@KeqiangY profile picture
Kelvin (Keqiang) Yan @KeqiangY
Joined September 2020
  • Tweets 319
  • Following 1,220
  • Followers 2,250
32 Photos and videos
Excited to share that I have joined ByteDance Seed AI for Science at Seattle, pushing the boundaries of AI-driven scientific discovery to accelerate real-world breakthroughs. Onward and upward! 😊
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Kelvin (Keqiang) Yan retweeted
JHU Computer Science@JHUCompSci
Mar 24
Double seminar next Thursday with @Princeton’s @KeqiangY and @UW’s @breda_joe! 🤩 Find more details here: cs.jhu.edu/events/
Computer Science Seminar Series. AI for Materials Discovery: Graphs, Language Models, and Agents. April 2, 2026, 228 Malone Hall. Refreshments available 10:30 a.m. Seminar begins 10:45 a.m. Keqiang Yan, Princeton University.

ALT Computer Science Seminar Series. AI for Materials Discovery: Graphs, Language Models, and Agents. April 2, 2026, 228 Malone Hall. Refreshments available 10:30 a.m. Seminar begins 10:45 a.m. Keqiang Yan, Princeton University.

Computer Science Seminar Series: From Personal to Public Health with Society-Scale Computing Platforms. April 2, 2026, 228 Malone Hall. Refreshments available 12:15 p.m. Seminar begins 12:30 p.m. Joe Breda, University of Washington.

ALT Computer Science Seminar Series: From Personal to Public Health with Society-Scale Computing Platforms. April 2, 2026, 228 Malone Hall. Refreshments available 12:15 p.m. Seminar begins 12:30 p.m. Joe Breda, University of Washington.
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I will be at #NeurIPS2025 next week from Wednesday, and I look forward to meeting with old and new friends! Just let me know if you want to talk about AI for Science, AI for Materials Discovery, LLMs for Science, and Agentic systems~😆
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Also open to fun activities if any!
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Received the hard copy of our book on AI for Science from my dear PhD supervisor @ShuiwangJi ✨ Check it out on Foundations and Trends in Machine Learning if interested!
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I am pleased to share that I will be joining CS @Princeton University as a Postdoctoral researcher. Look forward to working on foundational problems at the intersection of AI, LLMs, and science.
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Kelvin (Keqiang) Yan retweeted
Shuiwang Ji@ShuiwangJi
23 Jul 2025
Our 500 page AI4Science paper is finally published: Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Foundations and Trends® in Machine Learning, Vol. 18, No. 4, 385–912, 2025 nowpublishers.com/article/De…
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Kelvin (Keqiang) Yan retweeted
Hongliang Xin@XinGroup_VT
22 Jun 2025
All AIMEDx webinar recordings are available here: youtube.com/playlist?list=PL… Thanks to all our speakers and attendees for an incredible season of ideas and inspiration. We are off for a break, wishing everyone a restful summer! #AI4Science #AIMEDx

AIMEDx Webinar Series

Bridging disciplines to ignite learning at the frontier of AI and scientific research.

youtube.com
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A milestone to be celebrated 🎉 2^10=1,024 citations ✨
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Sincerely thank all my collaborators, and especially my Ph.D. advisor Dr. @ShuiwangJi to make this happen.
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Excited to share our latest preprint on LLM Agents, beyond tool use, for Materials Discovery. 🎉 ✨Unleash the discovery power of LLM agents by human intuitions ✨Achieve SOTA results without training ✨Towards greater autonomy with human in the loop
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Paper is released at arxiv.org/abs/2506.05616

Toward Greater Autonomy in Materials Discovery Agents: Unifying...

We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows,...

arxiv.org
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Kelvin (Keqiang) Yan retweeted
Shuiwang Ji@ShuiwangJi
9 Jun 2025
New paper alert: Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists. arxiv.org/abs/2506.05616
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Officially Dr. Yan 🥳🎉
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Excited and proud mentor moment 💫! A new materials foundation model with two of my undergraduate mentees Montgomery Bohde and Andrii Kryvenko as core authors. For Materials Foundation Models, Invariance V.S. Equivariance? Invariance Equivariance!
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(5/5) Our preprint and code are available now, including pretrained models available for use as an Atomic Simulation Environment (ASE) calculator. Paper: arxiv.org/abs/2503.05771 Code: github.com/divelab/AIRS/tree…
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(4/5) HIENet achieves top performance on ML benchmarks and we further demonstrate its utility on a variety of downstream materials science tasks such as calculating phonon band structures, elasticity tensors, bulk moduli, alloy phase diagrams, and molecular dynamics simulations.
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(3/5) HIENet uses gradient-based force and stress calculations to enforce important physical constraints such as equivariance and force field conservation. We find that satisfying these additional physical constraints is critical for downstream applications.
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(2/5) HIENet combines E(3)-invariant and O(3)-equivariant message passing layers. We find that adding invariant message passing layers retains the impressive performance scaling of equivariant models without the massive computational overhead of extra tensor product operations.
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(1/5) Our new materials foundation model, HIENet, combines invariant and equivariant message passing layers to achieve SOTA performance and efficiency on materials benchmarks and downstream tasks. arxiv.org/abs/2503.05771
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