tweetindex
PT

Olexandr Isayev 🇺🇦🇺🇸 ✓

@olexandr · Pittsburgh, PA · joined 16 Dec 2008

#CompChem Prof @CMU_Chem. Connecting chemical sciences with AI & #MachineLearning. #tarheels fan. Care:#design, #photography #Ukraine #cats🐈 Rants are mine

10 239Followers
3 764Following
13 593Posts total
27.4KViews on collected posts

Posts recentes

Exciting kick off of the @EnamineLtd drug discovery conference! #EnDDC Amazing science and great lineup of speakers. https://t.co/rmkYOsDXSg
703 views · 11 likes · 3 reposts · 0 replies Open on X →
ICLR Submission Number: 35164; early bird 🐦- we’ve sent paper way too early 🤣 #iclr2027 https://t.co/nBL0oyTR8e
GIF
1.4K views · 5 likes · 0 reposts · 0 replies Open on X →
About to board my plane to Riga! Honored to speak at the Enamine Drug Discovery Conference. Looking forward to seeing old friends and making new ones. In my talk I will feature CMU AI Founry - our autonomous science lab and advances with AI and robotic experimentation 🤖 795 views · 10 likes · 1 reposts · 0 replies Open on X →
Less than three weeks remain until the Enamine Drug Discovery Conference! Taking place on September 20-23, 2026, in Riga, Latvia, the conference will bring together experts in Organic, Medicinal, and Computational Chemistry, as well as Biology, to discuss current directions and
1.1K views · 1 likes · 0 reposts · 0 replies Open on X →
Shame on @Google @googlemaps ! You must be embarrassed https://t.co/c3i8UC8kzQ
766 views · 6 likes · 1 reposts · 0 replies Open on X →
@olexandr @ChemRxiv @NSF_CCAS @LosAlamosNatLab @gabepgomes @bobbypaton Congrats all! This is great 123 views · 3 likes · 0 reposts · 0 replies Open on X →
Despite being trained primarily on monophosphine ligands in Pd(0)/Pd(II) cycles, the model demonstrates good transferability to chemically diverse palladium complexes, including bidentate phosphines and dinuclear Pd(I) complexes. The speed of AIMNet2-Pd makes computational 939 views · 5 likes · 1 reposts · 0 replies Open on X →
The model can characterize entire catalytic cycles including geometry optimization, transition state searches, and energy calculations for each step of a catalytic process (oxidative addition, transmetalation, and reductive elimination). https://t.co/ZRoncXqL3E
904 views · 7 likes · 1 reposts · 0 replies Open on X →
AIMNet2-Pd enables rapid computational studies of Pd-catalyzed cross-coupling reactions by replacing computationally expensive QM calculations with a neural network while maintaining high accuracy (within 1-2 kcal mol⁻¹ and ~0.1 Å compared to reference quantum mechanics 874 views · 6 likes · 1 reposts · 0 replies Open on X →
Our latest @ChemRxiv preprint, "Transferable #MachineLearning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions" Collaboration with @NSF_CCAS @LosAlamosNatLab @gabepgomes @bobbypaton #compchem #NSFfunded https://t.co/lD176kTHqj https://t.co/tPKsGNvoK6
19.9K views · 116 likes · 15 reposts · 10 replies Open on X →

Em comparação com contas do mesmo porte

5 posts dos últimos 90 dias, ao lado da faixa de 10K–100K seguidores. aparece para muita gente, mas poucos desses espectadores reagem.

Mediana de visualizações795esta conta924mediana para 10K–100K
Alcance, %7.76%esta conta3.62%mediana para 10K–100K
Engajamento, %0.91%esta conta1.52%mediana para 10K–100K
MétricaEsta contaMediana para 10K–100KProporção
Mediana de visualizações por post7959240.86×
Alcance (visualizações ÷ seguidores)7.76%3.62%2.14×
Taxa de engajamento0.91%1.52%0.60×

Outras contas desta faixa →   Comparar com outra conta →   Como estas referências são construídas →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

19.9K18 Mar
874
904
939
123
76630 Aug
1.1K2 Sep
79519 Sep
1.4K
70320 Sep

Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.72%18 Mar
0.80%
0.88%
0.64%
2.44%
0.91%30 Aug
0.19%2 Sep
1.38%19 Sep
0.36%
1.99%20 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes72.6%170 in total
Reposts9.8%23 in total
Replies4.3%10 in total
Quotes0.9%2 in total
Bookmarks12.4%29 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Contas semelhantes