9 citations · 56 across the 16 of their papers we have counts for
17 papers
NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents
Lijie Ding, Changwoo Do
Designing a scientific instrument tests whether language-model agents can do physics rather than recall it, provided the grading cannot be argued with. We introduce NeutronGym, to…
ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing
Lijie Ding, Changwoo Do
We introduce ColPackAgent, an agent framework that autonomously runs Monte Carlo simulations of colloidal packing through a Model Context Protocol (MCP) tool server and an agent sk…
Deep Learning the Small-Angle Scattering of Polydisperse Hard Rods
Lijie Ding, Changwoo Do
We present a deep learning framework for modeling and analyzing the small-angle scattering data of polydisperse hard-rod systems, a widely used models for anisotropic colloidal par…
LLMs Can Assist with Proposal Selection at Large User Facilities
Lijie Ding, Janell Thomson, Jon Taylor +1
We explore how large language models (LLMs) can enhance the proposal selection process at large user facilities, offering a scalable, consistent, and cost-effective alternative to…
ToPolyAgent: AI Agents for Coarse-Grained Topological Polymer Simulations
Lijie Ding, Jan-Michael Carrillo, Changwoo Do
We introduce ToPolyAgent, a multi-agent AI framework for performing coarse-grained molecular dynamics (MD) simulations of topological polymers through natural language instructions…
SasAgent: Multi-Agent AI System for Small-Angle Scattering Data Analysis
Lijie Ding, Changwoo Do
We introduce SasAgent, a multi-agent AI system powered by large language models (LLMs) that automates small-angle scattering (SAS) data analysis by leveraging tools from the SasVie…
Deciphering the Small-Angle Scattering of Polydisperse Hard Spheres using Deep Learning
Lijie Ding, Changwoo Do
We introduce a deep learning approach for analyzing the scattering function of the polydisperse hard spheres system. We use a variational autoencoder-based neural network to learn…
Universal Convergence Metric for Time-Resolved Neutron Scattering
Chi-Huan Tung, Lijie Ding, Yuya Shinohara +4
This work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved Small-Angle Neutron Scattering (SANS) using early-time d…
Deciphering the Scattering of Mechanically Driven Polymers using Deep Learning
Lijie Ding, Chi-Huan Tung, Bobby G. Sumpter +2
We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compres…
Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement
Chi-Huan Tung, Sidney Yip, Guan-Rong Huang +6
Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significan…
Machine Learning Inversion from Small-Angle Scattering for Charged Polymers
Lijie Ding, Chi-Huan Tung, Jan-Michael Y. Carrillo +2
We develop Monte Carlo simulations for uniformly charged polymers and machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, whic…
Elongated particles in flow: Commentary on small angle scattering investigations
Guan-Rong Huang, Lionel Porcar, Ryan P. Murphy +7
This work thoroughly examines several analytical tools, each possessing a different level of mathematical intricacy, for the purpose of characterizing the orientation distribution…
Scattering-Based Structural Inversion of Soft Materials via Kolmogorov-Arnold Networks
Chi-Huan Tung, Lijie Ding, Ming-Ching Chang +8
Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in struct…
Machine Learning-Informed Scattering Correlation Analysis of Sheared Colloids
Lijie Ding, Yihao Chen, Changwoo Do
We carry out theoretical analysis, Monte Carlo simulations and Machine Learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particl…
Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering
Lijie Ding, Chi-Huan Tung, Zhiqiang Cao +5
Ladder polymers, known for their rigid, ladder-like structures, exhibit exceptional thermal stability and mechanical strength, positioning them as candidates for advanced applicati…
Machine Learning Inversion from Scattering for Mechanically Driven Polymers
Lijie Ding, Chi-Huan Tung, Bobby G. Sumpter +2
We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include…
Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers
Lijie Ding, Chi-Huan Tung, Bobby G. Sumpter +2
We develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces using Markov Chain Monte Carlo. Our approach models the polymer as a chain…