Researcher @MSFTResearch @MSRNE; Community builder @AI_for_Science
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❓How can we build AI agents that do what scientists actually do? Is scientific discovery merely a search problem?
🚀 Meet SAGA: Scientific Autonomous Goal-evolving Agents. Five discovery tasks across chemistry, biology & materials science, with wet-lab validation.
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Machine learning for molecule design is a fast-growing field with massive literature, to the best of our knowledge, we are the first to **comprehensively** review this field, the preprint is now available at Arxiv arxiv.org/abs/2203.14500.
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🧵1/6 Introducing Diffusion Constrained Samplers 🥳🥳🥳 arxiv.org/abs/2402.18012
Interested in optimization problems where (partial) constraints are unknown (protein/molecule design)? We show diffusion models learn it implicitly and optimize feasible solutions thru sampling!
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🧵1/6 Happy to introduce ChemFlow:
Navigating Chemical Space with Latent Flows
arXiv: arxiv.org/abs/2405.03987
Demos are available at: colab.research.google.com/dr…
Flows can uncover meaningful structures of latent spaces learned by generative models!
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📢📢📢 Happy to introduce Graph Generative Pre-trained Transformers (G2PT):
Can we tokenize graphs and train an autoregressive (AR) model with generative pre-trained transformers to generate graphs?
A new work led by @XiaohuiC16528, @YinkaiW, @jacksonleihao.
A thread 🧵1/6
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If you are working on or interested in graph generation, you may want to check out our recent survey about deep graph generation with methods and applications, available in Arxiv now arxiv.org/abs/2203.06714.
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(1/3) I am thrilled to announce that @AI_for_Science workshop is back with #NeurIPS2023! This year we put together several new programs with a new theme "from theory to practice", including a panel discussion to align the expectation between academia and funding agencies.
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I’m moving to Bay Area this week and will spend my entire fall @nvidia, looking forward to catching up with old friends and meeting new friends! I’ve never lived in the Bay Area and I’m particularly excited to feel the vibe and talk to interesting people!
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Happy to announce our new initiative AI4Science101. We wrote a series of documents to encourage knowledge sharing and collection in AI for Science from both the view of AI and Science researchers to motivate them to learn, join and work on AI for Science.
link.medium.com/Trr6L1aOfrb
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(1/n) After a month of "on hold" on arXiv, I am excited to share our latest work on unlocking the potential of ML for materials discovery! ML has been successfully applied to modeling molecular structures, esp. biomolecules. arxiv.org/abs/2307.05378
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🌞🌞🌞 The third Structured Probabilistic Inference and Generative Modeling (SPIGM) workshop is **back** this year with @NeurIPSConf at San Diego!
In the era of foundation models, we focus on a natural question: is probabilistic inference still relevant? #NeurIPS2025
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🧵1/7 Introducing an “Encyclopedia” of Molecular Design with Machine Learning @NatMachIntell! nature.com/articles/s42256-0…
Collaboration with @arian_jamasb*, @JeffGuo__*, @TianfanFu, @charlieharris01, @yingheng_wang, @chenru_duan, @pl219_Cambridge, @pschwller, and Tom Blundell.
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Scientific Knowledge Emerges in LLMs and YOU CAN Access It (via sampling)!
🔥🔥🔥New blog to summarize what we have learned from evaluating LLMs for several optimization, decision-making, and planning problems in science with truly impressive performances!
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🧵1/n LLMs significantly improve Evolutionary Algorithms for molecular discovery! For 18 different molecular optimization tasks, we demonstrate how to achieve SOTA performance by incorporating different LLMs! Learn more in our new paper!
Website: molleo.github.io/(w/ Code)
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Last year, we started AI4Science101 (ai4science101.github.io) which aims to bridge AI and Science community with a series of blogs designed for beginners. After a year, we have 12 blogs and many more are coming soon! We hope you enjoy reading the blog and consider joining us!
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If you are looking for internships about probabilistic modeling, from fundamental research, scaling to frontier applications, apply to join the GenAIR team for the summer internship, here you find expertise about all of them!
📢 The Fundamental Generative AI Research (GenAIR) team at NVIDIA is looking for outstanding candidates to join us as summer 2026 interns.
Apply via: nvidia.wd5.myworkdayjobs.com…
Email: genair-openings@nvidia.com
Group website: research.nvidia.com/labs/gen… 👇
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2022 was a year of new and exciting ventures for me:
1. I stepped out of the ML industry and joined a startup led by a group of physicists, chemists, etc.
2. I launched AI4Science101, a blog series aimed at building a new knowledge system for AI for Science.
(1/n)
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I will add another one: a cold email to @wellingmax led to the tuning point of my research and the birth of @AI_for_Science workshop later on. At that time, I didn’t graduate from any big university, just with curiosity and passion on research about AI and Science …
Cold emails are hard and good ones can change a life. Here is my email to @NandoDF that started my career in ML (at the time I was a PM at Google) docs.google.com/document/d/1… Real effort (incl feedback) went into drafting it. Thanks to @EugeneVinitsky for nudging me to put it online
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🧵1/5 New perspective on how LLMs are catalyzing chemistry education! chemrxiv.org/engage/chemrxiv…
Fun collaboration with @chenru_duan*, @drecmb*, Anna Sotnikova, Yi Qu, @KulikGroup, @ABosselut, Jinjia Xu, and @pschwller!
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Hero #NeurIPS2022 @AI_for_Science organizers!
@TianfanFu @cwcoley @WenhaoGao1 me @wellingmax @chenru_duan @hcwww_ (from left to right)
Shout out to the other organizers as well! @DaisyYDing @AnimaAnandkumar Yoshua Bengio, Carla Gomes, Aviv Regev and @marinkazitnik
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Finally accepted @NeurIPSConf! Cannot wait to discuss molecular dynamics, transition path sampling and stochastic optimal control in the coming December!
#NeurIPS2023
Excited to share our latest work at the intersection of machine learning and computational chemistry; Path Integral Stochastic Optimal Control for Sampling Transition Paths between molecular conformations. With @YuanqiD*, @priyankjaini, Ferry Hooft, @BerndEnsing and @wellingmax
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One of the coolest topics to listen at Hawaii (ICML 2023) this summer: structured probabilistic inference & generative modeling! This field has been rapidly growing and can’t wait to follow the new frontier!
Our #ICML2023 workshop proposal "Structured Probabilistic Inference & Generative Modeling" has been accepted 🎉. We can't wait to engage in insightful discussions with experts in probabilistic ML and other areas at the beautiful Hawaii 🌴🏖️.
Check🔍: spigmworkshop.github.io/
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Afterall, DiffOPT is accepted in @aistats_conf 2025! Generative priors are natural for optimization esp. formulated as sampling! We also extend to derivative-free settings. Great work with @konglingkai_AI and Wenhao!
🧵1/6 Introducing Diffusion Constrained Samplers 🥳🥳🥳 arxiv.org/abs/2402.18012
Interested in optimization problems where (partial) constraints are unknown (protein/molecule design)? We show diffusion models learn it implicitly and optimize feasible solutions thru sampling!
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Revisited Prof. Weinan E’s opinion paper “The Dawning of a New Era in Applied Mathematics”! The history of applied math is very inspiring and “generalize to/align with” the development of comp. tool (numerical, CS, AI) in Science. Highly recommended!
ams.org/notices/202104/rnoti…
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Don’t miss the deadline for the Structured Probabilistic Inference and Generative Modeling workshop at ICML 2023 (May 26th)!
We are calling submissions covering all aspects of probabilistic machine learning!
Website: spigmworkshop.github.io/
Submission: openreview.net/group?id=ICML…
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ChemFlow will appear in NeurIPS 2024! See you in Vancouver this December!
#NeurIPS2024
🧵1/6 Happy to introduce ChemFlow:
Navigating Chemical Space with Latent Flows
arXiv: arxiv.org/abs/2405.03987
Demos are available at: colab.research.google.com/dr…
Flows can uncover meaningful structures of latent spaces learned by generative models!
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AI for Science is a fast-growing and promising field for both AI and Science community. We started building this community several years ago and are super excited to share the first paper by the community effort, lead by @marinkazitnik, co-lead by @hcwww_*, @TianfanFu* and me!
How can #AI transform science?
Let us count the ways
A brilliant review @Nature
nature.com/articles/s41586-0…
@marinkazitnik @TianfanFu @YuanqiD and colleagues
@AI_for_Science #ScienceTwitter
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✈️✈️✈️I am visiting Georgia Tech tmr and next Monday! I am flattered to give a talk at the Applied and Comp Math seminar. I will be sharing some of my work and thoughts on "Accelerating Molecular Discovery with ML: A Geometric, Sampling and Optimization Perspective"!
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Happy holidays, graph and geometric deep learning community! Do not forget that if you like to become one of the @LogConference organizers in 2024, you still have time to sign up for the application form forms.gle/taDhZFQ79v5aLe4e8. We will review them on January!
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Check out our new work in sampling transition paths! We derive a variational formulation for Doob’s h-transform (conditioned Brownian dynamics) and optimize efficiently that avoids importance sampling!
Best experience working with @k_neklyudov @MichaelPlainer @brekelmaniac
Simulating protein folding between two metastable states is possible without any training data! Our #NeurIPS2024 spotlight paper brings us one step closer to this goal!
arxiv.org/abs/2410.07974
This project would not see the light without @YuanqiD @MichaelPlainer @brekelmaniac
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Working on sampling and seeking neural network ansatz? Longing for simulation-free* training approaches? – we review neural samplers and present a “failed” attempt towards it with pitfalls and promises!
Joint work with @JiajunHe614 (co-lead), Francisco Vargas …
🧵1/n
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We tried this idea about two years ago. The interesting observation is that our architecture and modality are sensitive to different part of information thus make them “complement” each other. Glad this idea has been picked up. Joint work with @Zhu_Yanqiao arxiv.org/abs/2209.15101
Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry
Three chemical modalities are contrasted against each other and used for property prediction.
Unfortunately, only evaluated on the MoleculeNet benchmarks
arxiv.org/abs/2401.03369
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Finally, after two years, our paper is out @NatComputSci ! Over the past two years, the community has witnessed rapid growth from distribution learning and substructure design to the optimization and fine-tuning of generative models for designing various types of molecules!
🚨@rneschneuing, @charlieharris01, @YuanqiD, @mmbronstein, @befcorreia and colleagues evaluate how diffusion models can be used to address structure-based drug design problems. nature.com/articles/s43588-0…
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If you are traveling to @icmlconf 2024 next week, don't miss our workshop on structured prob. inference and generative modeling on Friday in room Lehar 3! We have a stellar list of speakers to share the frontiers of sampling, Bayesian inference, generative models, and beyond!
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Our new work on generative models for chemical reactions: much faster inference with flow matching (OT path) training scheme, better leveraging our knowledge about the problem is a key to solve science problems! Check out the paper if you are interested!
New paper alert: React-OT: Optimal Transport for Generating Transition State in Chemical Reactions (arxiv.org/abs/2404.13430). React-OT formulates TS search as a transport problem, approaching chemical accuracy while taking only 0.5 seconds in inference on a single GPU. #compchem
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🥳🥳🥳 The AI for Science slack channel has almost reached 1,000 users, super excited about this growth! Welcome anyone interested in AI for Science to join, chat and post any related events, resources, or hiring info! @AI_for_Science
join.slack.com/t/aiforscienc…
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The third year of @AI_for_Science workshop @NeurIPSConf, covering more diverse areas, featuring more speakers from science communities, @OpenCatalyst competitions, and a panel from funding agencies about the future of AI for Science! Join us on December 16th!
🎉 Join us at NeurIPS 2023 AI for Science Workshop on 12/16:
7 speakers on cutting-edge AI research across fields🧠
Future-focused panel with funding agencies 💼
Open Catalyst Challenge announcement 🏆
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Glad to be recognized as the top reviewer award from NeurIPS 2024 (in both main and dataset tracks), for the first time! I thought I was never a good reviewer b/c I really like to ask people to connect refs seemingly far from the literature of the paper, ...
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Hanging out with @LogConference heroes #NeurIPS2022
Looking forward to seeing you all again in another week @LogConference (please register if you haven’t!)
@Zhu_Yanqiao me @GabriCorso @dereklim_lzh @andreeadeac22 @HannesStaerk (from left to right)
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Delighted that our paper is the highlight of the December issue @NatComputSci!
📢Our December issue is now live! Highlights include an approach to identify transition state structures in chemical reactions, a denoising method for fluorescence images, and an approach to identify stable surface reconstructions of complex materials.
👉nature.com/natcomputsci/volu…
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Today is the best time to study AI; today is the best time to study science; today is the best time to study the bridge between computation and science.
Many great physicists in the history had deep thoughts about and inspired computation.
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
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I will be in NYC Dec 3-6th, traveling to NeurIPS Dec 10-17th, and staying around Seattle till January. Let me know if anyone would like to chat at any point if we come across! 🌟🌟🌟#NeurIPS2023
I am happy to chat anything about AI for Science and Science for/of AI/Science.
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MolLEO is accepted @iclr_conf! We have made so much progress to show LLMs really have tons of knowledge about science and it’s not just retrieving. LLMs easily beat SOTA molecule optimization methods with an evolutionary process!
🧵1/n LLMs significantly improve Evolutionary Algorithms for molecular discovery! For 18 different molecular optimization tasks, we demonstrate how to achieve SOTA performance by incorporating different LLMs! Learn more in our new paper!
Website: molleo.github.io/(w/ Code)
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I find this encouraging in my life, I was not trained enough in mathematics and physics in early years so I have a hard time catching up (slowly). I find it so relieving to admit things I don’t know and I’m learning. It doesn’t frighten me to ask question and understand why.
Don't get frightened by not knowing things. I have approximate answers, and possible beliefs, and different degrees of certainty about different things, but I'm not absolutely sure of anything. There are many things I don't know anything about. It doesn't frighten me.
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Very excited to announce the first conference dedicated to machine learning on graphs, together with @HannesStaerk @dereklim_lzh @chaitjo @andreeadeac22 @DutaIulia @Josh_d_robinson, big thanks to all the organizers and advisors!
Here it is: the first Learning on Graphs Conference! 🎊
We think this new venue will be valuable for the Graph/Geometric Machine Learning community.
What makes it so important+unique? See our blog post!
michael-bronstein.medium.com…
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Many people asked me why AI for Science and I answered with several arguments and beliefs in this blog. Some interesting observations and trends in 2023 and hope the momentum continues for 2024!
🚀 Exciting News! Our blog “AI for Science in 2023: A Community Primer” is now live! In this blog, we delve into how AI intersects with various scientific fields - from Chemistry, Biology, Physics, Computer/Math. Science, Neuroscience to Earth Science.
medium.com/@AI_for_Science/a…
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One more week to submit your work to @AI_for_Science workshop at @NeurIPSConf!
Do not miss this opportunity to attend one of the best annual events about AI for Science!
We are also soliciting education-related papers to lower the barrier to entering the field!
#NeurIPS2023
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MSR New England is a wonderful place to spend time! The office has beautiful view over Charles River. Summer Boston is great for both research and life!
If you're a PhD student interested in interning with me or one of my amazing colleagues at Microsoft Research New England (@MSRNE, @MSFTResearch) this summer, please apply here
jobs.careers.microsoft.com/g…
(If you'd like to work with me, please include my name in your cover letter!)
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When people are talking about AF3 and equivariance, it is a great time to advertise our @AI_for_Science workshop #icml2024, we feature the theme "Scaling in AI for Science", and there would be lots of fun discussing those questions! Info at ai4sciencecommunity.github.i…
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Check out our new paper using diffusion model for structure-based drug design! Diffusion models are particularly powerful with inpainting and could be suitable for many drug design scenarios. Play with our demo if you are interested!
Happy to share our new paper:
Structure-based Drug Design with Equivariant Diffusion Models
arxiv.org/abs/2210.13695
A demo is available on Google Colab: colab.research.google.com/gi…
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Met so many old and new friends! See you next time! #NeurIPS2023
Thanks for all the speakers, organizers, authors, reviewers, area chairs to make the AI for Science workshop a great success! Looking forward to meeting you in the coming year!
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Finally on Arxiv! Amazing collaboration with @HoldijkLars, @priyankjaini, Ferry Hooft, @BerndEnsing, @wellingmax while visiting @AmlabUva!
Excited to share our latest work at the intersection of machine learning and computational chemistry; Path Integral Stochastic Optimal Control for Sampling Transition Paths between molecular conformations. With @YuanqiD*, @priyankjaini, Ferry Hooft, @BerndEnsing and @wellingmax
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🥳🥳🥳 I wrote a short blog post on some of my personal experiences and thoughts on organizing academic events and community building (esp. in AI and ML) medium.com/@yuanqidu/about-a…
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Happy new year, my friends on twitter! 2023 has been a challenging yet awarding year for me: both in work and life: (1) going through early PhD crisis - find out what interests me the most and what are the essentials to support them, (2) putting consistent effort in education
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Super excited about this topic and see how scaling plays a role in AI for Science (alone from scaling that was long studied in science)! We also put together a highly diverse program to share ideas/lessons across different fields!
🥳🥳🥳 We are excited to share that AI for Science workshop will be held again with @icmlconf 2024, Vienna! This time, we focus on scaling in AI for Science (as a new dimension to theory, methodology and discovery)! Tentative schedules can be found: ai4sciencecommunity.github.i…
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Now accepted at @NeurIPSConf! Feel free to play with our code to adapt state-of-the-art ML models to your materials problems or develop your new models with diverse materials datasets!
#NeurIPS2023
(1/n) After a month of "on hold" on arXiv, I am excited to share our latest work on unlocking the potential of ML for materials discovery! ML has been successfully applied to modeling molecular structures, esp. biomolecules. arxiv.org/abs/2307.05378
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I really enjoyed reading this discussion from prestigious ML researchers about **now** and **future** of ML research, particularly @andrewgwils's view on discovering scientific theory, highly recommended, a wonderful holiday read!
arxiv.org/pdf/2312.09323.pdf
@AI_for_Science
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🚀🚀🚀 Sharing a new exciting work! Nature teaches us to think not only in forward, but also in backward. The counterintuitive backward process gives so much strength, not only in understanding out-of-equilibrium process, but also controlling and estimating with diffusion models!
[1/9]🚀Excited to share our new work, RNE! A plug-and-play framework for everything about diffusion model density and control: density estimation, inference-time control & scaling, energy regularisation. More details👇
Joint work with @jmhernandez233 @YuanqiD, Francisco Vargas
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Interested in reviewing and learning the frontiers of probabilistic inference and generative models? Sign up this form forms.gle/jd6xGWGBgF8ukXLA6 for ICML 2023 workshop Structured Probabilistic Inference and Generative Modeling! #ICML2023
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A nice introduction for molecular dynamics (I found it very helpful for myself when working with Yanze)! This is also under the AI4Science101 initiative, more blogs are coming out soon!
ai4science101.github.io/
In our latest community blog, Yanze Wang and @YuanqiD provide an introductory overview of molecular dynamics simulations.
If you're interested in learning more, you can read the full blog here: m2d2.peek.link/355J
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Unfortunately, I cannot make NeurIPS this year, but I will be around Seattle starting later this week! So if anyone stops by Seattle before or after NeurIPS, ping me to catch up!
My excellent collaborators will also present three papers (includ. one spotlight) @NeurIPSConf!
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Excited to attend my first #ACSSpring2025 in San Diego next week! I’ll be sharing some of our latest work and can’t wait to meet everyone! If you’ll be there, let’s connect and chat about all things AI & Chemistry!
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After missing ML conferences for two years, I'm finally attending @NeurIPSConf next month! We are organizing a very nice @AI_for_Science party night, please sign up here if it interests you: partiful.com/e/IDy23TR5e14Dz…? Space limited, sign up ASAP!
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Super excited to kick off this seminar by @StefanoErmon on diffusion models and applications in science! This seminar series will be open to all, live-streamed and recorded! Zoom link: cornell.zoom.us/j/9433205327…
We are excited to announce the AI for Science seminar series! The seminar will feature both pioneers and Schmidt Futures AI for Science postdocs on advances and challenges at the frontier of AI for scientific discovery.
We hope you’ll join us!
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Great resource to beginners!
❓New to Geometric GNNs, GDL, PyTorch Geometric, etc.? Want to understand how theory/equations connect to real code?
Try this practical notebook before diving into this exciting area!
**Geometric GNNs 101**
github.com/chaitjo/geometric…
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Life update: I will stay in Seattle this summer, happy to chat about science if anyone around is interested! (I am also down to attend CVPR for the first time!)
I am also visiting Cali near the end of May (Bay area and LA), reach out if you like to catch up at some point!
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Come and join us if you are interested in a new perspective to build expressive and efficient equivariant GNNs! Very excited!
This Monday, we discuss a new 3D GNN framework with the authors Weitao Du, @YuanqiD, and @limei69990587: arxiv.org/abs/2304.04757
Come discuss with the authors what is new about this one more 3D GNN
Join us at 11am EDT / 5pm CEST on Zoom: m2d2.io/talks/logg/about/
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🔥Tremendous progress in applying AI to MOFs has emerged in just a few years. Agents can now propose and even help synthesize new materials.
📄Our new perspective preprint asks: where are we today, what challenges remain, and what promises lie ahead for AI-designed MOFs🌍⚡
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Yue has been doing wonderful work in building representations incorporating geometry, dynamics, physics, and more with many downstream applications. Congrats! Follow him and work with him if you are into this area!
Congratulations to Dr. Yue Song for accepting an Assistant Professor position in the College of AI at @Tsinghua_Uni!
It's been wonderful having you in @YueLabCaltech. Looking forward seeing more exciting research on structured representation learning as you start your own lab next year!
Follow him here:
kingjamessong.github.io/
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With @bmorphism and @Abel0828, we are about to organize a @LogConference local meetup around NYC area, please vote for the time period that you like!
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Sign up for the second annual Learning on Graph conference! Follow the most recent progress in the field and enjoy the big party of graph machine learning community! 🔥🔥🔥forms.gle/SpEzsAe9gPGAHwgH8 @LogConference
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Presenting Flexible Diffusion Model today 2pm at Exhibit Hall 1 Poster #425. Looking forward to meeting you all!
I will be traveling to #ICML2023 in Hawaii next week! I will present two papers, main conf track (Flexible Diffusion) and oral presentation @TAGinDS workshop (LEFTNet). Happy to chat about Geometric DL, Generative Models and AI for Science! DM me if you'd like to chat!
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Glad to have @mmbronstein to talk about GNNs in our next week seminar! Welcome to join us online!
We are excited to announce that this Friday, March 22nd Dr. Michael Bronstein will be joining us for a Physical Perspective on Graph Neural Networks. Hope you'll join us!
Zoom: cornell.zoom.us/j/9744775324…
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I knew @ZimingLiu11 more than three years ago when I just entered this field. He has been a great researcher, mentor and friend for me. He re-ignited my curiosity in physics and discovery. If you are looking for someone who works on AI + physics (science), reach out to him!
This fall, I’ll be on job market looking for postdoc and faculty positions in US! My research interests span in AI + physics (science). If there’re opportunities to present in your school, institute, group, seminar, workshop etc., I really appreciate it! 🥹kindxiaoming.github.io/
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Join the meeting group if you are interested! Previous machine learning potential work often considers predicting force/energy to drive the simulation, while we tackle another challenging problem to sample transition paths between two states (e.g. modeling protein folding)!
Tomorrow @HoldijkLars presents his paper "Path Integral Stochastic Optimal Control for Sampling Transition Paths" (arxiv.org/abs/2207.02149)
I think the SOC ideas might have even more applicability in this field!
Join on Zoom at 11am EDT / 5pm CET: m2d2.io/talks/logg/about/
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New Preprint! We believe the “optimal” forward process of diffusion models should be learned. Inspired by symplectic structure and Riemannian metric, we build the **first** learnable forward process of diffusion models with theoretical guarantees!
A Flexible Diffusion Model
deepai.org/publication/a-fle…
by Weitao Du et al. including @YuanqiD
#ComputerScience #Learning
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We use the advances in geometric deep learning and generative models to accelerate the “searching” of transition state in chemical reactions with high efficiency and accuracy! Great work with @chenru_duan @KulikGroup!
📢 @chenru_duan, @KulikGroup, @YuanqiD and colleagues introduce a diffusion model that generates chemical reactions in 3D with all desired symmetries preserved. nature.com/articles/s43588-0…
👉rdcu.be/dtInZ
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Education is the foundation of AI for Science. We are calling people who are interested in joining us and discussing the future this December at NeurIPS! If you have any questions or suggestions regarding the submission format for this track, please let us know!
We are opening a new track for Education @AI_for_Science workshop this year #NeurIPS2023. Education has been and will continue to be one of the largest gaps in AI for Science. We are calling for contributions of educational resources in flexible formats!
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Our workshop is back at #NeurIPS2025! Join our party at San Diego later this year! We will release a call for data proposal competition with opportunity to get fund soon!
AI for Science will be returning to @NeurIPSConf 2025! We aim to bring together scientists and AI researchers to discuss the reach and limits of AI for Scientific Discovery 🚀
📖 Workshop submission deadline: Aug 22
💡 Dataset proposal competition: more details coming soon
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Looking forward to sharing FEAT with @JiajunHe614! Come if you are interested!
Tomorrow the authors @YuanqiD and Jiajun He will present their paper "FEAT: Free energy Estimators with Adaptive Transport" arxiv.org/abs/2504.11516
Estimating free energy differences is a strong tool for comparing drug's binding affinities computationally
Join us on zoom at 12pm ET / 6pm CEST: portal.valencelabs.com/stark…
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A very nice and long review (w/ perspective) about AI for (Physical) Science! Education and community building are indispensable yet challenging parts in @AI_for_Science. This paper could serve as a great educational resource for people who are interested in this promising area!
Excited to share our latest survey paper: "Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems"! 🚀 ArXiv: arxiv.org/abs/2307.08423.
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We are partnering with @TmlrOrg! Present your Graph ML papers at @LogConference!
In collaboration with @TmlrOrg, we're excited to announce the new Journal Track for LoG 2024.
Super simple submission process for your awesome TMLR graph ML and geometric DL papers, due by 26 October 2024.
All details 👇
logconference.org/cfp/#tmlr-…
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@AI_for_Science workshop talks are freely accessible now!
You can now watch the recorded material from #NeurIPS2022 online without registration at:
slideslive.com/neurips-2022
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Incredible growing interest in @AI_for_Science this year at ICML! This already surpassed what we had last time at ICML!
We have received ~150 high quality submissions, we have ~200 reviewers and ~50 ACs waiting to bid and read your exiting work! Abstract due time AoE 23rd end of day which is less than 24 hours from now!
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We are organizing a local meetup for the Seattle area at UW Gates Center 371 on Dec 9th 1:30-5:00pm! The event is free for all to attend and we welcome contributed talks related to the intersection of geometry, graphs, networks and machine learning!
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Some personal updates: I will be at Boston from May to August this summer and from August to December I will be in Bay Area!
Ping me to chat & if there is any fun event in both places or we should organize some together!
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We are organizing the second learning meets geometry, graphs, and networks meetup (Nov 21-22) in Jersey City @LoGNYCMeet! Last time we had roughly 100 attendees and 15 talks!
Jointly organized with Ali Parviz, @yingheng_wang, @Abel0828 and @dereklim_lzh #NYC
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Will attend #NeurIPS2022 in-person next week! Can’t wait to meet new and old friends (many we haven’t met due to COVID)! If you would like to chat anything with me (research, community, etc.), feel free to message! I will also be around @AI_for_Science workshop on December 2nd.
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It was a wonderful chat with Jaanak brothers about my experience and perspective about AI for Science research. If you are interested in how I got into the field, found my direction and my thoughts about the present and future of the field, check out this podcast!
We greatly enjoyed speaking with @YuanqiD about research at the intersection of biology and computer science, the importance of multidisciplinary research, and the PhD journey. Please feel free to listen below! :)
open.spotify.com/episode/5v8…
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We designed an object-aware equivariant diffusion model tailored for transition state generation in chemical reaction! Check more details in the preprint if you are interested!
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Will be around Boston this weekend and early next week, please reach out if you like to chat!
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Just curious, anyone interested in a @LogConference local meetup around New York City area? Hit like and comment if you do!
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Some stats we tried to collect github.com/sherrylixuecheng/… credit to @SherryLixueC
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Unfortunately I’m not going to make @icmlconf 2024 this time, but we have a list of remarkable programs and hope you enjoy our workshop!
Our workshop @icmlconf 2024 happening next week features an exceptional list of speakers and panelists to discuss different aspects/goals of AI for Science: methodology, interpretability, discovery, and an increasingly discussed one, scaling!
Schedule: ai4sciencecommunity.github.i…
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Looking forward to seeing you all tomorrow! I am most proud of our ever-growing local meetup program this year!
LoG is happening tomorrow!
Highlights of the program:
🎤Exciting keynotes from @jure, @andreasloudaros, Stefanie Jegelka, @KyleCranmer, @ktschuett
🌟 12 orals
💻 Tutorials on scalability & recommendation
🤗 poster sessions & networking
Join now via logconference.org/
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A great memory visiting friends and chatting about everything related to AI, Physics, Mechanistic Interpretability, Science of Science, etc. and (more interestingly) the smiling pattern is similar to the "God fathers of Deep Learning"!
If I ever grew taller, these two photos will be more similar 😝 @ke_li_2021 @YuanqiD
Look up to role models @geoffreyhinton, Yoshua Bengio, @ylecun
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