Pith Number

pith:5L3ATRCJ

pith:2025:5L3ATRCJO47MRY5FAQFBQP2XIW

not attested not anchored not stored refs pending

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Aditi Krishnapriyan, Ishan Amin, Sanjeev Raja

arxiv:2501.09009 v2 · 2025-01-15 · physics.chem-ph · cond-mat.mtrl-sci · cs.LG · physics.bio-ph

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{5L3ATRCJO47MRY5FAQFBQP2XIW}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Cited by

5 papers in Pith

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations

Receipt and verification

First computed	2026-07-05T10:07:47.706504Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

eaf609c449773ec8e3a5040a183f5745ad681e56e4a2398671d43d078dfcc06a

Aliases

arxiv: 2501.09009 · arxiv_version: 2501.09009v2 · doi: 10.48550/arxiv.2501.09009 · pith_short_12: 5L3ATRCJO47M · pith_short_16: 5L3ATRCJO47MRY5F · pith_short_8: 5L3ATRCJ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/5L3ATRCJO47MRY5FAQFBQP2XIW \
  | 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: eaf609c449773ec8e3a5040a183f5745ad681e56e4a2398671d43d078dfcc06a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e924a70503e127d40e88d41d59cd2f2abf5dcd162be6b315952d4bc3b4df0274",
    "cross_cats_sorted": [
      "cond-mat.mtrl-sci",
      "cs.LG",
      "physics.bio-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.chem-ph",
    "submitted_at": "2025-01-15T18:50:52Z",
    "title_canon_sha256": "af6b0a8b6159d89f002cd74a84a77068bf0efeb86485bc2c1dfdc75ac0a5f8a9"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2501.09009",
    "kind": "arxiv",
    "version": 2
  }
}