ComENet: Towards complete and efficient message passing for 3D molecular graphs
Many real-world data can be modeled as 3D graphs, but learning representations that
incorporates 3D information completely and efficiently is challenging. Existing methods …
incorporates 3D information completely and efficiently is challenging. Existing methods …
Artificial intelligence for science in quantum, atomistic, and continuum systems
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, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
A latent diffusion model for protein structure generation
Proteins are complex biomolecules that perform a variety of crucial functions within living
organisms. Designing and generating novel proteins can pave the way for many future …
organisms. Designing and generating novel proteins can pave the way for many future …
Complete and efficient graph transformers for crystal material property prediction
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats
along a regular lattice throughout 3D space. The periodic and infinite nature of crystals …
along a regular lattice throughout 3D space. The periodic and infinite nature of crystals …
Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction
Physical simulations of fluids are crucial for understanding fluid dynamics across many
applications, such as weather prediction and engineering design. While high-resolution …
applications, such as weather prediction and engineering design. While high-resolution …
Paths towards time evolution with larger neural-network quantum states
In recent years, the neural-network quantum states method has been investigated to study
the ground state and the time evolution of many-body quantum systems. Here we expand on …
the ground state and the time evolution of many-body quantum systems. Here we expand on …
A Score-Based Model for Learning Neural Wavefunctions
Quantum Monte Carlo coupled with neural network wavefunctions has shown success in
computing ground states of quantum many-body systems. Existing optimization approaches …
computing ground states of quantum many-body systems. Existing optimization approaches …
Variational methods for solving high dimensional quantum systems
D Li - arXiv preprint arXiv:2404.11490, 2024 - arxiv.org
Variational methods are highly valuable computational tools for solving high-dimensional
quantum systems. In this paper, we explore the effectiveness of three variational methods …
quantum systems. In this paper, we explore the effectiveness of three variational methods …
Applications of Machine Learning Techniques to the Quantum Many-Body Problem
JR Moreno - 2023 - search.proquest.com
In recent years, the remarkable progress in machine learning has revolutionized various
fields, including natural sciences, and in particular the physical sciences. One area where …
fields, including natural sciences, and in particular the physical sciences. One area where …