TICC 2026 · Invited Talk
Subsurface Control of Active-Site Distributions in Pt-Skin HEA Electrocatalysts
How buried atoms tune hydrogen adsorption across 96 distinct sites
96 DFT H sites
3 SQS Pt-skin slabs
≈0 ΔG H* (eV)
5 elements (HEA)
H*
Pt skin
HEA subsurface
Chen-Cheng Liao (廖振成) Department of Chemistry, Fu Jen Catholic University, New Taipei City, Taiwan DFT & machine learning · 165804@mail.fju.edu.tw
02
The thread of this talk
One question, followed from problem to design principle
1
A distribution, not a site
HEA activity is a population of local environments
2
96-site DFT census
three SQS Pt-skin slabs, every hollow site
3
Why correlations mislead
subsurface counts are compositionally coupled
4
What controls H binding
subsurface Pt, acting through the d-band
5
Engineer the distribution
composition as the tuning handle
Key message From "where is the active site?" to "what is the tuning handle?"
02 / 18
03
An HEA active site is a population, not a point
Activity emerges from many non-equivalent local environments
Conventional alloy
One environment → one ΔG
single ΔG
ΔG_H*
A few equivalent sites — design moves one number.
High-entropy alloy
A spectrum of ΔG H*
ΔG_H*
Every site sees a different mix — activity is a distribution .
Key message The object of study is the distribution of binding sites , not a single active site.
03 / 18
04
Why Pt-skin HEA for hydrogen evolution
The Sabatier optimum: bind H neither too strongly nor too weakly
ΔG_H* (eV)
HER rate
ΔG ≈ 0 (optimum)
Pt(111) −0.09
Pt-skin HEA
Pt binds H slightly too strongly — at full noble-metal cost.
A Pt skin keeps a Pt-like surface but cuts Pt content.
Key idea The buried subsurface atoms re-tune surface Pt.
Key message Pt-skin HEA offers Pt-like activity, less Pt, and a buried tuning handle .
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05
The central question — and a testable hypothesis
If composition is the lever, we can rank elements and see the electronics
How does subsurface composition govern the site-to-site spread in hydrogen adsorption?
A
Subsurface composition
what sits beneath the Pt skin
→
B
Surface-Pt d-band
local electronic structure
→
C
ΔG H* distribution
the resulting site population
Hypothesis A causal chain from buried atoms to surface reactivity — testable link by link.
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06
Methods overview
Fe–Co–Ni–Cu–Pt Pt-skin (111): from random structure to descriptors
1
SQS bulk
random alloy in a finite cell
2
Pt-skin slab
4×4×6 · 96 atoms · pure-Pt top
3
96 hollow sites
all FCC + HCP, 3 slabs
5
Descriptors
correlation · d-band · ML
DFT settings
VASP · GGA-PBE
Free energy
ΔG H* = ΔE ads + 0.24 eV
computational hydrogen electrode
SQS gives finite periodic slabs whose local statistics approximate a random alloy.
Interactive SQS explanation →
Key message A controlled 96-site census across three Pt-skin HEA surfaces.
06 / 18
What is a special quasirandom structure (SQS)?
A small periodic cell built to imitate an infinite random alloy
An SQS reproduces the local atomic statistics of a random alloy — in a cell small enough for DFT.
1
The problem
A real random alloy is effectively infinite — it cannot enter a DFT calculation directly.
→
2
The constraint
DFT needs a finite, periodic cell — whatever it contains repeats forever.
→
3
The trick
Choose the arrangement whose neighbour statistics match a random alloy.
Key message An SQS is statistically random — not merely random-looking.
Choosing an SQS: match the neighbours
Same composition, different arrangement — pick the one closest to random
Count each atom's neighbours: the fractions should match the bulk composition.
Minimise the mismatch — the Warren–Cowley parameter α → 0 — over the nearest shells.
See it live the interactive companion walks through this step by step.
Interactive SQS explanation →
Key message The SQS is chosen by neighbour statistics , not by eye.
Our model: a Pt-skin (111) high-entropy slab
Top view and side view of the 4×4×6 slab — 96 atoms, six layers
Top view — Pt skin (16 surface Pt)
H
hollow
4×4 surface cell · FCC + HCP 3-fold hollow sites (96 in total)
Side view — 6 layers
vacuum
H
Pt skin
HEA
subsurface
Pt skin on top · Fe–Co–Ni–Cu–Pt below (top 3 relaxed, bottom 3 fixed)
Key message A pure-Pt skin over the HEA subsurface — the surface looks like Pt; the layers beneath do the tuning .
What we count: the 6 atoms beneath a hollow site
An x-ray view from the Pt skin into the subsurface ring
Pt
Fe
Cu
Co
Ni
Pt
H
An H atom adsorbs in a 3-fold hollow site on the Pt skin.
Make the skin transparent: directly beneath sit 6 subsurface atoms — the local ring.
Descriptor ls_X = counts in that ring → here Pt 2 · Fe 1 · Co 1 · Ni 1 · Cu 1.
Key message Each site's descriptor is the element makeup of the 6 atoms under its hollow .
07
The site population sits on the Sabatier optimum
ΔG H* across all 96 sites — tight and near-thermoneutral
Figure 1. ΔG H* distribution over 96 FCC + HCP hollow sites (3 SQS slabs); Pt(111) reference −0.09 eV.
+0.013 eV
mean ΔG H* — essentially thermoneutral
0.055 eV
standard deviation — a narrow spread
92 %
of sites within |ΔG H* | ≤ 0.10 eV
Key message The Pt-skin HEA naturally creates a near-optimal population of H-binding sites.
07 / 18
08
A trap hides in the simplest analysis
The subsurface counts are not independent — they add to a fixed total
site
6 subsurface neighbours — fixed total
Add one Fe and you must remove another element. The five counts are compositionally coupled .
Caution A raw correlation for one element absorbs everyone else's effect — correlation, not cause .
The fix is partial correlation : hold the other counts fixed, then ask what each element does.
Key message In a constrained alloy, naïve correlation can point at the wrong element .
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09
The apparent ranking is misleading
Raw Pearson correlation of each subsurface count with ΔG H* (n = 96)
Apparent ranking (raw r vs ΔG H* )
ls_Pt −0.64
ls_Fe +0.54
ls_Cu −0.22
ls_Co +0.20
ls_Ni +0.02
Bars left of centre = strengthen H binding · right = weaken. Fe is flagged.
ls_Pt is genuinely the strongest signal (r = −0.64, p < 10−12 ).
Trap Fe looks important (+0.54) — but this is mostly Pt's complement : "few Pt here," not "Fe acting."
Cu and Co look weak here — only a method that removes the coupling can tell.
Key message At face value Fe rivals Pt — a classic collinearity artifact .
09 / 18
10
The corrected hierarchy follows chemistry
Partial correlation — after controlling for the other counts
Controlling for the other counts unmasks the true ranking
Fe collapses from apparent hero to weakest; Cu rises to second. The order tracks electronegativity (χ) , not arithmetic.
More-electronegative subsurface neighbours pull charge from surface Pt → weaker H binding.
Key message A stable, electronegativity-ordered descriptor hierarchy: Pt > Cu > Ni ≈ Co > Fe.
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11
Subsurface Pt is the dominant handle
More Pt beneath the skin → weaker H binding, ΔG H* rising through zero
Figure 3. ΔG H* vs subsurface Pt count; colour = local Fe count. Linear fit r = −0.64.
−0.64 r
monotonic trend in subsurface Pt count
0 → 3 subsurface Pt walks a site's ΔG H* from positive toward and past the optimum.
Sign convention: higher ΔG H* = weaker H binding.
Key message One countable quantity — subsurface Pt — predicts which way H binding moves.
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12
The electronics confirm the mechanism
A site-projected d-band-center effect, measured atom by atom
Figure 6. (a) ΔG H* vs surface-Pt d-band center (r = −0.86). (b) d-projected DOS for high vs low subsurface Pt.
−0.86 r
ΔG H* vs surface-Pt d-band center
subsurface Pt ↑ → d-band center ↓ → Pt–H weakens → ΔG H* → 0
Key message The descriptor is not a fit — it is the d-band center doing the work.
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13
The mechanism in one line
A closed causal chain, each link independently measured
1
Buried atoms
subsurface Pt count
→
2
d-band center ↓
surface-Pt states shift down
→
3
H binding weakens
ΔG H* rises toward 0
→
4
Sabatier population
sites land on the optimum
Key message Composition, electronics, and energetics tell one consistent story .
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14
Model complexity is not the bottleneck
Seven regression algorithms converge to the same error
Figure 4. Predicted vs DFT ΔG H* ; seven models, leave-one-out cross-validation.
27–31 meV
LOO-CV MAE — the band all seven models share
Ridge, LASSO, Random Forest, GBRT, SVR, GPR, KRR — same accuracy. Already inside GGA-PBE's ~0.1 eV uncertainty.
Key message To improve prediction, improve descriptors , not algorithms.
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15
Design rule: shift the distribution with composition
Engineer the ΔG H* population — centre and spread — not one site
ΔG_H* (eV)
ΔG = 0
starting alloy
tuned to optimum
Tune the subsurface Pt / Cu / Ni / Co / Fe balance to slide the whole population toward ΔG H* = 0.
The d-band center is the transferable bridge from composition to reactivity.
Key message Choose a subsurface composition that centres the site population on the optimum.
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16
Scope, honestly — and where it goes next
Firm within these compositions; the frontier is generalization
Established / limits
Hierarchy, d-band mechanism, error ceiling — robust within the studied compositions (96 sites)
Only three compositions: cross-composition transfer not yet proven
So far, only the electronic d-band descriptor transfers across compositions
Outlook
Broaden composition space — machine-learning potentials as a future sampling tool
Carry the subsurface framing to OER / ORR / CO2 RR
Toward descriptor-guided, autonomous catalyst discovery
Key message Within-composition claims are firm; cross-composition generalization is next.
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17
Three things to take home
Subsurface control of active-site distributions in Pt-skin HEA
1
HEA catalysis is a distribution problem
96 Pt-skin sites form a near-optimal population (μ = +0.013 eV; 92% within ±0.10 eV) — study the distribution, not one site.
2
Subsurface composition controls H adsorption through the d-band
An electronegativity-ordered hierarchy (Pt > Cu > Ni ≈ Co > Fe); subsurface Pt lowers the surface-Pt d-band center.
3
Descriptor quality, not model complexity, limits prediction
Seven algorithms converge to 27–31 meV — invest in better descriptors, not bigger models.
Key message HEA site heterogeneity is, at heart, a subsurface problem — and a solvable one.
17 / 18
Thank you · Questions welcome
Buried atoms are a design handle for surface reactivity
distribution on the optimum → electronegativity-ordered hierarchy → d-band mechanism → descriptor-limited ceiling
At a glance
System Fe–Co–Ni–Cu–Pt Pt-skin (111)
Data 96 DFT sites · 3 SQS slabs
Centre ΔGH* = +0.013 eV · 92% optimal
Driver subsurface Pt → d-band (r = −0.86)
Ceiling 7 models · 27–31 meV
Chen-Cheng Liao (廖振成) · Department of Chemistry, Fu Jen Catholic University165804@mail.fju.edu.tw · Supported by NSTC 114-2113-M-030-015-MY2 With students Peggy P. M. J. and Yu-Huan Huang (黃宇桓)
