203 citations · 305 across the 14 of their papers we have counts for
15 papers
Universal Thermodynamic Interatomic Potentials for Crystalline Materials
Juno Nam, Bowen Deng, Xiaochen Du +3
Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensem…
Harnessing AtomisticSkills for Agentic Atomistic Research
Bowen Deng, Bohan Li, Matthew Cox +20
Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabi…
Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
Kevin Han, Haolin Cong, Bowen Deng +1
Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. Ho…
Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors
Luca Binci, KyuJung Jun, Bowen Deng +1
Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity (1 mS cm), excellent chemical stability, mechanical softness, an…
Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculations
Zhuohan Li, KyuJung Jun, Bowen Deng +1
Solid-state batteries require lithium-ion conductors that combine high ionic conductivity with stability under harsh electrochemical and chemical conditions. Here, we investigate t…
Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials
Peichen Zhong, Bowen Deng, Shashwat Anand +2
Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure (-phase) during electrochemi…
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Kevin Han, Bowen Deng, Amir Barati Farimani +1
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid…
Cross-functional transferability in universal machine learning interatomic potentials
Xu Huang, Bowen Deng, Peichen Zhong +3
The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy su…
Crystal structure prediction with host-guided inpainting generation and foundation potentials
Peichen Zhong, Xinzhe Dai, Bowen Deng +2
Unconditional crystal structure generation with diffusion models faces challenges in identifying symmetric crystals as the unit cell size increases. We present the Crystal Host-Gui…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
A Foundational Potential Energy Surface Dataset for Materials
Aaron D. Kaplan, Runze Liu, Ji Qi +6
Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs) offer…
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Tsz Wai Ko, Bowen Deng, Marcel Nassar +7
Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the…
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
Bowen Deng, Yunyeong Choi, Peichen Zhong +6
Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have seen the emergence of universal MLIPs (uMLIPs) that…
Effect of cation-disorder on lithium transport in halide superionic conductors
Peichen Zhong, Sunny Gupta, Bowen Deng +2
LiZrCl (LZC) is a promising solid-state electrolyte due to its affordability, moisture stability, and high ionic conductivity. We computationally investigate the role of ca…
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner +7
The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation. We present Matbench Discovery,…