Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches. Here, we present a systematic classification of these representations and then investigate (i) the sensitivity to perturbations and (ii) the effective dimensionality of a variety of atomic environment representations and over a range of material datasets. Representations investigated include atom centered symmetry functions, Chebyshev Polynomial Symmetry Functions (CHSF), smooth overlap of atomic positions, many-body tensor representation, and atomic cluster expansion. In area (i), we show that none of the atomic environment representations are linearly stable under tangential perturbations and that for CHSF, there are instabilities for particular choices of perturbation, which we show can be removed with a slight redefinition of the representation. In area (ii), we find that most representations can be compressed significantly without loss of precision and, further, that selecting optimal subsets of a representation method improves the accuracy of regression models built for a given dataset

Insights into the emerging networks of voids in simulated supercooled water

The structural evolution of supercooled liquid water as we approach the glass transition temperature continues to be an active area of research. Here, we use molecular dynamics simulations of TIP4P/ice water to study the changes in the connected regions of empty space within the liquid, which we investigate using the Voronoi-voids network. We observe two important features: supercooling enhances the fraction of nonspherical voids and different sizes of voids tend to cluster forming a percolating network. By examining order parameters such as the local structure index (LSI), tetrahedrality and topological defects, we show that water molecules near large void clusters tend to be slightly more tetrahedral than those near small voids, with a lower population of under- and overcoordinated defects. We show further that the distribution of closed rings of water molecules around small and large void clusters maintain a balance between 6 and 7 membered rings. Our results highlight the changes of the dual voids and water network as a structural hallmark of supercooling and provide insights into the molecular origins of cooperative effects underlying density fluctuations on the subnanometer and nanometer length scale. In addition, the percolation of the voids and the hydrogen bond network around the voids may serve as useful order parameters to investigate density fluctuations in supercooled water

Integrated modelling for sustainability assessment and decision making of alternative fuel buses

In this paper, a hybrid life cycle sustainability assessment (LCSA) model integrating multi region input–output analysis with novel multi-criteria decision-making techniques is proposed to assess three different fuel alternatives: compressed natural gas (CNG), electric buses (EBs), and diesel buses (DBs). A global hybrid LCSA model first quantified the environmental, economic, and social impacts of alternative fuel buses. The results were investigated in terms of multiple combinations of manufacturing and end-of-life scenarios by encompassing impacts embedded in the global supply chains taking Qatar as a case applied to the proposed model. The Interval-Valued Neutrosophic Fuzzy (IVNF)-Analytic Hierarchy Process with the Combined Compromise Solution (CoCoSo) approach is used to rank the alternative fuel buses based on their corresponding sustainability performance. The proposed model will help in quantitatively capturing the macrolevel life cycle socioeconomic and environmental impacts along with optimally selecting alternatives to support sustainable urban transport policy towards a net-zero transportation system globally

Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows towards object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create "deep" modular interfaces that connect big-data workflows and electronic structure codes, and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; and in another, DFTB+ receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+, or to enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities

Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows toward object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create deep modular interfaces that connect big-data workflows and electronic structure codes and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; in another, DFTB+ receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+ or enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments. The scheme goes beyond conventional tight binding descriptions as it represents the ab initio model to full order, rather than in two-centre or three-centre approximations. We achieve this by introducing an extension to the atomic cluster expansion (ACE) descriptor that represents Hamiltonian matrix blocks that transform equivariantly with respect to the full rotation group. The approach produces analytical linear models for the Hamiltonian and overlap matrices. Through an application to aluminium, we demonstrate that it is possible to train models from a handful of structures computed with density functional theory, and apply them to produce accurate predictions for the electronic structure. The model generalises well and is able to predict defects accurately from only bulk training data

Nanoyapılı ikili metal alaşımların özellikleri

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Bilişim Enstitüsü, 2013Bu Tez çalışmasında, Gömülü Atom Yöntemi (GAY) kullanılarak Cu-Ni alaşımları için yük yoğunluğu tanımlamaları yeniden düzenlenmiş ve bu tanım kullanılarak yarı deneysel çok cisimli model potansiyeller üretilmiştir. Cu ve Ni saf elementleri için yük yoğunluğu tanımı, 3d valans elektron yoğunluğuna 4s elektron yoğunluğunun katkısı eklenerek sağlanmıştır. Potansiyel fonksiyon parametrelerinin ayarlanması için uyumlu parçacık sürüsü optimizasyon (APSO) yönteminden yararlanılmış, yöntemin hesaplama süresinin kısaltılması için ise MPI tabanlı paralel dağıtık algoritmalar kullanılmıştır. Ayrıca, APSO yönteminde yerel minimum durumlarından kaçınılmasını sağlayan 'Elit Öğrenme' süreci paralel programlama algoritmaları yardımıyla hem dağıtık mimaride geliştirilmiş hem de birden fazla sayıda alınarak yakınsama hızının arttırılması sağlanmıştır. Potansiyel fonksiyonlarının hem saf Cu ve Ni, hem de Cu-Ni alaşımları için eğri ayarlanarak belirlenmesinde örgü sabiti, hacim modülü, elastik sabitler, boşluk oluşturma enerjisi, ikili bağ uzunluğu ve enerjisi gibi deneysel ve ilk-ilke değerleri kullanılmıştır. Üretilen potansiyellerin sınanması için ise saf Cu, Ni ve çeşitli Cu-Ni alaşımlarının özellikleri hesaplanmıştır. Bu özellikler; erime sıcaklıkları, alaşım oluşturma entalpisi, titreşim termodinamik fonksiyonları, denge durumu örgü yapıları, alaşım boşluk oluşturma enerjisi, istifleme hatası ve çatlak oluşma enerjileri ile (100) ve (111) yüzeylerinde Cu ve Ni ekatomları için hesaplanan bir çok difüzyon engel değerleridir.In this Thesis, a new semi-empirical and many-body type model potential for Cu-Ni alloys was developed using embedded atom method (EAM) formalism based on a modified charge density profile with an improved optimization technique. In the process, the charge density profile for pure Cu and Ni elements was modified by incorporating the 4s charge density contribution within the optimization. The adaptive particle swarm optimization (APSO) method was utilized to search the parameter space of the EAM functions. The technique was further optimized by implementing MPI based parallel algorithms. The potential was furnished by fitting to experimental and first-principle data for Cu, Ni, and Cu-Ni binary compounds, such as lattice constants, cohesive energies, bulk modulus, elastic constants, diatomic bond lengths, and bond energies. The generated potentials were then tested through computing a variety of properties of pure elements and the alloy of Cu, Ni: the melting points, alloy mixing enthalpy, vibrational thermodynamical functions, equilibrium lattice structures, vacancy formation, stacking and interstitial formation energies, various diffusion barriers on the (100) and (111) surfaces of Cu and Ni, and the growth mechanisms of Ni, Cu nanostructures on the Cu(111) surface both using molecular dynamic (MD) simulations and total energy calculations.DoktoraPh.D

A parallel implementation: Real space Green's function technique

We develop an MPI-based parallel algorithm to implement the real space Green's function technique for calculating the vibrational density of states corresponding to a solid. The Hamiltonian describing the interactions between the atoms within the system is obtained from the embedded atom method. The parallel implementation speeds up calculation by an order of magnitude. The parallel implementation details and results are presented in this paper.Publisher's Versio

The role of vibrations in thermodynamic properties of Cu-Ni alloys

We report results of a systematic study for vibrational thermodynamic functions of Cu-Ni alloys, in the harmonic approximation, using interaction potentials based on the embedded atom method with improved optimization techniques. The vibrational density of states of the systems is calculated using real space Green’s function method. From an investigation of local force fields we found that increasing Ni concentration in the alloy substantially stiffens the force experienced by Cu atoms compared to that of Ni atoms. Our calculations also reveal that vibrational entropy change between ordered and disordered crystals of Cu-Ni is negligible. However, the mixing entropy of the phonons and electronic states is found to be negative and favors un-mixing, and thus contributes to the miscibility gap