AFLOW: Integrated infrastructure for computational ...

COREY OSESDAVID HICKS

MARCO ESTERSRICO FRIEDRICH

ANDRIY SMOLYANYUK

CORMAC TOHERSTEFANO

CURTAROLOMaterials Science

Duke University

AFLOW: Integrated infrastructure for computational materials discovery

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AFLOW School Team

Corey OsesCormac Toher David Hicks Marco Esters

Andriy Smolyanyuk Stefano CurtaroloRico Friedrich

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AFLOW School Zoom Logistics

Microphone should be

muted if not speaking

If you have a question, use

the Chat or Raise Hand functions

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Reactions button

Raise hand button

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Raised hand button

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• Exercises will be part of each session

• For exercises, group will be separated into breakout rooms within Zoom

• One instructor will be in each breakout room, and you can ask this instructor for assistance, share your screen with them, etc.

• Exercise materials are available in the TU Dresden VNC portal – to access the portal, you will need to install and run the TU Dresden VPN: https://tu-dresden.de/zih/dienste/service-katalog/arbeitsumgebung/zugang_datennetz/vpn#

• Follow instructions for your operating system

https://tu-dresden.de/zih/dienste/service-katalog/arbeitsumgebung/zugang_datennetz/vpn

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• Once VPN is connected, log into VNC virtual machine using link and password sent to you by email

• Please do not use the “Log out” function, as this will shut down the VM for everyone

• Please do not shut down the VM

• To exit, simply close the browser window/tab

• Please do not change the resolution in the desktop environment

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Connect toVirtual

Machine

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Use password

sent to you by email

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Clipboard and screen

resizing

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Open clipboard to copy/paste into Portal

Use clipboard to copy/paste into Portal

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Settings

Clip to Window Scaling mode

Remote Resizing

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Terminal

Home directory

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AFLOW Exercisesdirectory

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Thermal barrier coatings:

energy/aerospace

Superhard, wear resistant coatings:

machine tools

Biocompatible, corrosion resistant

alloys: medical implants

Photovoltaic materials:

solar energy harvesting

Superconductors and heat sinks for advanced electronics

Solid state electrolyte:

Li-ion batteries

Novel Materials Applications

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✓78

2

◆= 3, 003

✓78

3

◆= 76, 076

✓78

4

◆= 1, 426, 425

✓78

5

◆= 21, 111, 090

✓78

6

◆= 256, 851, 595

Challenge: materials space

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M. J. Mehl et al., Comput. Mater. Sci. 136, S1-S828 (2017), D. Hicks et al., Comput. Mater. Sci. 161, S1-S1011 (2019)

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10177

M. J. Mehl et al., Comput. Mater. Sci. 136, S1-S828 (2017), D. Hicks et al., Comput. Mater. Sci. 161, S1-S1011 (2019)

• Automated computational materials discovery:

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Automated Materials Discovery

Experimental structures Automated QM calculations

Searchable/sortable database Candidate materials

Structural prototypes

ML models

Density functional theory

http://www.aflow.org

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AFLOW: Automatic FLOW

AFLOW

DFT-code

aflow.in

ElasticElectronic Thermal

Storage

Property calculation

Database storage

Calculation management

Input file generation

DFT-code

DFT-code

DFT-code

ERROR?

Curtarolo et al., Comput. Mater. Sci. 58, 218 (2012); Calderon et al., Comput. Mater. Sci. 108A, 233 (2015)

AFLOW source code now available under GPL 3

materials.duke.edu/AFLOW/

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AFLOW.org: web portal


> 3.5 million entries

> 700 million properties

breakdown of properties

aflow.orgAFLOW

Schools and Seminars

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AFLOW.org: seminars

• Online seminars hosted by Center for Autonomous Materials Design• Seminars take place every two weeks on Thursday afternoons• See aflow.org/seminars/ for more details

Seminar sign-up link

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AFLOW.org: applications


aflow.org

AFLOW.org: search page

aflow.org/search/

Element selection

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Number of species

Element group

selection

R. H. Taylor et al., Comput. Mater. Sci. 93, 178-192 (2014)

Logic operations


aflow.org/search/

Slide to Property

Filters

25R. H. Taylor et al., Comput. Mater. Sci. 93, 178-192 (2014)

Bring property

filters onto search page


aflow.org/search/ 26R. H. Taylor et al., Comput. Mater. Sci. 93, 178-192 (2014)

Property Filters


aflow.org/search/

Number of species=2

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Added filter for

Egap: electronic band bap

Selected Elements

R. H. Taylor et al., Comput. Mater. Sci. 93, 178-192 (2014)

Check box to restrict

search results to specific

value range

Select “Add” to add filter

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Exercises:1. Use the advanced search functionality to find the band gap for SiC in the

zincblende structure (space group number 216).

2. Use the advanced search functionality to find the materials containing Sn but not Pb with band gaps between 1 eV and 3 eV in the ICSD catalog of the AFLOW database. How many results are returned?

Alternative server: aflowlib.duke.edu/search/ui/

R. H. Taylor et al., Comput. Mater. Sci. 93, 178-192 (2014), F. Rose et al., Comput. Mater. Sci. 137, 362 (2017)

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Objective 1: Stability screening

elements + arrangements

Thermodynamic stability

1 0.8 0.6 0.4 0.2 0100

50

0

≠50

≠100

≠150

≠200

≠250

Zr

10.80.60.40.20

100

50

0

≠50

≠100

≠150

≠200

≠250Cu

form

ation

enth

alpy

(meV

/ato

m)

Cu 5

Zr

Cu 2

Zr

Cu 1

0Zr 7CuZ

r 2

aflow.org

C. Oses et al., J. Chem. Inf. Model. 58(12), 2477-2490 (2018)

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Objective 1: Stability screening

elements + arrangements

Mechanical stability

Mo6PbS8 Mo6NdS8

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Objective 2: Design rule discovery

A

BX3

ABX3 [X = F, O] ABF3 ABO3

/ B / A

Example 2: thermal conductivity in perovskites

Example 1: superconducting critical temperature

A. van Roekeghem, J. Carrete, C. Oses, S. Curtarolo, and N. Mingo,

Phys. Rev. X 6, 041061 (2016).

O. Isayev, D. Fourches, E. N. Muratov, C. Oses, K. Rasch, A. Tropsha, and S. Curtarolo,

Chem. Mater. 27(3), 735-743 (2014).

TC " TC #

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Objective 3: Machine learning modelscrystalstructure

Voronoitessella/onandneighborssearch

infiniteperiodicgraphconstruc/onandpropertylabeling

nodes(atoms)

decomposi/ontofragments

edges(bonds)

pathfragmentsoflengthl,l=2,3,…

circularfragments(polyhedrons)

a b c

d

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crystalstructure

ElectronicProper1es Thermo-MechanicalProper1es

metalorinsulator?

no EBG

{EBG � R :

EBG > 0}

bandgapenergy

predic4on

bulkmodulus(VRH)predic4on

{X � R}

yes

no

classifica4onmodel

regressionmodel

regressionmodels

FIG. 2. Outline of the modeling work-flow. ML models are represented by orange diamonds. Target properties predictedby these models are highlighted in green.

structure of the material and determine the atomic con-nectivity within it. In general, atomic connectivity isnot a trivial property to determine within materials.Not only must we consider the potential bonding dis-tances among the atoms, but also whether the topologyof nearby atoms allows for bonding. Therefore, we haveemployed a computational geometry approach to parti-tion the crystal structure (Figure 1a) into atom-centeredVoronoi-Dirichlet polyhedra [59–62] (Figure 1b). Thispartitioning scheme was found to be invaluable in thetopological analysis of metal organic frameworks (MOF),molecules, and inorganic crystals [63, 64]. Connectivitybetween atoms is established by satisfying two criteria:(i) the atoms must share a Voronoi face (perpendicu-lar bisector between neighboring atoms), and (ii) theinteratomic distance must be shorter than the sum ofthe Cordero covalent radii [65] to within a 0.25 A tol-erance. Here, we consider only strong interatomic in-teractions such as covalent, ionic, and metallic bonding,ignoring van der Waals interactions. Due to the ambigu-ity within materials, the bond order (single/double/triplebond classification) is not considered. Taken together,the Voronoi centers that share a Voronoi face and arewithin the sum of their covalent radii form a three-dimensional graph defining the connectivity within thematerial.

In the final steps of the PLMF construction, the fullgraph and corresponding adjacency matrix (Figure 1c)are constructed from the total list of connections. Theadjacency matrix A of a simple graph (material) with n

vertices (atoms) is a square matrix (n ⇥ n) with entriesaij = 1 if atom i is connected to atom j, and aij = 0 oth-erwise. This adjacency matrix reflects the global topol-

ogy for a given system, including interatomic bonds andcontacts within the crystal. The full graph is partitionedinto smaller subgraphs, corresponding to individual frag-ments (Figure 1d). While there are several subgraphs toconsider in general, we restrict the length l to a maximumof three, where l is the largest number of consecutive,non-repetitive edges in the subgraph. This restrictionserves to curb the complexity of the final descriptor vec-tor. In particular, we consider two types of fragments.Path fragments are subgraphs of at most l = 3 that en-code any linear strand of up to four atoms. Only theshortest paths between atoms are considered. Circularfragments are subgraphs of l = 2 that encode the firstshell of nearest neighbor atoms. In this context, circularfragments represent coordination polyhedra, or clustersof atoms with anion/cation centers each surrounded bya set of its respective counter ion. Coordination polyhe-dra are used extensively in crystallography and mineral-ogy [66].

Property labeling. PLMFs are di↵erentiated bylocal (standard atomic) reference properties [57], whichinclude: (i) general properties: the Mendeleev group andperiod numbers, number of valence electrons (NV); (ii)measured properties [57]: atomic mass, electron a�nity(EA), thermal conductivity (�), heat capacity (C), en-thalpies of atomization (�Hat), fusion (�Hfusion), and va-porization, first three ionization potentials (IP1,2,3); and(iii) derived properties: e↵ective atomic charge (Ze↵),molar volume (Vmolar), chemical hardness (⌘) [57, 67],covalent (rcov) [65], absolute [68], and van der Waalsradii [57], electronegativity (�), and polarizability. Wealso combine pairs of properties in the form of their mul-tiplication and ratio, as well as include the property value

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AFLOW: Installation

• AFLOW can be used on Linux, Mac, and Windows

• Windows requires additional software:– Windows Subsystem for Linux (Windows 10)– Cygwin (other Windows versions)

• For instructions and installation scripts, visit:

Curtarolo et al., Comput. Mater. Sci. 58, 218 (2012); Curtarolo et al., Comput. Mater. Sci. 58, 227 (2012)

http://aflow.org/install-aflow/

http://aflow.org/install-aflow/

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Outline: Day 1• Keynote address: To mix or not to mix? Synthesizability

entropy-descriptors and the controversial role of vibrations in the stability of high-entropy ceramics (S. Curtarolo)

• Session 1: DFT+AFLOW (C. Oses & D. Hicks)• VASP input files • Energy calculations and k-point convergence • Primitive and conventional cell representations • Spin-polarized calculations • Density of states and band structure calculations • AFLOW input file: aflow.in• Configuration file: aflow.rc• Managing ab-initio calculations

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Outline: Day 2• Session 2: Symmetry (D. Hicks)• AFLOW-SYM algorithm • AFLOW-SYM output files • AFLOW-SYM command line interface • AFLOW-SYM Python wrapper • Brillouin zone and AFLOW Standard k-path • AFLOW-SYM via AFLOW-Online

• Session 3: Structure prototyping (D. Hicks)• Resolving material/structural prototypes • AFLOW Crystal Prototypes (encyclopedia) • AFLOW prototype generation

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Outline: Day 3• Session 4: Thermodynamic stability (C. Oses)

• Identifying stable vs. unstable structures • Applications: Rare-earth free Heusler magnets • AFLOW-CHULL

• Session 5: Thermodynamics of polar materials (R. Friedrich)• Thermodynamic stability of polar materials: CCE• Application: Thermodynamic stability of ternary oxides and halides • AFLOW-CCE

• Session 6: Disorder (C. Oses)• Resolving material/structural prototypes • AFLOW-POCC: Ensemble average properties • AFLOW-POCC algorithm• AFLOW-POCC via AFLOW-Online• AFLOW-POCC command line interface

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Outline: Day 4• Session 7: Thermomechanical properties (C. Toher)• AFLOW-AEL: Elastic properties• AFLOW-AGL: Debye model

• Session 8: AFLOW.org database and APIs (M. Esters)• AFLOW.org database structure • AFLOW REST-API • AFLUX: AFLOW Search API

• Session 9: Machine Learning (C. Toher)• PLMF representation• AFLOW-ML API

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Outline: Day 5• Session 10: AFLOW-XtalFinder (D. Hicks)

• AFLOW-Xtalfinder algorithm• AFLOW-Xtalfinder command line interface• AFLOW-Xtalfinder Python wrapper

• Session 11: Phonons in the Harmonic Approximation (M. Esters)• Introduction to phonons• AFLOW-APL: Phonon dispersions and phonon DOS• AFLOW-APL: Thermodynamic properties from phonons• AFLOW-APL: Visualizing atomic displacements

• Session 12: The Quasi-Harmonic Approximation (A. Smolyanyuk)• Introduction to the quasi-harmonic approximation• AFLOW-QHA: Calculating and visualizing thermodynamic properties

AFLOW: Integrated infrastructure for computational ...

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