Code interoperability extends the scope of quantum simulations

-By Nikita Vijay Biliye

In many materials, the combination of heterogeneous components and the interfaces that appear between them decides their functionality. 

Building efficient computational schemes are essential to catch the appropriate lengths and time scales of the materials. These computational schemes predict and design various characteristics of a wide range of materials by developing interoperable codes. These codes can be useful in performing complex tasks when effectively coupled with each other.

In this article, we will discuss the uses of these interoperable codes in restoring the structural and spectroscopic characterization of the materials.

Introduction

The knowledge and forecasts about the material’s properties led to widespread research activities in science and engineering. These researches came up with developments in new technologies influencing quantum information science, health, energy, and national security. In the last two decades, there have been developments of predictive and simulation frameworks at multiple length scales and databases of materials. 

Simulation refers to the approximate replication of functions of the process that represents its functions over time. Simulations have a wide range of applications in the semiconductor and pharmaceutical industries. The simulation codes are used as end-of-the-line engineering tools. For example, is the use of numerical simulations in the designing process of a new chip.

The main aim of the modern theories of materials and software is to forecast and ultimately develop new strategies to design new materials. First-principles codes have a broad potential and are used to generate the required data to help the machine learn properties of a vast class of materials. Then, computationally generated data or Metadata assists in strategy designing, including fresh feedback loops with experiments.

It is essential to understand and forecast the reactions of matter to external stimuli. In recent years, the development of codes that singly perform particular tasks and carry out complex simulations has been an emerging trend in computational materials science. 

Vivo coupling of codes using a client-server model
Credit: npj Computational Materials, Govoni et al.

Vivo coupling of codes using a client-server model.

In the above figure, the basic physical quantities (indicated by blue) interchanged between the first principles molecular dynamic situations, advanced sampling techniques, and calculations of spectroscopic properties. The codes used here Qbox, SSAGES, and WEST are coupled with the help of the driver-engine approach. 

In vivo interoperability approach is described that enables calculation of materials properties. In this approach, the data-exchange operations – controlled by a client-server protocol.

Results:

Coupling interoperable software:

Implementation of calculation methods for various materials faces many challenges. These problems are due to the rapid increase in complexity of parallel simulation codes. As a solution, a modular computational strategy is adopted. Here an entire simulation is gained, either through in vivo or ex vivo coupling interoperable codes. Devising interoperable code is difficult as there is a lack of standardized procedures for sharing information between them. Also, complications arise when the codes have to communicate frequently.

In a driver-engine approach, driver code controls the flow of execution engine codes with inter-process communication between the driver and the engines.

In the client-server model, the driver program behaves like a client and a server sending a set of guidelines to an engine code. To ensure interoperability the data exchanged by each code is strictly documented with the help of a markup language. 

Applications of in vivo interoperability scheme:

Advanced sampling using first-principles molecular dynamics:

Recently, advanced sampling has been coupled with first-principles molecular dynamics (FPMD). This coupling contributed to overcoming the transferability issues that were present in classical interatomic potentials.

The need for parameterizing a force field was also avoided.

FPMD has a wide range of advantages but, it remains more demanding computationally than MD having shorter empirical potentials.

The in vivo coupling between Qbox and SSAGES is obtained using the client-server model. To compute the electronic ground state of the system, Qbox performs a wave function optimization initially. It also initializes velocities at the right temperatures and carries out one molecular dynamics integration step. SSAGES reads the required information for the calculation of enhanced sampling. 

Advanced sampling using first-principles molecular dynamics simulation. figure 2 shows the representation of three metastable minima (β, C7eq, and C7ax) of the alanine dipeptide with two angles (φ, θ) used as bias (panel a). Entropic (bcd) and potential energy (e, f, g panels) contribute to the free energy surface of the alanine di-peptide. The pointer in panel a shows a minimum free-energy path, and the circled region in panel b shows a significant overestimation of repulsive forces in the classical model.
Credit: American Chemical Society, 2018.

Spectroscopy from first principles:

There have been recent developments in the hybrid functions that led to advanced simulations of charged excitations. The time-dependent Density Functional Theory (DFT) has provided a computationally manageable method to compute neutral excitations.

Many-Body Perturbation Theory (MBPT) has helped in forecasting excite state properties of molecules and materials. The data sets that were exchange by Qbox and WEST were used to train the surrogate model of the dielectric screening computed in the field. 

The researchers have built a Quantum Embedding Theory that goes beyond the constrained random phase approximation.

Quantum Embedding Theory
Credit: npj Computational Materials

Transport from first principles:

Constrained density functional theory (CDFT) gives a vigorous framework for constructing diabatic states from first-principles. This framework also helps in predicting the electronic coupling in molecules and solids. A Python packaged (PyCDFT) is recently implemented to perform single-point self-consistent-field and geometric optimization calculation using CDFT.

The interoperable codes discussed here are:

  1. Qbox: Qbox is a first principle molecular dynamics code based on plane-wave, pseudopotential formalism. DFT or hybrid-DFT is adapted to obtain its electronic structure. Qbox also helps in the calculation of vibrational spectra, heat transport coefficients, and ionic conductivity.
  • WEST: This code performs large-scale MBPT calculations and provides an electronic and spectroscopic characterization of complex materials. This code also used plane wave, pseudopotential formalism. WEST is interfaced with Quantum Espresso Code and with Qbox in client-server mode.    
  • SSAGES: This code provides a framework to calculate reaction coordinates and reactive pathways, and free energies in molecular simulations. 
  • PyCDFT: PyCDFT is a python code that uses CDFT to compute diabatic states. It permits single-point self-consistent-field calculations as well as geometric optimizations. 

References:

  1. https://www.nature.com/articles/s41524-021-00501-z
  2. https://www.researchgate.net/publication/260241848_Code_Interoperability_and_Standard_Data_Formats_in_Quantum_Chemistry_and_Quantum_Dynamics_The_Q5D5Cost_Data_Model

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