REPOGEO REPORT · LITE

JuDFTteam/best-of-atomistic-machine-learning

Default branch main · commit f4966001 · scanned 5/29/2026, 3:37:05 PM

GitHub: 690 stars · 66 forks

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface JuDFTteam/best-of-atomistic-machine-learning, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README opening to clarify it's a ranked list of open-source projects

Why:

CURRENT

🏆 A ranked list of awesome atomistic machine learning (AML) projects. Updated regularly.

COPY-PASTE FIX

🏆 A **ranked and curated list of open-source atomistic machine learning (AML) *projects***, not a software library or a collection of papers. Each project is scored based on various metrics for quality and impact, providing a unique resource for researchers and developers.

mediumcomparison#2

Add a 'What this list is (and isn't)' section to the README

Why:

COPY-PASTE FIX

### What this list is (and isn't)
This repository is a **ranked list of open-source atomistic machine learning projects**, scored by objective metrics. It is **not** a software library, a collection of research papers with summaries, or a simple unranked 'awesome list' of links. Our focus is on providing a data-driven overview of the most impactful and actively maintained projects in the field.

lowhomepage#3
Add the project homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://best-of.org
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface JuDFTteam/best-of-atomistic-machine-learning

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pymatgen

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

pymatgen · recommended 1×
matminer · recommended 1×
MEGNet · recommended 1×
M3GNet · recommended 1×
Roost · recommended 1×

CATEGORY QUERY
Where can I find high-quality open-source machine learning tools for materials science?
you: not recommended
AI recommended (in order):
1. pymatgen
2. matminer
3. MEGNet
4. M3GNet
5. Roost
6. Atomistic Simulation Environment (ASE)
AI recommended 6 alternatives but never named JuDFTteam/best-of-atomistic-machine-learning. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best atomistic machine learning projects for computational chemistry research?
you: not recommended
AI recommended (in order):
1. MACE
2. NequIP
3. Allegro
4. DPMD
5. SchNetPack
6. ASE
7. TorchANI
AI recommended 7 alternatives but never named JuDFTteam/best-of-atomistic-machine-learning. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of JuDFTteam/best-of-atomistic-machine-learning?
pass
AI did not name JuDFTteam/best-of-atomistic-machine-learning — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts JuDFTteam/best-of-atomistic-machine-learning in production, what risks or prerequisites should they evaluate first?
pass
AI named JuDFTteam/best-of-atomistic-machine-learning explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo JuDFTteam/best-of-atomistic-machine-learning solve, and who is the primary audience?
pass
AI did not name JuDFTteam/best-of-atomistic-machine-learning — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite