Xiaonan Wang

RT @EnvSciTech: In ES&T, @YongSikOk2 @xnwang07 and team show that active learning accelerates synthesis of engineered #Biochar with desired…

RT @NatureSynthesis: Now online: News & Views on the Article by @jionglu_NUS @xnwang07 Chun Zhang & co-workers @NUSingapore @Tsinghua_Uni…

Our #AIforScience platform is functioning more and more effectively and intelligently towards lots of intuitions and knowledge discovery in various materials systems! Congrats team! Take a read and expect more to come ;)

Jiong Lu @jionglu_NUS

Atomic robotic probe with chemists' intuition! Our latest work out @NatureSynthesis , Great collaboration with @xnwang07 from @Tsinghua_Uni , Zhang group from @ResearchFoS, Congrats Jie, Jiali, Na @ChemNUS, I-FIM @NUSResearch ! 👇👇https://t.co/oQ5AsWqSgx https://t.co/UkD4Ao0NZ0

RT @isciverse: Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning @J_A_C_S #Chemistry #Science https://t.co/mpvTFX1…

Check out our latest review on @acsnano discussing "#AI for #Nanomaterials Development in #CleanEnergy and Carbon Capture, Utilization and Storage (#CCUS)" https://t.co/StxF7ql6lg Embrace the "emergence" of large language models (#LLMs) in future #AIforScience applications!

RT @Tsinghua_Uni: As we integrate #AI into our lives, how can we make the most of this technology? According to Dr. Wang Xiaonan from the D…

RT @MRSBulletin: Using various methods, #intelligentlaboratories can autonomously carry out designed experiments and make scientific discov…

RT @KABrownLab: Our issue of @MRSBulletin on AI in experimental materials research is now out! Co-editor @Milad_RoboChem and excellent co…

Looking forward to the 27th Annual Green Chemistry & Engineering Conference! The award quotation very much motivates us to deliver more exciting work crossing disciplines in #AIforScience and #AI4Good, and to share it with as many people as possible.

ACS Green Chemistry Institute @ACSGCI

We are thrilled to have Profs. Xianon Wang @xnwang07, Kevin Leonard @LeonardLabKU, and Gyorgy Szekely @SzekelyGroup speak at the #gcande conference in partnership with @ACSSustainable! https://t.co/wgZcPzSVqZ

Congrats team! Another amazing #AI for #Materials application! #Smart sensing and avatar reconstruction would be useful in many restricted environments. My student Jiali Li contributed lots of his figures and body movement examples to test the sensors lol😁

Selina La Barbera @Selinitter

Yang et al. demonstrate a topographic design for wearable Ti3C2Tx #MXenesensor modules with wireless streaming or in-sensor computing models for avatar reconstruction #GettingApplied @NatureComms https://t.co/B6CJ4tmVKr

Machine learning may explain the world at the #quantum level! #DeepMind's #DM21 is one of the most amazing developments using #ML for #DFT. We are very honored to publish this preview article on #Matter! Thanks for the invitation.

Matter_CP @Matter_CP

Xiaonan Wang (@xnwang07) and team highlight the DeepMind 21 density functional, constructed via deep learning to solved fractional electron problems. #MatterIssue38 Preview: https://t.co/JQ3DYTrCrs @ScienceMagazine Article: https://t.co/6sQYks93Em https://t.co/jKfjmZhFPr

Nice tool! Congrats team! Look forward to the development of more and more #AIforScience toolbox platforms! We also designed some simple APIs for prediction of molecular optical properties etc. See https://t.co/x5l4MV4TpY associated with this AdvSci paper: https://t.co/Ao9LMIIpJD

Thank you #JACS community for this nice recognition! Honored to see that our paper collaborating with @Lugroup_nus was chosen as a Readers' Pick! Will the prize be another JACS perspective on #MachineLearning for #Chemistry?😄 A bright future awaits with more discoveries!

J. Am. Chem. Soc. @J_A_C_S

Machine Vision Automated Chiral Molecule Detection and Classification in Molecular Imaging @NUSingapore @ca2dmgrc #JACSReadersPick https://t.co/DWc6iBMaAO

RT @J_A_C_S: We asked the readers to pick their favorite JACS publication by participating in a poll that spanned three weeks...read on

Reading after the CNY of the Tiger: Our recent #Nature Machine Intelligence paper "Automatic strain sensor design via active learning and data augmentation for soft machines" Working towards the intelligent life-cycle design of sensors for soft machines! https://t.co/v5KznrtZRq