170 citations · 395 across the 13 of their papers we have counts for
13 papers
Latent Reasoning with Normalizing Flows
Guancheng Tu, Xiangjun Fu, Suhao Yu +5
Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces t…
MatterGen: a generative model for inorganic materials design
Claudio Zeni, Robert Pinsler, Daniel Zügner +18
The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative mo…
Learning Interatomic Potentials at Multiple Scales
Xiang Fu, Albert Musaelian, Anders Johansson +2
The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a…
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Xiang Fu, Tian Xie, Andrew S. Rosen +2
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modula…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Virtual Node Graph Neural Network for Full Phonon Prediction
Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird +10
The structure-property relationship plays a central role in materials science. Understanding the structure-property relationship in solid-state materials is crucial for structure d…
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
Xiang Fu, Zhenghao Wu, Wujie Wang +4
Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace…
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks
Xiang Fu, Tian Xie, Nathan J. Rebello +2
Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive. Learning-based force fields have made significant progress in accelera…
Learning to Jump from Pixels
Gabriel B. Margolis, Tao Chen, Kartik Paigwar +4
Today's robotic quadruped systems can robustly walk over a diverse range of rough but continuous terrains, where the terrain elevation varies gradually. Locomotion on discontinuous…
Fragment-based Sequential Translation for Molecular Optimization
Benson Chen, Xiang Fu, Regina Barzilay +1
Searching for novel molecular compounds with desired properties is an important problem in drug discovery. Many existing frameworks generate molecules one atom at a time. We instea…
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea +2
Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in…
Learning Task Informed Abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal +1
Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this…
Modeling and Analysis of Tagging Networks in Stack Exchange Communities
Xiang Fu, Shangdi Yu, Austin R. Benson
Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behavior on the platforms in a variety of contexts, the…