH responsive nanoprobe that has a potential for intracellular pH measurement.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74316373","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}
Sona Saitou, J. Iijima, M. Fujimoto, Y. Mochizuki, Koji Okuwaki, H. Doi, Y. Komeiji
We have applied Google's TensorFlow deep learning toolkit to recognize the visualized results of the fragment molecular orbital (FMO) calculations. Typical protein structures of alpha-helix and beta-sheet provide some characteristic patterns in the two-dimensional map of inter-fragment interaction energy termed as IFIE-map (Kurisaki et al., Biophys. Chem. 130 (2007) 1). A thousand of IFIE-map images with labels depending on the existences of alpha-helix and beta-sheet were prepared by employing 18 proteins and 3 non-protein systems and were subjected to training by TensorFlow. Finally, TensorFlow was fed with new data to test its ability to recognize the structural patterns. We found that the characteristic structures in test IFIE-map images were judged successfully. Thus the ability of pattern recognition of IFIE-map by TensorFlow was proven.
我们应用b谷歌的TensorFlow深度学习工具包来识别片段分子轨道(FMO)计算的可视化结果。典型的α -螺旋和β -片的蛋白质结构在片段间相互作用能的二维图中提供了一些特征模式，称为IFIE-map (Kurisaki et al.， Biophys。化学，130(2007)1)。使用18种蛋白质和3种非蛋白质系统制备了1000张IFIE-map图像，这些图像的标签取决于α -螺旋和β -sheet的存在，并接受了TensorFlow的训练。最后，向TensorFlow输入新数据以测试其识别结构模式的能力。结果表明，该方法能够很好地判断出IFIE-map测试图像中的特征结构。从而证明了TensorFlow对IFIE-map的模式识别能力。
{"title":"Application of TensorFlow to recognition of visualized results of fragment molecular orbital (FMO) calculations","authors":"Sona Saitou, J. Iijima, M. Fujimoto, Y. Mochizuki, Koji Okuwaki, H. Doi, Y. Komeiji","doi":"10.1273/CBIJ.18.58","DOIUrl":"https://doi.org/10.1273/CBIJ.18.58","url":null,"abstract":"We have applied Google's TensorFlow deep learning toolkit to recognize the visualized results of the fragment molecular orbital (FMO) calculations. Typical protein structures of alpha-helix and beta-sheet provide some characteristic patterns in the two-dimensional map of inter-fragment interaction energy termed as IFIE-map (Kurisaki et al., Biophys. Chem. 130 (2007) 1). A thousand of IFIE-map images with labels depending on the existences of alpha-helix and beta-sheet were prepared by employing 18 proteins and 3 non-protein systems and were subjected to training by TensorFlow. Finally, TensorFlow was fed with new data to test its ability to recognize the structural patterns. We found that the characteristic structures in test IFIE-map images were judged successfully. Thus the ability of pattern recognition of IFIE-map by TensorFlow was proven.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75726411","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 : 2017-12-20DOI: 10.5772/intechopen.70688
T. Etienne
This chapter gives an introduction to qualitative and quantitative topological analyses of molecular electronic transitions. Among the possibilities for qualitatively describing how the electronic structure of a molecule is reorganized upon light-absorption, we chose to detail two of them, namely the detachment/attachment density matrix analysis and the natural transition orbitals strategy. While these tools are often introduced separately, we decided to formally detail the connection existing between the two paradigms in the case of excited states calculation methods expressing any excited state as a linear combination of singly excited Slater determinants, written based on a single-reference ground state wave function. In this context, we show how the molecular exciton wave function plays a central role in the topological analysis of the electronic transition process.
{"title":"Theoretical Insights into the Topology of Molecular Excitons from Single-Reference Excited States Calculation Methods","authors":"T. Etienne","doi":"10.5772/intechopen.70688","DOIUrl":"https://doi.org/10.5772/intechopen.70688","url":null,"abstract":"This chapter gives an introduction to qualitative and quantitative topological analyses of molecular electronic transitions. Among the possibilities for qualitatively describing how the electronic structure of a molecule is reorganized upon light-absorption, we chose to detail two of them, namely the detachment/attachment density matrix analysis and the natural transition orbitals strategy. While these tools are often introduced separately, we decided to formally detail the connection existing between the two paradigms in the case of excited states calculation methods expressing any excited state as a linear combination of singly excited Slater determinants, written based on a single-reference ground state wave function. In this context, we show how the molecular exciton wave function plays a central role in the topological analysis of the electronic transition process.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90419639","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}
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