2.9k citations · 3.8k across the 15 of their papers we have counts for
15 papers
MatterSim-MT: A multi-task foundation model for in silico materials characterization
Han Yang, Xixian Liu, Chenxi Hu +25
Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progres…
MHub: Unlocking the Potential of Machine Learning for Materials Discovery
Yuanqi Du, Yingheng Wang, Yining Huang +6
We introduce MHub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especial…
Inverse Design of Next-generation Superconductors Using Data-driven Deep Generative Models
Daniel Wines, Tian Xie, Kamal Choudhary
Finding new superconductors with a high critical temperature () has been a challenging task due to computational and experimental costs. We present a diffusion model inspired…
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…
A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations
Tian Xie, Ha-Kyung Kwon, Daniel Schweigert +9
Open material databases storing hundreds of thousands of material structures and their corresponding properties have become the cornerstone of modern computational materials scienc…
Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity
Sheng Gong, Tian Xie, Yang Shao-Horn +2
Historically, materials informatics has relied on human-designed descriptors of materials structures. In recent years, graph neural networks (GNNs) have been proposed for learning…
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…
Calibrating DFT formation enthalpy calculations by multi-fidelity machine learning
Sheng Gong, Shuo Wang, Tian Xie +3
Machine learning materials properties measured by experiments is valuable yet difficult due to the limited amount of experimental data. In this work, we use a multi-fidelity random…
Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
Tian Xie, Arthur France-Lanord, Yanming Wang +8
Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant c…
Charting Lattice Thermal Conductivity of Inorganic Crystals
Taishan Zhu, Sheng Gong, Tian Xie +2
Thermal conductivity is a fundamental material property but challenging to predict, with less than 5% out of about synthesized inorganic materials being documented. In this…
Predicting charge density distribution of materials using a local-environment-based graph convolutional network
Sheng Gong, Tian Xie, Taishan Zhu +4
Electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practic…
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
Tian Xie, Arthur France-Lanord, Yanming Wang +2
Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and envir…
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
Tian Xie, Jeffrey C. Grossman
The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structura…
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
Zeeshan Ahmad, Tian Xie, Chinmay Maheshwari +2
Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circui…
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Tian Xie, Jeffrey C. Grossman
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coo…