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MPInterfaces: A Materials Project based Python Tool for High-Throughput Computational Screening of Interfacial Systems
Kiran Mathew, Arunima K. Singh, Joshua J. Gabriel, Kamal Choudhary, Susan B. Sinnott, Albert V. Davydov, Francesca Tavazza, Richard G. Hennig
TL;DR
Characterizing interfacial systems is challenging, and experiments can be costly and difficult to apply across many candidates. MPInterfaces automates interface construction and computational workflows; illustrative examples demonstrate its capabilities in studying interfacial systems.
Problem
Interfacial systems offer many possible configurations and require greater computational resources than bulk materials, while comprehensive experiments across candidates are costly and difficult under extreme or hazardous conditions.
Source: §1 · p. 1, §2.3 · p. 4Method
MPInterfaces is an open-source Python package that automates interfacial-structure generation, prepares simulation inputs, and supports computational workflows and analysis.
Source: §1 · p. 2Results
Illustrative examples demonstrate MPInterfaces’ usefulness and capabilities for studying interfacial systems.
Source: §3 · p. 8Takeaways & Limitations
MPInterfaces extends existing high-throughput tools towards the study of interfacial systems.
Source: §3 · p. 8
Abstract
from arXiv · showhide
A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards the understanding of interfacial systems. We describe the various algorithms and methods implemented in the package. Using several test cases, we demonstrate how the package enables high-throughput computational screening of advanced materials, directly contributing to the Materials Genome Initiative (MGI), which aims to accelerate the discovery, development, and deployment of new materials.
1. Introduction
Interfaces strongly shape materials and device properties, but thorough characterization of many candidate systems is difficult and costly experimentally. MPInterfaces addresses this challenge with tools for generating interfacial structures, preparing calculations, creating workflows, and analyzing results.
1. Introduction
- Interfaces influence electrochemical-device efficiency and stability, nanoparticle self-assembly, and the essential characteristics of increasingly small materials and devices.The introduction also notes that understanding and controlling interface properties guides progress across applications from catalysis to microelectronics.Source: §1 · p. 1
- Comprehensive experimental characterization of many candidate systems is time-consuming and expensive, especially under extreme or hazardous conditions.The authors call for computational screening to complement and guide experiments.Source: §1 · p. 1
- Recent high-throughput computational studies have predicted materials for photocatalysis, energy storage, piezoelectrics, and electrocatalysis.The paper situates MPInterfaces within advances in materials simulation.Source: §1 · p. 2
- MPInterfaces automates interfacial-structure generation and prepares input files for DFT and molecular-dynamics simulations.The supported software includes VASP for DFT and LAMMPS for molecular dynamics.Source: §1 · p. 2
- MPInterfaces supports remote-deployable computational workflows and analysis tools, including nanocrystal-shape prediction using surface energies and the Wulff construction.Its calculation and workflow capabilities build on Materials Project packages pymatgen, custodian, and fireworks.Source: §1 · p. 2
- The paper presents an overview of MPInterfaces capabilities and algorithms through examples and code excerpts.The package is described as continuously developed.Source: §1 · p. 2
2. MPInterfaces: Overview with examples
MPInterfaces provides tools for constructing nanoparticle–ligand and solid–solid interfaces, predicting nanocrystal shapes, and managing computational workflows. Examples demonstrate these capabilities for PbS, graphene–metal interfaces, and DFT and empirical-potential calculations.
2.1. Ligand capped nanoparticles
- MPInterfaces samples nanoparticle–ligand interfaces across surface facets, ligand binding sites, and ligand coverage, with slab models parameterized by surface and ligand geometry.The inputs include the bulk phase, facet, binding site and atom, adsorption distance, and ligand coverage.Source: §2.1 · pp. 2–3, Figure 1 · p. 3
2.2. Wulff construction
- The Wulff implementation extends cubic-symmetry approaches to arbitrary space groups and uses facet surface energies to construct equilibrium nanocrystal shapes.For the example PbS inputs, it produces a truncated octahedron formed by (111) and (100) facets.Source: §2.2 · p. 4, Figure 2 · p. 4
2.3. Heterostructure Interfaces
- The heterostructure algorithm identifies distinct, symmetry-matched surface pairs within specified lattice-mismatch and interface-area limits.It uses the Zur et al. algorithm to find matches across rotations and generates distinct structures through near-interface atom placements.Source: §2.3 · pp. 4–5, Figure 3 · p. 5
- A 2 × 2 graphene supercell matches the other tested metal (111) surfaces with lattice mismatch below 4%.Primitive-cell 1:1 matches exceed 10% for all tested metals except Cu and Ni.Source: §2.3 · p. 5, Table 1 · p. 5
2.4. Workflows
- MPInterfaces workflows connect Python-function jobs linearly and use JSON checkpoints to record job status, inputs, parameters, and calculated energies.The design avoids a workflow database and is intended to simplify setup on different computational resources.Source: §2.4 · pp. 6–7
2.4.1. VASP workflow example
- The VASP example relaxes lead acetate, PbS bulk, and surface slabs before generating ligand adsorption configurations and extracting interfacial properties.Optional VASPsol calculations estimate solvent or electrolyte effects on surface and binding energies.Source: §2.4.1 · pp. 7–8, Figure 5 · p. 7
2.4.2. LAMMPS workflow example
- The LAMMPS example uses COMB3 to optimize bulk Al and low-index facets, then calculates surface energies for a Wulff nanocrystal construction.Source: §2.4.2 · p. 8
3. Summary
MPInterfaces extends existing high-throughput tools toward computational study of interfacial systems. The paper illustrates its capabilities with an aluminum nanocrystal workflow and proposes an open, collaborative platform for further development.
3. Summary
- MPInterfaces extends existing high-throughput frameworks toward the study of interfacial systems.The package builds on tools including pymatgen, custodian, and fireworks.Source: §3 · p. 8
- The package demonstrates an aluminum nanocrystal shape-prediction workflow using empirical energy models with LAMMPS.Source: Figure 6 · p. 8
- The authors intend the open-source project to provide a collaborative platform for reviewing and improving its code base.Source: §3 · p. 9