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Channel

Цифровая химия | ИОНХ РАН

@dIGIChemistry

On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Cite this entry

312subscribers

+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-1002594340926
TypeChannel
Username@dIGIChemistry
DescriptionНовости цифровой химии в России и мире Сотрудничество: Центр цифрового материаловедения ИОНХ РАН https://digimatter.ru/ e-mail: alex90pavlov@mail.ru
CreatedBetween 1 March 2025 and 31 July 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live8 August 2026
Measurements held2
On Telegramt.me/dIGIChemistry

Growth

3127 Aug 2026, 17:06 — 312 subscribers8 Aug 2026, 07:45 — 312 subscribers7 Aug 2026, 17:068 Aug 2026, 07:45
2 measurements taken within a single day. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 311–313 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
8 Aug 2026, 07:45312no change
7 Aug 2026, 17:06312first reading

Engagement

19 posts held, back to 16 January 2026the 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
246.3%
avg views ÷ 312 subscribers
Avg views / post
769
2 posts measured
Reaction rate
1.04%
reactions ÷ views · ER floor
Posts in window
2
of 19 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 7 August 2026
Posts held19 (16 January 20267 August 2026)
Views total1,537
Reactions total16
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken8 Aug 2026, 07:45 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
31
Videos
1
Links
33

Lifetime counters from Telegram’s own channel header, read 8 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.

Reaction mix

268 reactions across 19 posts, in 23 distinct kinds. The most used accounts for 27.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥7327.2%
6725.0%
👍6022.4%
custom 5458559694098407473217.84%
👏72.61%
🆒41.49%
🎉31.12%
🏆31.12%
👀31.12%
🗿31.12%
🥰31.12%
🦄31.12%
20.746%
💯20.746%
😐20.746%
🤔20.746%
🤣20.746%
🤯20.746%
🥱20.746%
❤‍🔥10.373%
3 further kinds31.12%

Custom emoji. One row above is a Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The count beside it isTelegram’s.

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 19 of the 19 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 268reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 19 most recent posts we hold, published 16 January 2026 to 7 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Telegram Stars

Stars received
4
across the posts below
Posts paid on
3
of 19 we hold a reading for · 16%
Most on one post
2
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @dIGIChemistry. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 19 most recent posts we hold for this entry, published 16 January 2026 to 7 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Recent posts

7 Aug 2026, 07:04 UTC267 views4 reactionsread 8 August 2026
Photo

Неопределённость как инструмент, а не побочный продукт предсказания Модели с оценкой неопределённости предсказаний существуют давно. Классический пример - регрессия на гауссовских процессах, где неопределённость по сути отражает, насколько новая точка "далека" от уже виденных данных обучающей выборки. Обычно эта неопределённость используется как индикатор доверия к прогнозу - сигнал "здесь модель уверена, а здесь не

3👍1

24 Jul 2026, 06:08 UTC≈1,270 views12 reactionsread 8 August 2026
Photo

Расшифровка масс-спектров при помощи ML: как выглядит прогресс, если присмотреться Примечание: работы по хемоинформатике публикуются отнюдь не только в рецензируемых журналах, но и в качестве препринтов на arXiv или openreview - последний вариант особенно распространен в ML-сообществе, где ключевые результаты всегда представляют на международных конференциях. Сегодняшняя статья - из последних, с воркшопа ICML GenBio

🔥63👍2🤨1

2 Jul 2026, 06:59 UTC307 views9 reactionsread 8 August 2026
Forwarded from @firstchemicalPhoto

Каскадное обучение — новый виток развития хемоинформатики В органической химии давно известно понятие «каскадная реакция». В таком процессе исходное вещество без удаления промежуточных продуктов последовательно превращается в конечное целевое соединение: на каждой стадии создаются именно те функциональные группы или реакционные центры, которые необходимы для следующего шага. Удивительно, но аналогию с каскадной реа

👍5custom 545855969409840747331

26 Jun 2026, 07:04 UTC373 views9 reactionsread 8 August 2026
Forwarded from @techinsiderruPhoto

Алгоритмы искусственного интеллекта способны моделировать структуру и свойства сложных химических соединений, но качество результатов напрямую зависит от данных, на которых обучали модель. Компания «Норникель» и Институт общей и неорганической химии им. Н.С. Курнакова РАН соберут базу для обучения ИИ на основе массива экспериментальных данных, накопленных за десятилетия. Подробнее читайте в нашем материале. Techins

custom 54585596940984074735🔥4

23 Jun 2026, 12:31 UTC≈2,760 views22 reactions1 Starread 8 August 2026
Photo

Спектроскопия ЯМР парамагнитных комплексов металлов: современное состояние Заведующий центром цифрового материаловедения ИОНХ РАН, д.х.н. Павлов А.А. и главный научный сотрудник ИФХЭ РАН, чл.-корр. РАН, д.х.н. Мартынов А.Г. опубликовали обзор в журнале «Успехи химии», посвященный современному состоянию спектроскопии ЯМР парамагнитных комплексов металлов. Настоящий обзор впервые предлагает целостный взгляд на парама

🔥126👍4

16 Jun 2026, 13:15 UTC≈1,850 views8 reactionsread 8 August 2026
Photo

🎓 В Институте Химии Силикатов им. И.В. Гребенщикова с 1 - 5 июня прошло «XXV Всероссийское совещание по неорганическим и органосиликатным покрытиям». Семинар-конференция посвящен получению и исследованию неорганических и органо-неорганических покрытий, как перспективному направлению современного материаловедения. 📌 Конференция была организована в далеком 1964 году, сначала как семинар, а затем переросла в крупное нау

6🔥2

15 Jun 2026, 05:26 UTC300 views14 reactionsread 8 August 2026
Photo

Расшифровка масс-спектров при помощи ML: как выглядит прогресс, если присмотреться Примечание: работы по хемоинформатике публикуются отнюдь не только в рецензируемых журналах, но и в качестве препринтов на arXiv или openreview - последний вариант особенно распространен в ML-сообществе, где ключевые результаты всегда представляют на международных конференциях. Сегодняшняя статья - из последних, с грядущего воркшопа I

8👍4🔥2

26 May 2026, 12:30 UTC≈2,230 views27 reactionsread 8 August 2026

Первый в России диссертационный совет по присуждению учёной степени доктора и кандидата наук по специальности 1.4.5 «Хемоинформатика» (химические науки) на базе ИОНХ РАН Специальность «Хемоинформатика» была официально введена в России приказом Министерства науки и высшего образования РФ № 118 от 24 февраля 2021 года. Однако до настоящего времени не существовало ни одного диссертационного совета по присуждению степен

🔥15👀3👏32💯2🤯2

30 Apr 2026, 14:14 UTC≈2,400 views14 reactionsread 8 August 2026

🧪 ИИ превзошел людей в зрении и текстах, но пасует перед химией. Почему? Вездесущий ИИ уже управляет автономными машинами и пишет сложные коды. Однако в химии машинное обучение до сих пор спотыкается. И проблема — не в мощности алгоритмов, а в методологии. Сотрудники Центра цифрового материаловедения ИОНХ РАН (Павлов А.А., Рекут Н.А., Злобин И.С.) проанализировали ситуацию и выделили 4 главных барьера современной х

👍9custom 545855969409840747332

24 Apr 2026, 11:42 UTC491 views11 reactionsread 8 August 2026
Forwarded from @chem_ml

📕JCTC и JCIM ужесточают требования к воспроизводимости https://pubs.acs.org/doi/full/10.1021/acs.jctc.6c00733 Вышел совместный editorial двух ведущих журналов по вычислительной химии — Journal of Chemical Theory and Computation и Journal of Chemical Information and Modeling. Главное: с 1 мая 2026 года все оригинальные исследовательские статьи обязаны сопровождаться данными и кодом, необходимыми для воспроизведения

👍8🤔2🦄1

13 Apr 2026, 14:17 UTC435 views9 reactionsread 8 August 2026
Forwarded from @platinuminfo

📚 Образовательная школа в ИОНХ РАН "ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ХИМИИ И МАТЕРИАЛОВЕДЕНИИ" 🗓 с 25 мая по 29 мая 2026 г. 📋 ИОНХ РАН, г. Москва, Ленинский проспект, 31. 📊 За 5 дней интенсивного обучения вы пройдете путь от основ хемоинформатики до создания собственных моделей машинного обучения для дизайна новых материалов и прогнозирования свойств химических соединений. Что вас ждет на курсе? 🔹 Введение в хемоинформат

7🔥2

9 Apr 2026, 17:29 UTC429 views18 reactionsread 8 August 2026
Photo

📌 7 и 8 апреля 2026 г. в рамках XVI Конференции молодых учёных по общей и неорганической химии состоялись доклады молодых сотрудников и студентов Центра цифрового материаловедения ИОНХ РАН. 🌟Беспалов Иван Андреевич в докладе "MAIGIC: программное обеспечение для гибкого моделирования магнитных свойств" представил работоспособный прототип разрабатываемого в Центре научного программного обеспечения для моделирования ма

8🔥5👏2custom 54585596940984074732👍1

Showing the 12 most recent of 19 posts we hold for @dIGIChemistry. 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.

Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.

Citation-graph rank

Citation-graph rank — 179,670 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

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.

Mentions

Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

Named by

Channels on the register whose posts name this channel's handle.

Names

Channels on the register whose handles appear in this channel's posts.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

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 8 August 2026 — this entry's latest reading, not the date you are reading this.

“Цифровая химия | ИОНХ РАН” (@dIGIChemistry), 312 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/dIGIChemistry.

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.