Venkata Rohit Punyapu, Jiazhou Zhu, Paul Meza-Morales, Anish Chaluvadi, O. Thompson Mefford, Rachel B. Getman
A grand challenge in materials research is identifying the relationship between composition and performance. Herein, we explore this relationship for magnetic properties, specifically magnetic saturation (M$_s$) and magnetic anisotropy energy (MAE) of ferrites. Ferrites are materials derived from magnetite (which has the chemical formulae Fe$_3$O$_4$) that comprise metallic elements in some combination such as Fe, Mn, Ni, Co, Cu and Zn. They are used in a variety of applications such as electromagnetism, magnetic hyperthermia, and magnetic imaging. Experimentally, synthesis and characterization of magnetic materials is time consuming. In order to create insight to help guide synthesis, we compute the relationship between ferrite composition and magnetic properties using density functional theory (DFT). Specifically, we compute M$_s$ and MAE for 571 ferrite structures with the formulae M1$_x$M2$_y$Fe$_{3-x-y}$O$_4$, where M1 and M2 can be Mn, Ni, Co, Cu and/or Zn and 0 $le$ x $le$ 1 and y = 1 - x. By varying composition, we were able to vary calculated values of M$_s$ and MAE by up to 9.6$times$10$^5$ A m$^{-1}$ and 14.1$times$10$^5$ J m$^{-3}$, respectively. Our results suggest that composition can be used to optimize magnetic properties for applications in heating, imaging, and recording. This is mainly achieved by varying M$_s$, as these applications are more sensitive to variation in M$_s$ than MAE.
{"title":"Computational Exploration of Magnetic Saturation and Anisotropy Energy for Nonstoichiometric Ferrite Compositions","authors":"Venkata Rohit Punyapu, Jiazhou Zhu, Paul Meza-Morales, Anish Chaluvadi, O. Thompson Mefford, Rachel B. Getman","doi":"arxiv-2309.09754","DOIUrl":"https://doi.org/arxiv-2309.09754","url":null,"abstract":"A grand challenge in materials research is identifying the relationship\u0000between composition and performance. Herein, we explore this relationship for\u0000magnetic properties, specifically magnetic saturation (M$_s$) and magnetic\u0000anisotropy energy (MAE) of ferrites. Ferrites are materials derived from\u0000magnetite (which has the chemical formulae Fe$_3$O$_4$) that comprise metallic\u0000elements in some combination such as Fe, Mn, Ni, Co, Cu and Zn. They are used\u0000in a variety of applications such as electromagnetism, magnetic hyperthermia,\u0000and magnetic imaging. Experimentally, synthesis and characterization of\u0000magnetic materials is time consuming. In order to create insight to help guide\u0000synthesis, we compute the relationship between ferrite composition and magnetic\u0000properties using density functional theory (DFT). Specifically, we compute\u0000M$_s$ and MAE for 571 ferrite structures with the formulae\u0000M1$_x$M2$_y$Fe$_{3-x-y}$O$_4$, where M1 and M2 can be Mn, Ni, Co, Cu and/or Zn\u0000and 0 $le$ x $le$ 1 and y = 1 - x. By varying composition, we were able to\u0000vary calculated values of M$_s$ and MAE by up to 9.6$times$10$^5$ A m$^{-1}$\u0000and 14.1$times$10$^5$ J m$^{-3}$, respectively. Our results suggest that\u0000composition can be used to optimize magnetic properties for applications in\u0000heating, imaging, and recording. This is mainly achieved by varying M$_s$, as\u0000these applications are more sensitive to variation in M$_s$ than MAE.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522552","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}
Shokirbek Shermukhamedov, Dilorom Mamurjonova, Michael Probst
The application of machine learning (ML) techniques in computational chemistry has led to significant advances in predicting molecular properties, accelerating drug discovery, and material design. ML models can extract hidden patterns and relationships from complex and large datasets, allowing for the prediction of various chemical properties with high accuracy. The use of such methods has enabled the discovery of molecules and materials that were previously difficult to identify. This paper introduces a new ML model based on deep learning techniques, such as a multilayer encoder and decoder architecture, for classification tasks. We demonstrate the opportunities offered by our approach by applying it to various types of input data, including organic and inorganic compounds. In particular, we developed and tested the model using the Matbench and Moleculenet benchmarks, which include crystal properties and drug design-related benchmarks. We also conduct a comprehensive analysis of vector representations of chemical compounds, shedding light on the underlying patterns in molecular data. The models used in this work exhibit a high degree of predictive power, underscoring the progress that can be made with refined machine learning when applied to molecular and material datasets. For instance, on the Tox21 dataset, we achieved an average accuracy of 96%, surpassing the previous best result by 10%. Our code is publicly available at https://github.com/dmamur/elembert.
{"title":"Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties","authors":"Shokirbek Shermukhamedov, Dilorom Mamurjonova, Michael Probst","doi":"arxiv-2309.09355","DOIUrl":"https://doi.org/arxiv-2309.09355","url":null,"abstract":"The application of machine learning (ML) techniques in computational\u0000chemistry has led to significant advances in predicting molecular properties,\u0000accelerating drug discovery, and material design. ML models can extract hidden\u0000patterns and relationships from complex and large datasets, allowing for the\u0000prediction of various chemical properties with high accuracy. The use of such\u0000methods has enabled the discovery of molecules and materials that were\u0000previously difficult to identify. This paper introduces a new ML model based on\u0000deep learning techniques, such as a multilayer encoder and decoder\u0000architecture, for classification tasks. We demonstrate the opportunities\u0000offered by our approach by applying it to various types of input data,\u0000including organic and inorganic compounds. In particular, we developed and\u0000tested the model using the Matbench and Moleculenet benchmarks, which include\u0000crystal properties and drug design-related benchmarks. We also conduct a\u0000comprehensive analysis of vector representations of chemical compounds,\u0000shedding light on the underlying patterns in molecular data. The models used in\u0000this work exhibit a high degree of predictive power, underscoring the progress\u0000that can be made with refined machine learning when applied to molecular and\u0000material datasets. For instance, on the Tox21 dataset, we achieved an average\u0000accuracy of 96%, surpassing the previous best result by 10%. Our code is\u0000publicly available at https://github.com/dmamur/elembert.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522624","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}
Relativistic magnetic hyperfine interaction Hamiltonian based on the Douglas-Kroll-Hess (DKH) theory up to the second order is implemented within the ab initio multireference methods including spin-orbit coupling in the Molcas/OpenMolcas package. This implementation is applied to calculate relativistic hyperfine coupling (HFC) parameters for atomic systems and diatomic radicals with valence s or d orbitals by systematically varying active space size in the restricted active space self-consistent field (RASSCF) formalism with restricted active space state interaction (RASSI) for spin-orbit coupling. The DKH relativistic treatment of the hyperfine interaction reduces the Fermi contact contribution to the HFC due to the presence of kinetic factors that regularize the singularity of the Dirac delta function in the nonrelativitic Fermi contact operator. This effect is more prominent for heavier nuclei. As the active space size increases, the relativistic correction of the Fermi contact contribution converges well to the experimental data for light and moderately heavy nuclei. The relativistic correction, however, does not significantly affect the spin-dipole contribution to the hyperfine interaction. In addition to the atomic and molecular systems, the implementation is applied to calculate the relativistic HFC parameters for large trivalent and divalent Tb-based single-molecule magnets (SMMs) such as Tb(III)Pc$_2$ and Tb(II)(Cp$^text{iPr5}$)$_2$ without ligand truncation using well-converged basis sets. In particular, for the divalent SMM which has an unpaired valence 6s/5d hybrid orbital, the relativistic treatment of HFC is crucial for a proper description of the Fermi contact contribution. Even with the relativistic hyperfine Hamiltonian, the divalent SMM is shown to exhibit strong tunability of HFC via an external electric field (i.e., strong hyperfine Stark effect).
{"title":"Relativistic Douglas-Kroll-Hess Calculations of Hyperfine Interactions within First Principles Multireference Methods","authors":"Aleksander L. Wysocki, Kyungwha Park","doi":"arxiv-2309.09349","DOIUrl":"https://doi.org/arxiv-2309.09349","url":null,"abstract":"Relativistic magnetic hyperfine interaction Hamiltonian based on the\u0000Douglas-Kroll-Hess (DKH) theory up to the second order is implemented within\u0000the ab initio multireference methods including spin-orbit coupling in the\u0000Molcas/OpenMolcas package. This implementation is applied to calculate\u0000relativistic hyperfine coupling (HFC) parameters for atomic systems and\u0000diatomic radicals with valence s or d orbitals by systematically varying active\u0000space size in the restricted active space self-consistent field (RASSCF)\u0000formalism with restricted active space state interaction (RASSI) for spin-orbit\u0000coupling. The DKH relativistic treatment of the hyperfine interaction reduces\u0000the Fermi contact contribution to the HFC due to the presence of kinetic\u0000factors that regularize the singularity of the Dirac delta function in the\u0000nonrelativitic Fermi contact operator. This effect is more prominent for\u0000heavier nuclei. As the active space size increases, the relativistic correction\u0000of the Fermi contact contribution converges well to the experimental data for\u0000light and moderately heavy nuclei. The relativistic correction, however, does\u0000not significantly affect the spin-dipole contribution to the hyperfine\u0000interaction. In addition to the atomic and molecular systems, the\u0000implementation is applied to calculate the relativistic HFC parameters for\u0000large trivalent and divalent Tb-based single-molecule magnets (SMMs) such as\u0000Tb(III)Pc$_2$ and Tb(II)(Cp$^text{iPr5}$)$_2$ without ligand truncation using\u0000well-converged basis sets. In particular, for the divalent SMM which has an\u0000unpaired valence 6s/5d hybrid orbital, the relativistic treatment of HFC is\u0000crucial for a proper description of the Fermi contact contribution. Even with\u0000the relativistic hyperfine Hamiltonian, the divalent SMM is shown to exhibit\u0000strong tunability of HFC via an external electric field (i.e., strong hyperfine\u0000Stark effect).","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522550","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}
Balasaheb J. Nagare, Sajeev Chacko, Dilip. G. Kanhere
Gaussian Process Regression-based Gaussian Approximation Potential has been used to develop machine-learned interatomic potentials having density-functional accuracy for free sodium clusters. The training data was generated from a large sample of over 100,000 data points computed for clusters in the size range of N = 40 - 200, using the density-functional method as implemented in the VASP package. Two models have been developed, model M1 using data for N=55 only, and model M2 using additional data from larger clusters. The models are intended for computing thermodynamic properties using molecular dynamics. Hence, particular attention has been paid to improve the fitting of the forces. Interestingly, it turns out that the best fit can be obtained by carefully selecting a smaller number of data points viz. 1,900 and 1,300 configurations, respectively, for the two models M1 and M2. Although it was possible to obtain a good fit using the data of Na55 only, additional data points from larger clusters were needed to get better accuracies in energies and forces for larger sizes. Surprisingly, the model M1 could be significantly improved by adding about 50 data points per cluster from the larger sizes. Both models have been deployed to compute the heat capacities of Na55 and Na147 and to obtain about 40 isomers for larger clusters of sizes N = 147, 200, 201, and 252. There is an excellent agreement between the computed and experimentally measured melting temperatures. The geometries of these isomers when further optimized by DFT, the mean absolute error in the energies between DFT results and those of our models is about 7 meV/atom or less. The errors in the interatomic bond lengths are estimated to be below 2% in almost all the cases.
{"title":"Machine-Learned Potential Energy Surfaces for Free Sodium Clusters with Density Functional Accuracy: Applications to Melting","authors":"Balasaheb J. Nagare, Sajeev Chacko, Dilip. G. Kanhere","doi":"arxiv-2309.08937","DOIUrl":"https://doi.org/arxiv-2309.08937","url":null,"abstract":"Gaussian Process Regression-based Gaussian Approximation Potential has been\u0000used to develop machine-learned interatomic potentials having\u0000density-functional accuracy for free sodium clusters. The training data was\u0000generated from a large sample of over 100,000 data points computed for clusters\u0000in the size range of N = 40 - 200, using the density-functional method as\u0000implemented in the VASP package. Two models have been developed, model M1 using\u0000data for N=55 only, and model M2 using additional data from larger clusters.\u0000The models are intended for computing thermodynamic properties using molecular\u0000dynamics. Hence, particular attention has been paid to improve the fitting of\u0000the forces. Interestingly, it turns out that the best fit can be obtained by\u0000carefully selecting a smaller number of data points viz. 1,900 and 1,300\u0000configurations, respectively, for the two models M1 and M2. Although it was\u0000possible to obtain a good fit using the data of Na55 only, additional data\u0000points from larger clusters were needed to get better accuracies in energies\u0000and forces for larger sizes. Surprisingly, the model M1 could be significantly\u0000improved by adding about 50 data points per cluster from the larger sizes. Both\u0000models have been deployed to compute the heat capacities of Na55 and Na147 and\u0000to obtain about 40 isomers for larger clusters of sizes N = 147, 200, 201, and\u0000252. There is an excellent agreement between the computed and experimentally\u0000measured melting temperatures. The geometries of these isomers when further\u0000optimized by DFT, the mean absolute error in the energies between DFT results\u0000and those of our models is about 7 meV/atom or less. The errors in the\u0000interatomic bond lengths are estimated to be below 2% in almost all the cases.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522616","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}
Holger S. P. Müller, Atsuko Maeda, Frank Lewen, Stephan Schlemmer, Ivan R. Medvedev, Eric Herbst
An investigation of the rotational spectrum of the interstellar molecule thioformaldehyde between 110 and 377 GHz through a pyrolysis reaction revealed a multitude of absorption lines assignable to H$_2$CS and H$_2$C$^{34}$S in their lowest four excited vibrational states besides lines of numerous thioformaldehyde isotopologues in their ground vibrational states reported earlier as well as lines pertaining to several by-products. Additional transitions of H$_2$CS in its lowest four excited vibrational states were recorded in selected regions between 571 and 1386 GHz. Slight to strong Coriolis interactions occur between all four vibrational states with the exception of the two highest lying states because both are totally symmetric vibrations. We present combined analyses of the ground and the four interacting states for our rotational data of H$_2$CS and H$_2$C$^{34}$S. The H$_2$CS data were supplemented with two sets of high-resultion IR data in two separate analyses. The $v_2 = 1$ state has been included in analyses of Coriolis interactions of low-lying fundamental states of H$_2$CS for the first time and this improved the quality of the fits substantially. We extended furthermore assignments in $J$ of transition frequencies of thioketene in its ground vibrational state.
{"title":"Rotational spectroscopy of the thioformaldehyde isotopologues H$_2$CS and H$_2$C$^{34}$S in four interacting excited vibrational states and an account on the rotational spectrum of thioketene, H$_2$CCS","authors":"Holger S. P. Müller, Atsuko Maeda, Frank Lewen, Stephan Schlemmer, Ivan R. Medvedev, Eric Herbst","doi":"arxiv-2309.08992","DOIUrl":"https://doi.org/arxiv-2309.08992","url":null,"abstract":"An investigation of the rotational spectrum of the interstellar molecule\u0000thioformaldehyde between 110 and 377 GHz through a pyrolysis reaction revealed\u0000a multitude of absorption lines assignable to H$_2$CS and H$_2$C$^{34}$S in\u0000their lowest four excited vibrational states besides lines of numerous\u0000thioformaldehyde isotopologues in their ground vibrational states reported\u0000earlier as well as lines pertaining to several by-products. Additional\u0000transitions of H$_2$CS in its lowest four excited vibrational states were\u0000recorded in selected regions between 571 and 1386 GHz. Slight to strong\u0000Coriolis interactions occur between all four vibrational states with the\u0000exception of the two highest lying states because both are totally symmetric\u0000vibrations. We present combined analyses of the ground and the four interacting\u0000states for our rotational data of H$_2$CS and H$_2$C$^{34}$S. The H$_2$CS data\u0000were supplemented with two sets of high-resultion IR data in two separate\u0000analyses. The $v_2 = 1$ state has been included in analyses of Coriolis\u0000interactions of low-lying fundamental states of H$_2$CS for the first time and\u0000this improved the quality of the fits substantially. We extended furthermore\u0000assignments in $J$ of transition frequencies of thioketene in its ground\u0000vibrational state.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522549","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}
Jianzhi Xu, Joost M. Bakker, Olga V. Lushchikova, Peter Lievens, Ewald Janssens, Gao-Lei Hou
Buckminsterfullerene C60 has received extensive research interest ever since its discovery. In addition to its interesting intrinsic properties of exceptional stability and electron-accepting ability, the broad chemical tunability by decoration or substitution on the C60-fullerene surface makes it a fascinating molecule. However, to date there is uncertainty about the binding location of such decorations on the C60 surface, even for a single adsorbed metal atom. In this work, we report the gas-phase synthesis of the C60V+ complex and its in-situ characterization by mass spectrometry and in-frared spectroscopy with the help of quantum chemical calculations and molecular dynamics simulations. We identify the most probable binding position of a vanadium cation on C60 above a pentagon center in eta5-fashion, demonstrate a high thermal stability for this complex, and explore the bonding nature between C60 and the vanadium cation, reveal-ing that large orbital and electrostatic interactions lie at the origin of the stability of the eta5-C60V+ complex.
{"title":"Pentagon, Hexagon, or Bridge? Identifying the Location of a Single Vanadium Cation on Buckminsterfullerene Surface","authors":"Jianzhi Xu, Joost M. Bakker, Olga V. Lushchikova, Peter Lievens, Ewald Janssens, Gao-Lei Hou","doi":"arxiv-2309.05890","DOIUrl":"https://doi.org/arxiv-2309.05890","url":null,"abstract":"Buckminsterfullerene C60 has received extensive research interest ever since\u0000its discovery. In addition to its interesting intrinsic properties of\u0000exceptional stability and electron-accepting ability, the broad chemical\u0000tunability by decoration or substitution on the C60-fullerene surface makes it\u0000a fascinating molecule. However, to date there is uncertainty about the binding\u0000location of such decorations on the C60 surface, even for a single adsorbed\u0000metal atom. In this work, we report the gas-phase synthesis of the C60V+\u0000complex and its in-situ characterization by mass spectrometry and in-frared\u0000spectroscopy with the help of quantum chemical calculations and molecular\u0000dynamics simulations. We identify the most probable binding position of a\u0000vanadium cation on C60 above a pentagon center in eta5-fashion, demonstrate a\u0000high thermal stability for this complex, and explore the bonding nature between\u0000C60 and the vanadium cation, reveal-ing that large orbital and electrostatic\u0000interactions lie at the origin of the stability of the eta5-C60V+ complex.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"17 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522544","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}
Understanding the impact of extractant functionalization in solvent extraction is essential to guide the development of better separations processes. Traditionally, computational extractant design uses electronic structure calculations to determine the metal binding energy of the lowest energy state. Although highly accurate, this approach does not account for all the relevant physics encountered under experimental conditions, such as temperature effects and ligand flexibility, in addition to approximating solvent-extractant interactions with implicit solvent models. In this study, we use classical MD simulations with an advanced sampling method, metadynamics, to map out extractant molecule conformational free energies in the condensed phase. We generate the complete conformational landscape in solution for a family of bidentate malonamide-based extractants with different functionalizations of the head group and the side chains. In particular, we show how such alkyl functionalization reshapes the free energy landscape, affecting the free energy penalty of organizing the extractant into the cis-like metal binding conformation from the trans-like conformation of the free extractant in solution. Specifically, functionalizing alkyl tails to the center of the head group has a greater influence on increasing molecular rigidity and disfavoring the binding conformation than functionalizing side chains. These findings are consistent with trends in metal binding energetics based on experimentally reported distribution ratios. This study demonstrates the feasibility of using molecular dynamics simulations with advance sampling techniques to investigate extractant conformational energetics in solution, which, more broadly, will enable extractant design that accounts for entropic effects and explicit solvation.
{"title":"Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes","authors":"Xiaoyu Wang, Michael J. Servis","doi":"arxiv-2309.06400","DOIUrl":"https://doi.org/arxiv-2309.06400","url":null,"abstract":"Understanding the impact of extractant functionalization in solvent\u0000extraction is essential to guide the development of better separations\u0000processes. Traditionally, computational extractant design uses electronic\u0000structure calculations to determine the metal binding energy of the lowest\u0000energy state. Although highly accurate, this approach does not account for all\u0000the relevant physics encountered under experimental conditions, such as\u0000temperature effects and ligand flexibility, in addition to approximating\u0000solvent-extractant interactions with implicit solvent models. In this study, we\u0000use classical MD simulations with an advanced sampling method, metadynamics, to\u0000map out extractant molecule conformational free energies in the condensed\u0000phase. We generate the complete conformational landscape in solution for a\u0000family of bidentate malonamide-based extractants with different\u0000functionalizations of the head group and the side chains. In particular, we\u0000show how such alkyl functionalization reshapes the free energy landscape,\u0000affecting the free energy penalty of organizing the extractant into the\u0000cis-like metal binding conformation from the trans-like conformation of the\u0000free extractant in solution. Specifically, functionalizing alkyl tails to the\u0000center of the head group has a greater influence on increasing molecular\u0000rigidity and disfavoring the binding conformation than functionalizing side\u0000chains. These findings are consistent with trends in metal binding energetics\u0000based on experimentally reported distribution ratios. This study demonstrates\u0000the feasibility of using molecular dynamics simulations with advance sampling\u0000techniques to investigate extractant conformational energetics in solution,\u0000which, more broadly, will enable extractant design that accounts for entropic\u0000effects and explicit solvation.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522627","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}
Mohammad Goli, Nahid Sadat Riyahi, Shant Shahbazian
In [Phys. Rev. B 107, 094433 (2023)], Deng et al. have proposed an electron-muon correlation functional within the context of the two-component density functional theory (TC-DFT) for crystals/molecules containing positively charged muons. In order to verify its performance, we applied the functional in conjunction with the B3LYP, as a hybrid electronic exchange-correlation functional, to a benchmark set of molecules. The results demonstrate that the proposed functional is not capable of reproducing the correct one-muon densities as well as some other key properties like muon's kinetic energy, the total energies and the mean muonic bond lengths. Using the muonium atom in a double-harmonic trap as a model we also demonstrate that the successful reproduction of the electron-muon contact hyperfine coupling constants by Deng et al. is probably the result of error cancellations. We also discuss some theoretical intricacies with the very definition of the electron-muon correlation energy within the context of the TC-DFT that must be taken into account in future efforts to design electron-muon correlation functionals.
{"title":"Comment on \"Two-component density functional theory study of quantized muons in solids\"","authors":"Mohammad Goli, Nahid Sadat Riyahi, Shant Shahbazian","doi":"arxiv-2309.03345","DOIUrl":"https://doi.org/arxiv-2309.03345","url":null,"abstract":"In [Phys. Rev. B 107, 094433 (2023)], Deng et al. have proposed an\u0000electron-muon correlation functional within the context of the two-component\u0000density functional theory (TC-DFT) for crystals/molecules containing positively\u0000charged muons. In order to verify its performance, we applied the functional in\u0000conjunction with the B3LYP, as a hybrid electronic exchange-correlation\u0000functional, to a benchmark set of molecules. The results demonstrate that the\u0000proposed functional is not capable of reproducing the correct one-muon\u0000densities as well as some other key properties like muon's kinetic energy, the\u0000total energies and the mean muonic bond lengths. Using the muonium atom in a\u0000double-harmonic trap as a model we also demonstrate that the successful\u0000reproduction of the electron-muon contact hyperfine coupling constants by Deng\u0000et al. is probably the result of error cancellations. We also discuss some\u0000theoretical intricacies with the very definition of the electron-muon\u0000correlation energy within the context of the TC-DFT that must be taken into\u0000account in future efforts to design electron-muon correlation functionals.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522541","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}
Wenkai Wu, Alexey V. Verkhovtsev, Theodoros Pavloudis, Andrey V. Solov'yov, Richard E. Palmer
Atomic cluster-based networks represent a promising architecture for the realization of neuromorphic computing systems, which may overcome some of the limitations of the current computing paradigm. The formation and breakage of synapses between the clusters are of utmost importance for the functioning of these computing systems. This paper reports the results of molecular dynamics simulations of synapse (bridge) formation at elevated temperatures and thermal breaking processes between 2.8 nanometer-sized Au$_{1415}$ clusters deposited on a carbon substrate, a model system. Crucially, we find that the bridge formation process is driven by the diffusion of gold atoms along the substrate, however small the gap between the clusters themselves. The complementary simulations of the bridge-breaking process reveal the existence of a threshold bias voltage to activate bridge rupture via Joule heating. These results provide an atomistic-level understanding of the fundamental dynamical processes occurring in neuromorphic cluster arrays.
{"title":"Neuromorphic nanocluster networks: Critical role of the substrate in nano-link formation","authors":"Wenkai Wu, Alexey V. Verkhovtsev, Theodoros Pavloudis, Andrey V. Solov'yov, Richard E. Palmer","doi":"arxiv-2309.02299","DOIUrl":"https://doi.org/arxiv-2309.02299","url":null,"abstract":"Atomic cluster-based networks represent a promising architecture for the\u0000realization of neuromorphic computing systems, which may overcome some of the\u0000limitations of the current computing paradigm. The formation and breakage of\u0000synapses between the clusters are of utmost importance for the functioning of\u0000these computing systems. This paper reports the results of molecular dynamics\u0000simulations of synapse (bridge) formation at elevated temperatures and thermal\u0000breaking processes between 2.8 nanometer-sized Au$_{1415}$ clusters deposited\u0000on a carbon substrate, a model system. Crucially, we find that the bridge\u0000formation process is driven by the diffusion of gold atoms along the substrate,\u0000however small the gap between the clusters themselves. The complementary\u0000simulations of the bridge-breaking process reveal the existence of a threshold\u0000bias voltage to activate bridge rupture via Joule heating. These results\u0000provide an atomistic-level understanding of the fundamental dynamical processes\u0000occurring in neuromorphic cluster arrays.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522542","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}
Matthew D. Dickers, Alexey V. Verkhovtsev, Nigel J. Mason, Andrey V. Solov'yov
This study presents the results of atomistic structural characterisation of 3.7 nm diameter gold nanoparticles (NP) coated with polymer polyethylene glycol (PEG)-based ligands of different lengths (containing $2-14$ monomers) and solvated in water. The system size and composition are selected in connection to several experimental studies of radiosensitisation mechanisms of gold NPs. The coating structure and water distribution near the NP surface are characterised on the atomistic level by means of molecular dynamics simulations. The results of simulations carried out in this study, combined with the results of our recent study [J. Phys. Chem. A 126 (2022) 2170] and those from the field of polymer physics, are used to calculate key structural parameters of the coatings of radiosensitising gold NPs. On this basis, connections between the coating structure and distribution of water are established for different NP sizes as well as lengths and surface densities of coating molecules. The quantitative analysis of water distribution in the vicinity of coated metal NPs can be used to evaluate the radiosensitising effectiveness of a particular NP system based on the proximity of water to the NP metal core, which should impact the production of hydroxyl radicals and reactive oxygen species in the vicinity of metal NPs exposed to ionising radiation.
{"title":"Atomistic modelling and structural characterisation of coated gold nanoparticles for biomedical applications","authors":"Matthew D. Dickers, Alexey V. Verkhovtsev, Nigel J. Mason, Andrey V. Solov'yov","doi":"arxiv-2309.02541","DOIUrl":"https://doi.org/arxiv-2309.02541","url":null,"abstract":"This study presents the results of atomistic structural characterisation of\u00003.7 nm diameter gold nanoparticles (NP) coated with polymer polyethylene glycol\u0000(PEG)-based ligands of different lengths (containing $2-14$ monomers) and\u0000solvated in water. The system size and composition are selected in connection\u0000to several experimental studies of radiosensitisation mechanisms of gold NPs.\u0000The coating structure and water distribution near the NP surface are\u0000characterised on the atomistic level by means of molecular dynamics\u0000simulations. The results of simulations carried out in this study, combined\u0000with the results of our recent study [J. Phys. Chem. A 126 (2022) 2170] and\u0000those from the field of polymer physics, are used to calculate key structural\u0000parameters of the coatings of radiosensitising gold NPs. On this basis,\u0000connections between the coating structure and distribution of water are\u0000established for different NP sizes as well as lengths and surface densities of\u0000coating molecules. The quantitative analysis of water distribution in the\u0000vicinity of coated metal NPs can be used to evaluate the radiosensitising\u0000effectiveness of a particular NP system based on the proximity of water to the\u0000NP metal core, which should impact the production of hydroxyl radicals and\u0000reactive oxygen species in the vicinity of metal NPs exposed to ionising\u0000radiation.","PeriodicalId":501259,"journal":{"name":"arXiv - PHYS - Atomic and Molecular Clusters","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522727","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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