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pith:X5DVB4ZE

pith:2026:X5DVB4ZEU34GI4TW3QOE4EHLHM

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Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics

A.V. Mikheyenkov, E.A. Levashov, N.M. Chtchelkatchev, V.E. Valiulin

A neural-network interatomic potential locates eutectic compositions in ultra-refractory alloys by simulating only the liquid phase.

arxiv:2605.16091 v1 · 2026-05-15 · cond-mat.dis-nn · cond-mat.mtrl-sci

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Claims

C1strongest claim

The core of the algorithm is a machine-learning interatomic potential, based on a neural network, which achieves accuracy comparable to ab initio methods. Crucially, the algorithm operates effectively in the liquid phase, eliminating the need for information about the solid alloy's crystalline structure to estimate eutectic points.

C2weakest assumption

The machine-learning potential trained on the Ti-B-C system accurately captures the liquid-phase thermodynamics needed to locate the true eutectic composition, and the proposed criterion derived from it is transferable to other ultra-refractory multicomponent systems.

C3one line summary

A neural-network machine learning interatomic potential is used to estimate eutectic points in high-melting alloys by operating directly in the liquid phase without solid-structure input, demonstrated on the Ti-B-C system.

References

97 extracted · 97 resolved · 0 Pith anchors

[1] This approach enabled systematic sampling across the entire concentration space

[2] Kurchatov In- stitute 2000

[3] White, Takashi Goto, and Eliz- abeth C 2016

[4] Pollock, Dierk Raabe, Marc Andr´ e Meyers, Assel Aitkaliyeva, Kerri-Lee Chintersingh, Zachary C 2023

[5] Schreiber, Ruth Schwaiger, Martin Heilmaier, and Scott J 2022

Receipt and verification

First computed	2026-05-20T00:01:52.264661Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

bf4750f324a6f8647276dc1c4e10eb3b36bb42b766457e4692d9ee5309e2fe37

Aliases

arxiv: 2605.16091 · arxiv_version: 2605.16091v1 · doi: 10.48550/arxiv.2605.16091 · pith_short_12: X5DVB4ZEU34G · pith_short_16: X5DVB4ZEU34GI4TW · pith_short_8: X5DVB4ZE

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/X5DVB4ZEU34GI4TW3QOE4EHLHM \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: bf4750f324a6f8647276dc1c4e10eb3b36bb42b766457e4692d9ee5309e2fe37

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cond-mat.dis-nn",
    "submitted_at": "2026-05-15T15:48:27Z",
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