Alqem AI & MPI CPfS Launch Project For New Magnetic Materials https://quantumzeitgeist.com/alqem-ai-mpi-cpfs-project-magnetic/

Channel
Computational Chemistry and Materials Engineering
@compchem_more
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
41subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1001667993004 |
|---|---|
| Type | Channel |
| Username | @compchem_more |
| Description | Channel covers the latest in computational chemistry and materials engineering. It dives into computer-aided chemical problem solving and the innovation of new materials, spotlighting software advancements, material design, and industry applications. |
| Created | 25 April 2023 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 10 August 2026 |
| Last confirmed live | 10 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/compchem_more |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 10 Aug 2026, 18:30 | 41 | no change |
| 7 Aug 2026, 19:48 | 41 | first reading |
Engagement
20 posts held, back to 14 April 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 24.4%
- avg views ÷ 41 subscribers
- Avg views / post
- 10.0
- 1 post measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 1
- of 20 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 20 July 2026 |
|---|---|
| Posts held | 20 (14 April 2025 – 20 July 2026) |
| Views total | 10 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 10 Aug 2026, 18:30 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
- Photos
- 2
- Links
- 474
Lifetime counters from Telegram’s own channel header, read 10 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Recent posts
Superconductivity Breakthrough: Hidden Order Found Inside Quantum Chaos https://scitechdaily.com/superconductivity-breakthrough-hidden-order-found-inside-quantum-chaos/
COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin–Orbit Couplings and Dynamics | The Journal of Physical Chemistry A https://pubs.acs.org/doi/10.1021/acs.jpca.5c02047
www.chemengonline.com https://www.chemengonline.com/sandboxaq-releases-ai-based-industrial-catalyst-dataset-with-quantum-chemistry-calculations/
Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges | Chemical Reviews https://pubs.acs.org/doi/10.1021/acs.chemrev.5c00259?mi=6l0kix4&af=R&AllField=anticancer&target=default&targetTab=std
AI Characterization Advances Tackle Exponential Scaling In Large-Scale Quantum Systems https://quantumzeitgeist.com/ai-systems-characterization-advances-tackle-exponential-scaling-large-scale/
A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists | Nature Chemistry https://www.nature.com/articles/s41557-025-01815-x
Machine learning-driven prediction of optical responses and inverse design of annular aperture arrays | Journal of Applied Physics | AIP Publishing https://pubs.aip.org/aip/jap/article/137/24/243105/3350839/Machine-learning-driven-prediction-of-optical
Accurate Predictions of Concrete in High Heat Conditions https://www.azobuild.com/news.aspx?newsID=23831
OTI Lumionics Releases Breakthrough Algorithms for Quantum Chemistry Simulations https://www.azoquantum.com/News.aspx?newsID=10845
(PDF) A step toward a micromechanics‐informed neural network for predicting asphalt mixture stiffness https://www.researchgate.net/publication/392941043_A_step_toward_a_micromechanics-informed_neural_network_for_predicting_asphalt_mixture_stiffness
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01008-1
Showing the 12 most recent of 20 posts we hold for @compchem_more. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 10 August 2026 — this entry's latest reading, not the date you are reading this.
“Computational Chemistry and Materials Engineering” (@compchem_more), 41 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/compchem_more.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.