cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:3f-cy
title: Tight-binding calculation files of modulated Dirac bands and integer hopping ratios in a honeycomb lattice of phenalenyl-tessellation molecules

description: The dataset uploaded here records atomic positions, lattices vectors and Python scripts for tight-binding calculations related to the paper entitled "Modulated Dirac bands and integer hopping ratios in a honeycomb lattice of phenalenyl-tessellation molecules." In a family of nanographene called phenalenyl-tessellation molecules (PTMs), two types of zero modes appear: one is uniformly extended over the entire molecule and the other is localized around vacancies. Therefore, it is expected that energy bands reflecting the properties of these two types of zero modes will appear in a honeycomb PTM (H-PTM). In this study, we show that modulated energy gap and the Fermi velocity of Dirac bands appear in H-PTMs and that the effective two-site model has positive integer hopping ratios based on the uniformly extended zero mode and  the number of connections between PTMs. Furthermore, we find that the localized zero modes around vacancies can coexist with the modulated Dirac bands. The tight-binding calculations confirm that modulated Dirac bands with integer hopping ratios and coexisting localized zero modes that are consistent with analytical solutions for H-PTMs.
DOI: 10.24435/materialscloud:kr-68
title: Tight-binding calculation files of modulated Dirac bands and integer hopping ratios in a honeycomb lattice of phenalenyl-tessellation molecules

description: The dataset uploaded here records atomic positions, lattices vectors and Python scripts for tight-binding calculations related to the paper entitled "Modulated Dirac bands and integer hopping ratios in a honeycomb lattice of phenalenyl-tessellation molecules." In a family of nanographene called phenalenyl-tessellation molecules (PTMs), two types of zero modes appear: one is uniformly extended over the entire molecule and the other is localized around vacancies. Therefore, it is expected that energy bands reflecting the properties of these two types of zero modes will appear in a honeycomb PTM (H-PTM). In this study, we show that modulated energy gap and the Fermi velocity of Dirac bands appear in H-PTMs and that the effective two-site model has positive integer hopping ratios based on the uniformly extended zero mode and  the number of connections between PTMs. Furthermore, we find that the localized zero modes around vacancies can coexist with the modulated Dirac bands. The tight-binding calculations confirm that modulated Dirac bands with integer hopping ratios and coexisting localized zero modes that are consistent with analytical solutions for H-PTMs.
DOI: 10.24435/materialscloud:j6-zz
title: Efficient implementation of gaussian process regression accelerated saddle point searches with application to molecular reactions

description: The task of locating first order saddle points on high-dimensional surfaces describing the variation of energy as a function of atomic coordinates is an essential step for identifying the mechanism and estimating the rate of thermally activated events within the harmonic approximation of transition state theory. When combined directly with electronic structure calculations, the number of energy and atomic force evaluations needed for convergence is a primary issue. Here, we describe an efficient implementation of Gaussian process regression (GPR) acceleration of the minimum mode following method where a dimer is used to estimate the lowest eigenmode of the Hessian. A surrogate energy surface is constructed and updated after each electronic structure calculation. The method is applied to a test set of 500 molecular reactions previously generated by Hermez and coworkers [J. Chem. Theory Comput. 18, 6974 (2022)]. An order of magnitude reduction in the number of electronic structure calculations needed to reach the saddle point configurations is obtained by using the GPR compared to the dimer method. Despite the wide range in stiffness of the molecular degrees of freedom, the calculations are carried out using Cartesian coordinates and are found to require similar number of electronic structure calculations as an elaborate internal coordinate method implemented in the Sella software package. The present implementation of the GPR surrogate model in C++ is efficient enough for the wall time of the saddle point searches to be reduced in 3 out of 4 cases even though the calculations are carried out at a low Hartree-Fock level. This record contains the saddle search output logs for Sella and EON (dimer, with and without GPR acceleration). The data also includes full trajectories of the GPR accelerated dimer method for visual analysis. These logs are used to generate the figures in the manuscript. For details, refer to the code in the associated GitHub repository.
DOI: 10.24435/materialscloud:dq-ax
title: Homogeneous nucleation of undercooled Al-Ni melts via a machine-learned interaction potential

description: Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describing the interactions between atoms,  hich is further complicated by chemical order through cross-species interactions. The large scales needed to observe rare nucleation events are far beyond the capabilities of ab initio simulations. Machine-learning is used for overcoming these limitations in terms of both accuracy and speed, by building a high-dimensional neural network potential for binary Al-Ni alloys, which serve as a model system relevant to many industrial applications. The potential is validated against experimental diffusion, viscosity, and scattering data, and is applied to large-scale molecular dynamics simulations of homogeneous nucleation at equiatomic composition, as well as for pure Ni. Pure Ni nucleates in a single-step into an fcc crystal phase, in contrast to previous results obtained with a classical empirical potential. This highlights the sensitivity of nucleation pathways to the underlying atomic interactions. Our findings suggest that the nucleation pathway for AlNi proceeds in a single step toward a B2 structure, which is discussed in relation to the pure elements counterparts.
DOI: 10.24435/materialscloud:eh-a7
title: Efficient implementation of gaussian process regression accelerated saddle point searches with application to molecular reactions

description: The task of locating first order saddle points on high-dimensional surfaces describing the variation of energy as a function of atomic coordinates is an essential step for identifying the mechanism and estimating the rate of thermally activated events within the harmonic approximation of transition state theory. When combined directly with electronic structure calculations, the number of energy and atomic force evaluations needed for convergence is a primary issue. Here, we describe an efficient implementation of Gaussian process regression (GPR) acceleration of the minimum mode following method where a dimer is used to estimate the lowest eigenmode of the Hessian. A surrogate energy surface is constructed and updated after each electronic structure calculation. The method is applied to a test set of 500 molecular reactions previously generated by Hermez and coworkers [J. Chem. Theory Comput. 18, 6974 (2022)]. An order of magnitude reduction in the number of electronic structure calculations needed to reach the saddle point configurations is obtained by using the GPR compared to the dimer method. Despite the wide range in stiffness of the molecular degrees of freedom, the calculations are carried out using Cartesian coordinates and are found to require similar number of electronic structure calculations as an elaborate internal coordinate method implemented in the Sella software package. The present implementation of the GPR surrogate model in C++ is efficient enough for the wall time of the saddle point searches to be reduced in 3 out of 4 cases even though the calculations are carried out at a low Hartree-Fock level. This record contains the saddle search output logs for Sella and EON (dimer, with and without GPR acceleration). The data also includes full trajectories of the GPR accelerated dimer method for visual analysis. These logs are used to generate the figures in the manuscript. For details, refer to the code in the associated GitHub repository.
DOI: 10.24435/materialscloud:6t-zn
title: Homogeneous nucleation of undercooled Al-Ni melts via a machine-learned interaction potential

description: Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describing the interactions between atoms,  hich is further complicated by chemical order through cross-species interactions. The large scales needed to observe rare nucleation events are far beyond the capabilities of ab initio simulations. Machine-learning is used for overcoming these limitations in terms of both accuracy and speed, by building a high-dimensional neural network potential for binary Al-Ni alloys, which serve as a model system relevant to many industrial applications. The potential is validated against experimental diffusion, viscosity, and scattering data, and is applied to large-scale molecular dynamics simulations of homogeneous nucleation at equiatomic composition, as well as for pure Ni. Pure Ni nucleates in a single-step into an fcc crystal phase, in contrast to previous results obtained with a classical empirical potential. This highlights the sensitivity of nucleation pathways to the underlying atomic interactions. Our findings suggest that the nucleation pathway for AlNi proceeds in a single step toward a B2 structure, which is discussed in relation to the pure elements counterparts.
DOI: 10.24435/materialscloud:kp-8f
title: Paramagnetic half-moon shaped diffuse scattering arising from 3D magnetic frustration

description: We use spin dynamics simulations to determine the origin of the unusual correlated diffuse scattering, characterised by half-moon shapes bridging the magnetic Bragg peaks, observed in the polarised elastic neutron scattering from manganese tungstate, MnWO₄. We first fit a Heisenberg Hamiltonian with twelve nearest-neighbour exchange interactions and single-ion anisotropy to the experimental ground-state magnon dispersion. We then show via spin dynamics simulations that our model Hamiltonian both reproduces the experimentally observed half-moon features and captures their persistence into the paramagnetic regime. Moreover, we identify the three-dimensional, competing antiferromagnetic interactions driving this behavior. Our work complements earlier studies of half-moon-shaped signatures in pyrochlore and triangular structures, by providing insight into their origin in a zigzag chain geometry with three-dimensional competing exchange interactions.
DOI: 10.24435/materialscloud:te-81
title: Paramagnetic half-moon shaped diffuse scattering arising from 3D magnetic frustration

description: We use spin dynamics simulations to determine the origin of the unusual correlated diffuse scattering, characterised by half-moon shapes bridging the magnetic Bragg peaks, observed in the polarised elastic neutron scattering from manganese tungstate, MnWO₄. We first fit a Heisenberg Hamiltonian with twelve nearest-neighbour exchange interactions and single-ion anisotropy to the experimental ground-state magnon dispersion. We then show via spin dynamics simulations that our model Hamiltonian both reproduces the experimentally observed half-moon features and captures their persistence into the paramagnetic regime. Moreover, we identify the three-dimensional, competing antiferromagnetic interactions driving this behavior. Our work complements earlier studies of half-moon-shaped signatures in pyrochlore and triangular structures, by providing insight into their origin in a zigzag chain geometry with three-dimensional competing exchange interactions.
DOI: 10.24435/materialscloud:85-8x
title: Simulated 3d transition metal oxides cation K-edge XANES dataset

description: X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents with significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on simulated K-edge X-ray absorption near-edge structure (XANES) spectra database covering eight 3d transition metals (Ti-Cu). This database contains FEFF and VASP K-edge XANES spectra of 3d transition metal-containing oxide materials used to develop the above machine learning models. The materials structures are sourced from the Materials Project, corresponding to primary, secondary, ternary and quaternary oxide materials. Specifically, FEFF9 was used to compute the spectra of Ti, V, Cr, Mn, Fe, Co, Ni and Cu compounds; VASP 6.2.1 was used to compute the spectra of Ti and Cu compounds.
DOI: 10.24435/materialscloud:2r-6a
title: Simulated 3d transition metal oxides cation K-edge XANES dataset

description: X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents with significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on simulated K-edge X-ray absorption near-edge structure (XANES) spectra database covering eight 3d transition metals (Ti-Cu). This database contains FEFF and VASP K-edge XANES spectra of 3d transition metal-containing oxide materials used to develop the above machine learning models. The materials structures are sourced from the Materials Project, corresponding to primary, secondary, ternary and quaternary oxide materials. Specifically, FEFF9 was used to compute the spectra of Ti, V, Cr, Mn, Fe, Co, Ni and Cu compounds; VASP 6.2.1 was used to compute the spectra of Ti and Cu compounds.
DOI: 10.24435/materialscloud:x3-z3
title: Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals

description: Spin excitations play a fundamental role in understanding magnetic properties of materials, and have significant technological implications for magnonic devices. However, accurately modeling these in transition-metal and rare-earth compounds remains a formidable challenge. Here, we present a fully first-principles approach for calculating spin-wave spectra based on time-dependent (TD) density-functional perturbation theory (DFPT), using nonempirical Hubbard functionals. This approach is implemented in a general noncollinear formulation, enabling the study of magnons in both collinear and noncollinear magnetic systems. Unlike methods that rely on empirical Hubbard U parameters to describe the ground state, and Heisenberg Hamiltonians for describing magnetic excitations, the methodology developed here probes directly the dynamical spin susceptibility (efficiently evaluated with TDDFPT throught the Liouville-Lanczos approach), and treats the linear variation of the Hubbard augmentation (in itself calculated non-empirically) in full at a self-consistent level. Furthermore, the method satisfies the Goldstone condition without requiring empirical rescaling of the exchange-correlation kernel or explicit enforcement of sum rules, in contrast to existing state-of-the-art techniques. We benchmark the novel computational scheme on prototypical transition-metal monoxides NiO and MnO, showing remarkable agreement with experiments and highlighting the fundamental role of these newly implemented Hubbard corrections. The method holds great promise for describing collective spin excitations in complex materials containing localized electronic states.
DOI: 10.24435/materialscloud:7c-6j
title: Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals

description: Spin excitations play a fundamental role in understanding magnetic properties of materials, and have significant technological implications for magnonic devices. However, accurately modeling these in transition-metal and rare-earth compounds remains a formidable challenge. Here, we present a fully first-principles approach for calculating spin-wave spectra based on time-dependent (TD) density-functional perturbation theory (DFPT), using nonempirical Hubbard functionals. This approach is implemented in a general noncollinear formulation, enabling the study of magnons in both collinear and noncollinear magnetic systems. Unlike methods that rely on empirical Hubbard U parameters to describe the ground state, and Heisenberg Hamiltonians for describing magnetic excitations, the methodology developed here probes directly the dynamical spin susceptibility (efficiently evaluated with TDDFPT throught the Liouville-Lanczos approach), and treats the linear variation of the Hubbard augmentation (in itself calculated non-empirically) in full at a self-consistent level. Furthermore, the method satisfies the Goldstone condition without requiring empirical rescaling of the exchange-correlation kernel or explicit enforcement of sum rules, in contrast to existing state-of-the-art techniques. We benchmark the novel computational scheme on prototypical transition-metal monoxides NiO and MnO, showing remarkable agreement with experiments and highlighting the fundamental role of these newly implemented Hubbard corrections. The method holds great promise for describing collective spin excitations in complex materials containing localized electronic states.
DOI: 10.24435/materialscloud:5r-rf
title: A prediction rigidity formalism for low-cost uncertainties in trained neural networks

description: Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes 'prediction rigidities' as a formalism to obtain uncertainties of arbitrary pre-trained regressors. A clear connection between the suggested framework and Bayesian inference is established, and a last-layer approximation is developed and rigorously justified to enable the application of the method to neural networks. This extension affords cheap uncertainties without any modification to the neural network itself or its training procedure. The effectiveness of this approach is shown for a wide range of regression tasks, ranging from simple toy models to applications in chemistry and meteorology.
This record includes computational experiments supporting the MLST paper titled "A prediction rigidity formalism for low-cost uncertainties in trained neural networks".
DOI: 10.24435/materialscloud:b2-2p
title: A prediction rigidity formalism for low-cost uncertainties in trained neural networks

description: Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes 'prediction rigidities' as a formalism to obtain uncertainties of arbitrary pre-trained regressors. A clear connection between the suggested framework and Bayesian inference is established, and a last-layer approximation is developed and rigorously justified to enable the application of the method to neural networks. This extension affords cheap uncertainties without any modification to the neural network itself or its training procedure. The effectiveness of this approach is shown for a wide range of regression tasks, ranging from simple toy models to applications in chemistry and meteorology.
This record includes computational experiments supporting the MLST paper titled "A prediction rigidity formalism for low-cost uncertainties in trained neural networks".
DOI: 10.24435/materialscloud:es-25
title: Quantum simulations of radiation damage in a molecular polyethylene analog

description: An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. We performed ensembles of quantum simulations of radiation damage in a polyethylene analog using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and we compute occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. Our results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. The data set contains how DFTB+ simulations were setup and running isomorphic analysis and graphs to obtain chemical structures.
DOI: 10.24435/materialscloud:w4-72
title: Quantum simulations of radiation damage in a molecular polyethylene analog

description: An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. We performed ensembles of quantum simulations of radiation damage in a polyethylene analog using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and we compute occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. Our results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. The data set contains how DFTB+ simulations were setup and running isomorphic analysis and graphs to obtain chemical structures.