ARTICLE

Nano-topology optimization for materials designwith atom-by-atom controlChun-Teh Chen 1, Daryl C. Chrzan1 & Grace X. Gu 2✉

Atoms are the building blocks of matter that make up the world. To create new materials to

meet some of civilization’s greatest needs, it is crucial to develop a technology to design

materials on the atomic and molecular scales. However, there is currently no computational

approach capable of designing materials atom-by-atom. In this study, we consider the pos-

sibility of direct manipulation of individual atoms to design materials at the nanoscale using a

proposed method coined “Nano-Topology Optimization”. Here, we apply the proposed

method to design nanostructured materials to maximize elastic properties. Results show that

the performance of our optimized designs not only surpasses that of the gyroid and other

triply periodic minimal surface structures, but also exceeds the theoretical maximum

(Hashin–Shtrikman upper bound). The significance of the proposed method lies in a platform

that allows computers to design novel materials atom-by-atom without the need of a pre-

determined design.

https://doi.org/10.1038/s41467-020-17570-1 OPEN

1 Department of Materials Science and Engineering, University of California, Berkeley 94720 CA, USA. 2Department of Mechanical Engineering, University ofCalifornia, Berkeley 94720 CA, USA. ✉email: ggu@berkeley.edu

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In 1959, Richard P. Feynman gave his famous talk entitled“There is Plenty of Room at the Bottom”1, which has inspiredthe field of nanotechnology and nanoscience. Nanotechnology

involves the manipulation of materials at the nanoscale and hashad significant impact on multiple research directions such asdrug delivery and tissue engineering in medicine2 and solar cellsin renewable energy applications3. Atoms are the building blocksof matter that make up the world. To create new materials tomeet some of civilization’s greatest needs, it is crucial to develop atechnology to design materials on the atomic and molecularscales. However, decades after Feynman envisioned the develop-ment of nanomachines, machines constructed of single atoms andmolecules, we are still not capable of realizing his original idea tocreate materials or machines with atom-by-atom control—arranging atoms one-by-one the way we want them. As of today,not only is there a lack of mature manufacturing technologies thatcan directly manipulate individual atoms to create materials butalso there is no computational approach capable of designingmaterials atom-by-atom. The ability to design materials withatomic-level precision will unleash the full potential of matter andcreate immense opportunities across a wide range of scientificand engineering fields.

Recent advances in additive manufacturing have opened thegate to complex and multiscale architected materials4–7. Forinstance, the photopolymerization-based two-photon lithography(TPL) technique can print arbitrarily complex three dimensional(3D) structures with submicrometer resolution8. To fully leveragethese new manufacturing technologies, it is essential to developcomputational approaches capable of optimizing materialsstructures at different length scales to achieve desired properties.Conventional materials discovery and design processes largelyrely on empirical, trial-and-error observations, which areexpensive and time-consuming. While the rational design ofmaterials is challenging, researchers have been turning to naturefor inspiration. This design approach is referred to as biomimicryand has led to many innovations in materials design such asnacre-inspired nanocomposites with high strength and hightoughness9,10, conch shell‐inspired composites with high impactresistance11, and bone-inspired materials with high fracturetoughness12. In addition to nature, researchers have been lookingfor inspiration from mathematics to design novel materials. Forinstance, triply periodic minimal surfaces (TPMS) have drawntremendous attention in the materials science community. Thereis a widely held view that cellular materials created by TPMS mayhave superior properties due to their unique geometric features.Cellular materials are made up of a representative unit cell that isrepeated throughout. Light-weight cellular materials have variousproperties beyond solid materials. From a mathematical point ofview, TPMS structures are interesting as their surfaces have zeromean curvature and are characterized by local area-minimizing.In the TPMS family, the gyroid is the most widely studiedstructure and has been found in nature such as the wing scales ofvarious butterflies. In recent years, extensive investigations ongyroid cellular materials have been reported to explain the physicsunderlying their mechanical13–16, thermal17, optical18, and elec-tromagnetic19 properties.

Although nature or mathematics is an important source ofinspiration, they should not be taken as the predominant guide todesign materials as there is no one structure that works best forevery purpose. Recently, machine learning (ML), a branch ofartificial intelligence (AI), has been perceived as a promising toolto design novel materials20–23. Nevertheless, using ML models forthe inverse design of materials with thousands of design variablesis still an active field of reseach24. In major engineering industries,a more mature design approach referred to as topology optimi-zation (TO) has been extensively implemented. TO provides

unrestricted design freedom and has been successfully applied toproblems with more than a billion design variables25. Theobjective of TO is to search for optimal shapes and materialdistributions to maximize the performance of materials orstructures such as aircraft and automotive components, buildingsand bridges, and cellular materials25–29. Despite a large numberof interesting shapes and designs that were proposed using TO,the design domains were always discretized using a finite elementmesh. For this reason, conventional TO approaches using thefinite element method (FEM) have been so far limited to thedesign of structures at the continuum scale and cannot be appliedto design materials at the atomistic level.

Here, we aim to bring the world closer to realizing Feynman’svision with a de novo TO approach capable of designing materialsat the nanoscale with atom-by-atom control. To distinguish ourTO approach using atomistic modeling from the conventionalTO approaches using FEM, we name the proposed method“Nano-Topology Optimization (Nano-TO)”. In this study, weapply Nano-TO to design nanostructured materials to maximizeelastic properties. Results show that the performance of ouroptimized designs not only surpasses that of the gyroid and otherTPMS structures but also exceeds the theoretical maximum thatis defined by the Hashin–Shtrikman (HS) upper bound30. Wedemonstrate that by optimizing the surface effect at the nanoscaleusing Nano-TO, the theoretical maximum of the bulk moduluscan be exceeded. The significance of the proposed method liesin a platform that allows computers to design novel materialsatom-by-atom without the need of a predetermined design. Weenvision that a broad array of novel nanomaterials and nano-machines with unprecedented performance can be designed usingNano-TO.

ResultsNano-topology optimization. In this study, we consider thepossibility of direct manipulation of individual atoms to designmaterials at the nanoscale. The flowchart of Nano-TO is shown inFig. 1. The objective of Nano-TO is to search for the best possibleatom distributions to maximize (or minimize) a desired propertyof nanostructured materials. Two types of atoms are considered:real and virtual. The atom type is allowed to switch during theoptimization process. A sensitivity analysis is performed to cal-culate the sensitivity value of each atom in a design domain (see“Methods” section). Conceptually, the sensitivity analysis evalu-ates the contribution of each atom to the objective function(desired property) and this information is used to redistribute theatoms in the design domain. Afterwards, a sensitivity filteringtechnique is applied to modify the sensitivity value of each atombased on a weighted average of the sensitivity values of otheratoms in a fixed neighborhood. The neighborhood region isdefined by the filter radius. The purpose of applying the sensi-tivity filtering technique is to obtain the sensitivity values ofvirtual atoms (see “Methods” section). The real atoms with thelowest sensitivity values are considered the most inefficient andwill be removed (converted to virtual atoms) in the next iteration.On the other hand, the virtual atoms with the highest sensitivityvalues will be added back (converted to real atoms) to the designdomain. The number of real atoms to be converted to virtualatoms and that of virtual atoms to be converted to real atoms arecontrolled by the rejection and admission rates, respectively. Asthe initial structure consists of mostly real atoms, to reach thetarget volume fraction (relative density), the rejection rate has tobe larger than admission rate in the first stage of the optimizationprocess. The net rejection rate can be defined as the rejection rateminus admission rate. The smaller the net rejection rate, the moreiterations are required to reach the target volume fraction.

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However, if the net rejection rate is too large, it can cause theoptimization to converge to a low-quality design or make theoptimization process unstable. After the target volume fraction isreached, the rejection and admission rates are set to be equal inthe second stage of the optimization process, until the optimi-zation is converged. Lastly, the atoms denoted by real will be keptand the atoms denoted by virtual will be removed from the designdomain. Consequently, a nanostructured material with an opti-mized atom distribution can be generated.

Objective of maximizing the bulk modulus. Nano-TO is ageneral-purpose design approach and can be applied to designmaterials at the nanoscale with different objectives. Here we applyNano-TO to design nanostructured materials with the objectiveof maximizing the bulk modulus. The bulk modulus representsthe resistance of a material to being elastically deformed by ahydrostatic pressure. Aluminum is selected as the base materialand the initial structure is created based on the face-centeredcubic (FCC) structure. The interactions between atoms aredescribed by the embedded atom method (EAM)31. The designdomain is a cubic unit cell with a length of approximately 4 nm.Periodic boundary conditions are imposed along the x-direction,y-direction, and z-direction to create the supercell structure forevaluating the macroscopic elastic properties. The initial structureconsists of 4000 atoms. If all atoms are set to be real atoms andperiodic boundary conditions are imposed, each atom in thedesign domain is identical and will have the same sensitivityvalue. For this reason, an atom at a random location is set to be avirtual atom to introduce an imperfection in the initial structure.For the optimization parameters, the target volume fraction is setto be in a range of 50–80%. In the first stage, to ensure thestability of the optimization process, the rejection and admissionrates are set to be 2 and 1, respectively. Consequently, two realatoms with the lowest sensitivity values are converted to virtualatoms while a virtual atom with the highest sensitivity value isconverted to a real atom in each iteration, until the target volumefraction is reached. In the second stage, the rejection andadmission rates are both set to be 1, until the optimization isconverged. The filter radius is chosen to be slightly smaller thanthe lattice constant of the base material (i.e., aluminum), which isapproximately 4 Å. Consequently, the 12 nearest neighbors ofeach atom are considered when the sensitivity filtering technique

is implemented. Note that using a larger filter radius will causethe optimized designs to lose topological details (undesirable inthis case) and increase the computational cost as more neigh-boring atoms have to be considered in the optimization process.

Nano-TO is a gradient-based design approach and theoptimized designs would vary with the selection of the initialstructure. To ensure that the optimized designs are of highperformance (strong local minima), we use a strategy of randominitialization (see “Methods” section). Starting with differentinitial structures, a total of 16 designs with a volume fraction of50% are generated by Nano-TO. Those designs have the bulkmodulus in a range of 18.25–22.20 GPa, with an average of 20.95GPa. The design with the highest bulk modulus is denoted byNano-TO design and selected for further examination. TheNano-TO designs with varying volume fractions are shown inFig. 2a, b. Periodic images of the Nano-TO design with a volumefraction of 50% are shown in Fig. 2c and Supplementary Fig. 1.The structural evolution during the optimization process ispresented in Supplementary Movies 1 and 2. It can be seen in thefigures that the Nano-TO designs are nearly cubic symmetric.Furthermore, the virtual atoms form truncated octahedronstructures in the body-centered cubic (BCC) arrangement. Thesurfaces in the Nano-TO designs are mostly {111} and {100}.Interestingly, the thermodynamically most stable structure (Wulffpolyhedron) for a simple metal with a FCC structure (e.g.,aluminum) is also the truncated octahedron exposing the faces{111} and {100}32. At present, it is unclear whether the optimalstructures for maximizing the bulk modulus would be related tothe Wulff polyhedron of the base material. Future studies arerequired to provide physical insight into this finding. Anothercase study for maximizing the elastic constant of C33 is reportedin Supplementary Notes, Supplementary Figs. 2–4, Supplemen-tary Movies 3 and 4.

Comparison of Nano-TO designs with TPMS structures. Toevaluate the capability of the proposed method, the Nano-TOdesigns are compared with TPMS structures including the gyroid,Schwarz D (diamond), and Schwarz P (primitive). Porousmaterials can be considered as two-phase composites with thesolid and void phases. It has been shown that the HS bounds givean accurate estimate for the effective moduli of two-phase com-posites30. The HS upper bound is applied to calculate the

Start

Define design domain, initialstructure, and boundary conditions

Define target volume fraction v*,filter radius rmin, rejection rate �r,and admission rate �a (�r > �a)

Set rejection rate �r to beequal to admission rate �a

Filtering sensitivity

Sensitivity analysis

Atomistic modeling No

No

Yes

Stage 1

Stage 2

Convergence?

Generate new designby removing and

adding atoms

Volume constraintsatisfied?

r

Initial structure

Yes

End

Fig. 1 Flowchart of Nano-TO. The flowchart shows the approach of Nano-TO to design materials at the nanoscale with atom-by-atom control. The blackand red arrows represent the two optimization stages (stage 1 and 2), respectively.

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theoretical maxima of the bulk modulus for varying volumefractions (see “Methods” section). The bulk moduli of the Nano-TO designs and TPMS structures are shown in Fig. 3a. It can beseen in the figure that the bulk moduli of the Nano-TO designssurpass those of the TPMS structures with the same volumefraction. The result indicates that the TPMS structures are quitefar from the optimal structures for maximizing the bulk modulus.Surprisingly, the bulk moduli of the Nano-TO designs exceed theHS upper bound. For instance, the bulk modulus of the Nano-TOdesign with a volume fraction of 50% is 22.20 GPa and the HSupper bound for the same volume fraction is only 19.63 GPa. Wefurther confirm that the bulk moduli of the Nano-TO designs areindeed higher than the HS upper bound after considering theanisotropy effect (see “Methods” section and SupplementaryFig. 5).

Exploring the surface effect. The surface effect can no longer beneglected when the cell size of a material is reduced to a fewnanometers. It is important to understand whether the Nano-TOdesigns are still superior to the TPMS structures at a larger scale.Here we take the Nano-TO design with a volume fraction of 50%as a template and parametrize the design based on vacancies withtruncated octahedron structures in the BCC arrangement tocreate the same design with varying cell sizes. The Nano-TOdesign and TPMS structures with varying cell sizes from 4 to 64nm are created (Supplementary Fig. 6) and their bulk moduli areshown in Fig. 3b. It can be seen in the figure that the bulk moduliof the Nano-TO design and TPMS structures vary with the cellsize since the surface-to-volume ratio decreases with the cell size.This size dependence effect at the nanoscale provides an oppor-tunity to design novel materials with superior properties. When

the cell size is 16 nm or larger, the surface effect in those struc-tures is negligible. The bulk modulus of the Nano-TO design isalways higher than those of the TPMS structures regardless of thecell size. Furthermore, the bulk modulus of the Nano-TO designexceeds the HS upper bound (19.63 GPa) when the cell size issmall (e.g., 4 nm) and converges to the HS upper bound when thecell size is large enough. Hence the proposed method shows thatthe theoretical maximum bulk modulus is attainable in practicefor this system and identifies a structure with that maximum bulkmodulus. To shed light on the superiority of the Nano-TO designcompared to the TPMS structures, the atomic strain (εzz) dis-tributions of those structures subjected to a constant hydrostaticstrain of −10−2 are shown in Fig. 3c. The cell size in the com-parison is 16 nm and the volume fraction is 50%. The averageatomic strains for the Nano-TO design, gyroid, diamond, andprimitive are −0.0048, −0.0033, −0.0035, and −0.0029, respec-tively. The average atomic strains of εxx and εyy are the same asthat of εzz due to the symmetry of those structures. The resultshows that the Nano-TO design has a better load transfermechanism as the average atomic strain (compressive) is largerthan those of the TPMS structures. This is probably due to thestructural simplicity of the Nano-TO design compared to thecomplex TPMS structures. Consequently, a higher bulk modulusis achieved as more elastic strain energy can be accommodated.Note that the TPMS structures in the comparison are based onsolid-networks. Another comparison with the TPMS structuresbased on sheet-networks is reported in Supplementary Notes,Supplementary Figs. 7 and 8.

Exceeding the theoretical maximum. To understand how thebulk modulus of a material can exceed the theoretical maximum,

80%

a

b

c

20%

{100}

[001]

[010]

x

y

z

[100]{111}

70%

30%

60%

40%

50%

50%

Fig. 2 Nano-TO designs for maximizing bulk modulus. a The Nano-TO designs for maximizing the bulk modulus with varying volume fractions from 50 to80%. The bulk atoms are shown in green and the surface atoms are shown in gray. b The corresponding distributions of virtual atoms. c Periodic images(3 × 3 × 3) of the Nano-TO design with a volume fraction of 50% and the corresponding distribution of virtual atoms. The surfaces in the Nano-TO designsare mostly {111} and {100}. The virtual atoms form truncated octahedron structures in the BCC arrangement.

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slice views of the Nano-TO design with a volume fraction of 50%are shown in Fig. 4a to reveal the interconnectivity. It can be seenin the figure that the Nano-TO design consists of nanoplatestructures with {111} surfaces. Depending on the cell size, thenanoplate structures consist of varying numbers of surface andbulk layers. For instance, in the unit cell with a length of 4 nm, thenanoplate structures are three atomic-layer thick, including twosurface layers and one bulk layer. When the cell size is increasedto 16 nm, the nanoplate structures become 12 atomic-layer thick,including 2 surface layers and 10 bulk layers. Elastic properties ofthe surface material are different from those of the bulk mate-rial33. To quantify the differences, an atomistic model of ananoplate with a free surface {111} on each side is created andshown in Fig. 4b. Periodic boundary conditions are imposedalong the in-plane directions (i.e., ½1�10� and ½11�2�). Vacuumregions are created for the top and bottom of the nanoplate torepresent two flat free surfaces. In this specific model, thenanoplate consists of 12 layers including 2 surface layers and 10bulk layers. Other models with varying numbers of layers from 3to 48 are also created. We first investigate extreme cases with theminimum and maximum thicknesses. The nanoplate consists ofthree layers including two surface layers and one bulk layer areconsidered as the thinnest possible nanoplate and is denoted bysurface model. On the other hand, the nanoplate with the infinitenumber of layers, denoted by bulk model, is created by removingthe vacuum regions and applying a periodic boundary conditionalong the out-of-plane direction (i.e., [111]). We apply normalstrains along the two in-plane directions in a range of −10−2 to10−2 on those two models and the corresponding strain–energycurves are shown in Fig. 4c.

The result shows that the material with free surfaces iselastically stiffer, indicating that the {111} surfaces are stiffer thanthe bulk material in the ½1�10� and ½11�2� directions. Furthermore,the Young’s moduli of nanoplates in the in-plane directions arethe same since the strain–energy curves in the ½1�10� and ½11�2�directions are identical. To quantify how much stiffer the materialwith free surfaces is compared to the bulk material, the modulusratios in the two in-plane directions for the nanoplates withvarying numbers of layers are shown in Fig. 4d. The modulusratio is defined as the Young’s modulus of a nanoplate divided bythat of the bulk material. The result shows that the nanoplateconsisting of three layers is the stiffest, which is more than 30%stiffer than the bulk material. Note that surfaces can be softerthan the bulk material as the lower atomic coordination onsurfaces tends to make them softer (see Supplementary Notes).However, in certain combinations of surface orientation andloading direction, the electron redistribution on surfaces may giverise to stronger bonding, and thus making them stiffer than thebulk material34. Whether a surface is stiffer or softer depends onthe competition between the electron redistribution and the loweratomic coordination on the surface.

DiscussionWe demonstrate that Nano-TO can utilize the surface effect inthe design of nanostructured materials. By optimizing the surfacetopology at the nanoscale, the HS upper bound for the bulkmodulus can be exceeded. Note that the surface effect comes fromthe variation of bond strength with coordination, which can onlybe captured in atomistic simulations. Therefore, the conventional

50aNano-TO (T)

HS boundPrimitive (P)Diamond (D)Gyroid (G)

40

30

Bul

k m

odul

us (

GP

a)20

10

0.8

–0.02 0.02�zz

0.5 0.70.6

Volume fraction

600 4020

Unit cell size (nm)

30

25

20

Bul

k m

odul

us (

GP

a)

15

10

5

Nano-TO (T)

HS boundPrimitive (P)Diamond (D)Gyroid (G)

[001]

[010]

x

T y

z

[100]

G D P

b

c

Fig. 3 Performance of Nano-TO design and TPMS structures. a The bulk moduli of the Nano-TO design (blue circles), gyroid structure (red squares),diamond structure (yellow diamonds), and primitive structure (purple triangles) with varying volume fractions in comparison with the HS upper bound(green line). The cell size in the comparison is 4 nm. b The bulk moduli of the Nano-TO design (blue circles), gyroid structure (red squares), diamondstructure (yellow diamonds), and primitive structure (purple triangles) with varying cell sizes from 4 to 64 nm in comparison with the HS upper bound(green line). The volume fraction in the comparison is 50%. c Atomic strain (εzz) distributions of those structures subjected to a constant hydrostatic strainof −10−2. The cell size of those structures is 16 nm and the volume fraction is 50%. The average atomic strains for the Nano-TO design (T), gyroid (G),diamond (D), and primitive (P) are −0.0048, −0.0033, −0.0035, and −0.0029, respectively.

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TO approaches using FEM will not generate the Nano-TOdesigns shown in this study. We show the applications of Nano-TO on the design of nanostructured materials for maximizing adesired property, in which two objectives, the bulk modulus andelastic constant of C33 (see Supplementary Notes), are considered,respectively. Various multiobjective optimization methods35 canbe implemented in Nano-TO to design nanostructured materialswith multiple desired properties. The most common approach isthe weighted sum method. To apply this method, the sensitivityvalues of each atom for different objectives are calculated indi-vidually and the weighted sensitivity value is calculated bychoosing proper weights (user’s preference) for different objec-tives. Consequently, optimized designs with the best tradeoffbetween competing objectives can be generated by Nano-TO.Although the examples presented here focus on elastic moduli,Nano-TO can be applied to design nanomaterials with otherproperties. It has been shown that the mechanical properties ofmetallic materials are highly related to their elastic moduli. Forinstance, an elastic anisotropy parameter can be used to identifyalloys that display super elasticity, super strength, and highductility, known as gum metals36. If the correlations between theelastic moduli of a material and its other mechanical properties(e.g., failure strain, strength, and toughness) can be discovered,the same design approach to tailor materials’ elastic moduli canbe applied to tailor materials’ other mechanical properties.

The reliability of Nano-TO depends on the accuracy of theatomistic modeling implemented in the optimization process. Inthis study, we choose aluminum as the base material since thereare interatomic potentials available in the literature to accuratelyreproduce basic equilibrium properties of aluminum including itselastic constants, vacancy formation and migration energies, andsurface energies. With accurate interatomic potentials, Nano-TO

can be applied to design nanostructured materials using otherbase materials (e.g., copper, nickel, and gold). Most TO problemsat the continuum-scale, except for some simple cases, are non-convex optimization problems, which contain many localminima29. We find that this is also the case in the Nano-TOexamples presented in this study since the optimized designs aregenerally dependent on the initial structure. We show that Nano-TO is capable of identifying the optimal designs by using severaldifferent initial structures. However, this dependence on theinitial structure could become a computational bottleneck whenapplying Nano-TO to large-scale materials design problems as thecomputational cost is increased. Therefore, further improvementsof the Nano-TO approach are essential to make it less sensitive tothe initial structure and prevent generating low-quality designs.

A typical material with a volume of a few cubic centimetersconsists of around 1023 atoms. Even if the volume is reduced to afew cubic micrometers, the material still consists of around 1011

atoms. To achieve atomic-level precision, each atom is a designvariable. Therefore, the computational cost to design materials ona scale of only a few micrometers is already beyond currentcomputational capabilities. However, this computational bottle-neck could be overcome in the future using AI and ML techni-ques. Future studies are required to develop suitable MLtechniques to replace the computationally expensive atomisticmodeling in the sensitivity analysis. We envision that this ML-based approach could potentially reduce the computational costof Nano-TO by several orders-of-magnitude.

MethodsCellular materials based on TPMS. Three lattice types based on TPMS areinvestigated: the gyroid (G), diamond (D), and primitive (P). Their level surface

[001]

a

[010]

x

yz

[100]

6× 10–4

4

2

Str

ain

ener

gy (

eV/a

tom

)

0

–2–0.01 0.01 0

1.3

1.2

Mod

ulus

rat

io

1.1

1

10 20 30

Number of layers

40 50–0.005 0.005

Surface [110]–

Surface [112]–

Bulk [110]–

Bulk [112]–

0

Strain

[111]

Surface{111}

Surface{111}

Bulk atoms

[112]–

–x

yz

[110]

–{111}/[110]–{111}/[112]

b

c d

Fig. 4 Surface effect in nanostructured materials. a Slice views of the Nano-TO design with a volume fraction of 50% to reveal the interconnectivity. Thebulk atoms are shown in green and the surface atoms are shown in gray. The length of the unit cell on the left is 4 nm and that on the right is 16 nm.The yellow boxes indicate a nanoplate structure with surfaces {111}. b The atomistic model of a nanoplate with surfaces {111}. c Strain–energy curves forthe bulk model in the ½1�10� (blue solid line) and ½11�2� (red dashed line) directions, and for the surface model in the ½1�10� (yellow solid line) and ½11�2� (purpledashed line) directions. d The modulus ratios in the ½1�10� (blue circles with solid line) and ½11�2� (red squares with dashed line) directions for the nanoplateswith varying numbers of layers. The modulus ratio is defined as the modulus of a nanoplate divided by that of the bulk material.

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approximations are37:

UG ¼ sin 2πxa

� �cos 2π

ya

� �þ sin 2π

ya

� �cos 2π

za

� �

þ sin 2πza

� �cos 2π

xa

� �� t;

ð1Þ

UD ¼ sin 2πxa

� �sin 2π

ya

� �sin 2π

za

� �

þ sin 2πxa

� �cos 2π

ya

� �cos 2π

za

� �

þ cos 2πxa

� �sin 2π

ya

� �cos 2π

za

� �

þ cos 2πxa

� �cos 2π

ya

� �sin 2π

za

� �� t;

ð2Þ

UP ¼ cos 2πxa

� �þ cos 2π

ya

� �þ cos 2π

za

� �� t; ð3Þ

where a is the length of a cubic unit cell and t is the threshold of the surface. Thesurface is generated by finding U= 0. Unit cells with a finite volume are created byin-filling one side of the surface. When t is 0, a porous structure with a relativedensity (volume fraction) of 0.5 will be created. Porous structures with varyingvolume fractions can be created by adjusting the value of t (SupplementaryTable 1). The maximum volume fraction is set to be 80% (i.e., 80% solid phase and20% void phase). The minimum volume fraction is set to be 50% to avoidinstability of porous structures due to a large portion of surface atoms in a smallunit cell.

Sensitivity analysis. Pair potentials (e.g., Lennard–Jones) do not include theenvironmental dependence of bonding. Therefore, the strength of individual bondsin the bulk is the same as that on (or near) the surface, which is physically not true.This local environmental dependence is especially important for simulations ofsurfaces and can be considered in many-body potentials such as the EAMpotentials31. The total potential energy of a material system consisting of N atomsin the EAM potentials is given by:

Etot ¼XNi¼1

FαXNj≠i

ρβ rij� � !

þ 12

XNj≠i

ϕαβ rij� �( )

; ð4Þ

where rij is the distance between atoms i and j, Fα is the embedding energy to placeatom i of type α into the electron cloud, ρβ is the contribution to the electroncharge density from atom j of type β at the location of atom i, ϕαβ is the pair-wisepotential energy between atom i of type α and atom j of type β. Since theembedding energy term considers the local background electron density of atoms,the EAM potentials can describe the variation of bond strength with coordination.Therefore, the EAM potentials are applicable for modeling material systems withsurfaces or other crystalline defects as those investigated in this study. To adopt anEAM potential in Nano-TO, the potential is modified in the way that the designvariables are integrated into the total potential energy of a material system. Themodified EAM potential is:

Etot ¼XNi¼1

xiFαXNj≠i

xjρβ rij� � !

þ 12xiXNj≠i

xjϕαβ rij� �( )

; ð5Þ

where x is a design variable, in which 1 indicates that the corresponding atom iskept and 0 indicates that the atom is removed. Here, instead of removing atomsfrom a material system, atoms are converted to virtual atoms. Therefore, thoseatoms with the design variable of 1 are referred to as real atoms and the others withthe design variable of 0 are referred to as virtual atoms. When a material system issubjected to a constant external strain (e.g., εzz), the elastic strain energy of thesystem can be calculated as the energy difference between the total potential energyin the deformed state and that in the equilibrium state. The calculation is:

Eela ¼XNi¼1

xiFαXNj≠i

xjρβ rij;def� � !

þ 12xiXNj≠i

xjϕαβ rij;def� �( )

�XNi¼1

xiFαXNj≠i

xjρβ rij;equ� � !

þ 12xiXNj≠i

xjϕαβ rij;equ� �( )

;

ð6Þ

where rij,def is the distance between atoms i and j in the deformed state and rij,equ isthe distance between atoms i and j in the equilibrium state. Compared to the pair-wise potential energy ϕαβ, the embedding energy Fα is less sensitive to a change ofrij (see Supplementary Notes, Supplementary Figs. 9 and 10). Thus, to simplify thecalculation, the elastic strain energy of the material system is approximated as:

Eela �XNi¼1

12xiXNj≠i

xjϕαβ rij;def� �( )

�XNi¼1

12xiXNj≠i

xjϕαβ rij;equ� �( )

; ð7Þ

Assuming that the objective is to maximize the elastic constant of C33. In thiscase, the elastic constant of C33 is associated with the external strain of εzz. Thus,maximizing the elastic constant of C33 is equivalent to maximizing the elastic strainenergy of the material system subjected to a constant external strain of εzz. This

approach can also be applied to optimize other elastic properties. For instance,maximizing the bulk modulus is equivalent to maximizing the elastic strain energyof the material system subjected to a constant hydrostatic strain. The optimizationproblem can be written as:

maxx

PNi¼1

12 xiPNj≠i

xjϕαβ rij;def� �( )

�PN

i¼1

12 xiPNj≠i

xjϕαβ rij;equ� �( ) !

subject to :PNi¼1

xiN ¼ v*

; ð8Þ

where v* is the prescribed volume fraction. Nano-TO uses a sensitivity analysis todetermine the atom rejection and admission. The gradient of the objective function(i.e., elastic strain energy) with respect to the design variable xk can beapproximated as:

∂Eela∂xk

� ∂

∂xk

XNi¼1

12xiXNj≠i

xjϕαβ rij;def� �( )

� ∂

∂xk

XNi¼1

12xiXNj≠i

xjϕαβ rij;equ� �( )

¼ 12

XN

j≠k

xjϕαβ rkj;def� �

þ 12

XN

i≠k

xiϕαβ rik;def� �

8<:

9=;

� 12

XN

j≠k

xjϕαβ rkj;equ� �

þ 12

XN

i≠k

xiϕαβ rik;equ� �

8<:

9=;

¼XN

j≠k

xjϕαβ rkj;def� �

�XN

i≠k

xiϕαβ rik;equ� �

� 2Ek;elaxk

;

ð9Þ

where Ek,ela is the elastic strain energy of atom k. The design variable xk can eitherbe 0 or 1. If atom k is a real atom (xk= 1), its sensitivity value (gradient) can beapproximated as two times of its elastic strain energy. On the other hand, if atom kis a virtual atom (xk= 0), its sensitivity value is undefined.

Sensitivity filtering. TO problems at the continuum-scale using FEM have shownthat modifying the sensitivity value of each element (finite element) based on aweighted average of the element sensitivity values in a fixed neighborhood is ahighly efficient way to ensure mesh-independency38. The filtered sensitivity valueof atom k is calculated as:

c∂Eela∂xk

¼ 1Pn

i¼1Hi

Pni¼1

Hi∂Eela∂xi

Hi ¼ rmin � dist k; ið Þ; i 2 njdist k; ið Þ≤ rminf g; ð10Þ

where n is the total number of atoms (including virtual atoms) in a fixed neigh-borhood of atom k, Hi is the weighting factor for atom i, dist(k, i) is the distancebetween atoms k and i, and rmin is the filter radius. Note that the sensitivity value ofa virtual atom is undefined. Thus, it is set to be zero in the sensitivity analysis.However, the filtered sensitivity value of a virtual atom could be nonzero whenthere are real atoms in its fixed neighborhood. This nonzero sensitivity informationfor virtual atoms is essential as it allows Nano-TO to determine which virtualatoms should be converted to real atoms in the optimization process.

Atomistic modeling. Full-atomistic simulations are implemented using Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)39. A modifiedEAM potential for aluminum is adopted for the simulations. The original versionof the EAM potential was developed by Mishin et al.40. The potential functions ofthe embedding energy Fα, atomic electron density ρβ, and pair-wise potentialenergy ϕαβ in the EAM potential are fitted to both experimental data and ab initiocalculations to accurately reproduce basic equilibrium properties of aluminumincluding its elastic constants, vacancy formation and migration energies, andsurface energies. To give Nano-TO the ability to remove an atom from a materialsystem and add it back when it is needed, a new atom type denoted by virtual isintroduced. When a real atom is converted to a virtual atom, the interactionsbetween the virtual atom and the other atoms in the material system are completelyturned off. Physically, the atom is removed from the material system as it does notinteract with the other atoms at all. However, the position information of thevirtual atom is reserved in order to convert the virtual atom back to a real atomwhen it is needed. To ensure that the modified EAM potential and elastic prop-erties calculations are accurate, we compare the lattice properties of aluminumpredicted in this study with those reported in the literature40. The comparison isshown in Supplementary Table 2 and the results are identical. The approach tocalculate the elastic properties is described in the Supplementary Methods. TheOpen Visualization Tool (OVITO)41 is implemented for the visualization andatomic strain analysis.

Hashin–Shtrikman (HS) bounds. The HS bounds provide theoretical upper andlower bounds for the effective elastic moduli of multiphase materials of arbitraryphase geometry30. The HS bounds for the bulk modulus of an, at least cubic

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symmetric, two-phase composite are given by:

KþHS ¼ K2 þ

v1K1 � K2ð Þ�1þ3v2 3K2 þ 4G2ð Þ�1 ; ð11Þ

K�HS ¼ K1 þ

v2K2 � K1ð Þ�1þ3v1 3K1 þ 4G1ð Þ�1 ; ð12Þ

where KþHS and K�

HS represent the upper and lower bounds, respectively. K is thebulk modulus, G is the shear modulus, and v is the volume fraction. The subscriptof K, G, and v denotes the phase of materials. The phase 2 material is assumed to bestiffer than the phase 1 material (K2 > K1; G2 >G1). For porous materials, the solidphase can be represented as the phase 2 material and the void phase as the phase 1material (K1= 0; G1= 0). The HS upper bound for the bulk modulus of a porousmaterial is given by:

KþHS ¼ K2 þ

v1�K2ð Þ�1þ3v2 3K2 þ 4G2ð Þ�1 : ð13Þ

The HS lower bound for the bulk modulus of a porous material is zero. TheVoigt–Reuss–Hill (VRH) average42 is applied to compute the shear modulus ofaluminum (nearly isotropic). This approach calculates the mean value of the upperand lower bounds of the effective shear modulus obtained through the Voigt andReuss assumptions:

GV ¼ C11 � C12ð Þ þ 3C44

5; ð14Þ

GR ¼ 5C44 C11 � C12ð Þ4C44 þ 3 C11 � C12ð Þ ; ð15Þ

Gavg ¼GV þ GR

2; ð16Þ

where GV and GR represent the upper and lower bounds, respectively. Gavg is theaverage shear modulus. The elastic constants used in the calculations are reportedin Supplementary Table 2. The average shear modulus (29.28 GPa) is used tocalculate the HS upper bound for the bulk modulus.

To eliminate the anisotropy effect of aluminum and ensure that the HS upperbound for the bulk modulus can certainly be surpassed by utilizing the surfaceeffect at the nanoscale, the range of the shear modulus of aluminum isinvestigated. For an anisotropic material, the shear modulus varies with the shearplane as well as the shear direction. The transformation of the shear modulus froma reference set of axes (1, 2, and 3) to a new set of axes (1′, 2′, and 3′) can bewritten as43:

G04 ¼

1s044

; ð17Þ

s044 ¼ s44 þ 4s11 � 4s12 � 2s44ð Þ a231a221 þ a232a

222 þ a233a

223

� �; ð18Þ

where G04 is the shear modulus for the new set of axes, sij is the elastic

compliance constant using the Voigt notation, and aim is the direction cosineindicating the angle between the i axis of the new axis system and the m axis ofthe reference axis system. Here, without loss of generality, the new axis 3′ is setto be the plane of shear and the new axis 2′ is set to be the direction of shear.After exploring all possible combinations of the shear plane and direction, themaximum shear modulus (Gmax) and minimum shear modulus (Gmin) ofaluminum are identified. Gmax is calculated as 31.60 GPa, which is associatedwith a shear in the ⟨100⟩ direction and is independent of the shear plane. Gmin

is calculated as 26.12 GPa, which is associated with a shear in the ⟨110⟩direction on a {110} plane. Gmax and Gmin represent the extreme values of theshear modulus of aluminum. Thus, the HS upper bounds calculated with thesetwo extreme values represent the limits when taking the anisotropy effect intoconsideration.

Data availabilityAll data needed to evaluate the conclusions in this study are present in the paper andSupplementary Information. Additional data related to this study are available from thecorresponding author upon reasonable request.

Code availabilityAll necessary information to generate the code used to evaluate the conclusions in thisstudy are present in the paper and Supplementary Information. The code used for theatomistic simulations is the open-source code, LAMMPS. The interatomic potential canbe found at http://www.ctcms.nist.gov/potentials. The script used to calculate elasticconstants can be found at https://github.com/lammps/lammps/tree/master/examples/ELASTIC.

Received: 29 January 2020; Accepted: 2 July 2020;

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AcknowledgementsWe acknowledge support from Extreme Science and Engineering Discovery Environ-ment (XSEDE) Bridges system by the National Science Foundation (grant number ACI-1548562), NVIDIA GPU Seed Grant, Haythornthwaite Foundation Research InitiationGrant, and the Savio computational cluster resource provided by the Berkeley ResearchComputing program.

Author contributionsC.-T.C., D.C.C., and G.X.G conceived the idea. C.-T.C. designed the theory and modelingapproach, implemented the simulations, and analyzed the data. C.-T.C. wrote themanuscript with valuable input from D.C.C. and G.X.G.

Competing interestsThe authors declare no competing interests.

Additional informationSupplementary information is available for this paper at https://doi.org/10.1038/s41467-020-17570-1.

Correspondence and requests for materials should be addressed to G.X.G.

Peer review information Nature Communications thanks Denvid Lau, Shiwei Zhou andthe other, anonymous, reviewer for their contribution to the peer review of this work.Peer reviewer reports are available.

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