Introduction

The ASAP (Atomistic Simulation Advanced Platform) software package is a product of SIMUNE Atomistics S.L.. ASAP is a platform for materials design and property modeling at both the quantum mechanical and machine-learning levels. By combining electronic structure methods (DFT/NEGF) with state-of-the-art machine learning interatomic potentials (MLIPs), ASAP accelerates materials R\(\&\)D, and reduces time and costs thanks to its intuitive graphical interface, powerful structure builder, robust algorithmic workflows and local and remote jobs control.

• Ready to use packages with necessary libraries and solvers.

• Interactive GUI widgets for system construction, visualisation, and analysis.

• Cross Platform performance: Linux, Mac, Windows operating systems.

ASAP Packages & Modules

ASAP is broken down into specialized packages and modules tailored to specific user requirements:

ASAP Pro A comprehensive platform equipped with a set of robust workflows for screening new materials, offering batch job management, advanced analysis capabilities, and an extended range of project types. It features a powerful structure builder for constructing, visualising, and manipulating 1D, 2D, and 3D materials. It is especially useful for molecular electronics—enabling easy computation and comparison of electronic properties across a set of molecules, including charge, HOMO, and LUMO energy levels. AI-supported ASAP Pro workflows enhance the user experience in material design across numerous fields, including automotive, electronic, and chemical industries, as well as energy, biological, and pharmaceutical applications.

With the integrated Machine Learning (ML) MACE calculator, ASAP Pro enables accurate, fast simulations to accelerate material design across a wide range of fields

ASAP Pro Transport A collection of additional automated workflows for ASAP Pro, specifically designed to compute and analyse electronic nanotransport using the DFT+NEGF formalism.

The Transport package incorporates a device builder for constructing electrodes of various shapes and cross-sections, as well as buffer and scattering regions. This set of workflows automates the device geometry optimisation, as well as the construction and visualisation of the planar and macro-average electrostatic potential across the device. Additionally, they facilitate the visualisation of the transmission function for bulk electrodes, providing spin-resolved plots for transmission and current. This includes spin difference and spin sum plots.

In Table 1 and Table 2, we present an overview of the structure builder features and the type of projects (workflows) implemented in ASAP Pro and ASAP Transport. Table 3 provides an overview of the main material properties whose calculations are automated within the various ASAP modules.

Table 1 Overview of the structure builder features implemented in the different ASAP modules. See Chapter The atomic structure builder for further information on ASAP structure builder.

Structure Builder Features	ASAP Pro	ASAP Pro Transport
Import/View: pre-existing structures, structure manipulation, measure geometric quantities, dynamic visualisation, flexible view settings	X	X
Build (1D, 2D, 3D): molecular structures from built-in databases, most common crystal structures, supercell slabs (Miller index), nanoparticles, nanoribbons, natubes	X	X
Merge two structures	X	X
Build (electronic device): electrodes, buffer and scattering regions		X

Table 2 Overview of ASAP project types.

Project Type:	ASAP Pro	ASAP Pro Transport
Single-point calculation	X	X
Geometry Optimization	X	X
Local density of states (LDOS)	X	X
Molecular Dynamics	X	X
Equation Of State (EOS)	X	X
Nudged Elastic Band (NEB)	X	X
Interfacial energy tool (IET)	X	X
Optical response	X	X
Vibrations	X	X
Phonons	X	X
Convergence Tools: mesh cutoff	X	X
Convergence Tools: Brillouin Zone sampling	X	X
Interaction energy	X	X
Transport Device Geometry Optimisation		X
Transport		X
Electronic Transport workflow: Analysis		X

Note: Machine Learning calculator (MACE) is only compatible with the following atomic-structure workflows: Single-point, Geometry Optimization, Molecular Dynamics, NEB, and EOS but do not support electronic structure properties (e.g., DOS, Band Structure, Charge Density, Transport).

Table 3 An overview of the main material properties whose calculations are automated within ASAP.

	ASAP Pro	ASAP Pro Transport
Electronic properties:
Fermi energy	X	X
Single particle energies	X	X
Density Of States (DOS)	X	X
Partial Density of States (PDOS)	X	X
Band structure	X	X
Projected molecular orbitals (LDOS)	X	X
Charge (Mulliken, Hirshfeld, Voronoy, Bader)	X	X
Electrostatic potential (incl. visualisation)	X	X
Thermodynamics properties:
Equilibrium volume	X	X
Bulk modulus	X	X
Chemical properties:
Interaction energy	X	X
Reaction diagram	X	X
Activation energies	X	X
Phonons and vibrations:
Vibrational spectrum	X	X
Zero-Point Energy correction (ZPE)	X	X
Phonon density of states	X	X
Phonon band structure	X	X
Chemical properties:
Interaction energy	X	X
Reaction diagram	X	X
Activation energies	X	X
MD analysis:
Kinetic and Potential energy	X	X
Radial Distribution Function (RDF)	X	X
MDS, RSMD analysis	X	X
Diffusion coefficients	X	X
Velocity autocorrelation function	X	X
Electronic nanotransport:
Transmission function		X
I-V curve		X
Conductance		X

About ASAP

We have developed ASAP in Python 3. It is compatible with Python \(\ge\) 3.9. We use the Python library PySide2 (binding of the GUI toolkit Qt) for GUI rendering. The other main packages and libraries ASAP relies on are:

• The Atomic Simulation Environment, aka ASE (https://ase-lib.org/)

• SciPy (https://scipy.org/)

• Matplotlib (https://matplotlib.org/)

We have implemented a user-friendly graphical user interface designed to help the user in preparing the input file for the selected calculator. ASAP interfaces with SIESTA and Quantum Espresso electronic structure codes, as well as the MACE machine learning force field framework.

If you are preparing an article using ASAP, please include following acknowledgment in your manuscript:

“These studies were performed using some results obtained with the ASAP-YYYY.N (Atomistic Simulation Advanced Platform).”

Here YYYY is the 4-digit representation of the year and N the release index for that year, as for example ASAP-2023.1.

Please find below the references to methodology and software papers:

• E. Artacho, D. Sánchez‐Portal, P. Ordejón, A. Garcia, and J. M. Soler, Linear‐Scaling ab‐initio Calculations for Large and Complex Systems, Phys. Status Solidi B 215, 809 (1999).

• J. M. Soler, E. Artacho, J. D. Gale, A. Garcia, J. Junquera, P. Ordejón and D. Sánchez-Portal, The SIESTA method for ab initio order-N materials simulation J. Phys. Condens. Matter 14, 11, (2002)

• E. Artacho, E. Anglada, O. Diéguez, J. D. Gale, A. Garcia, J. Junquera, R. M. Martin, P. Ordejón, J. M. Pruneda, D. Sánchez-Portal and J. M. Soler, The SIESTA method; developments and applicability, J. Phys. Condens. Matter 20, 064208, (2008).

• A. H. Larsen, J. J. Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dulak, J. Friis, M. N. Groves, B.Hammer, C. Hargus, E. D. Hermes, P. C. Jennings, P. B. Jensen,K. Kaasbjerg, J. Kermode, J. R. Kitchin, E. L. Kolsbjerg, J. Kubal, S. Lysgaard, J. B. Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiøtz, O. Schutt, M. Strange, K.Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, and K. W.Jacobsen, The Atomic Simulation Environment — A Python library for working with atoms, J. Phys. Condens. Matter 29, 273002 (2017).