136 citations · 554 across the 13 of their papers we have counts for
15 papers
Atomistic prediction of magnetic behaviour across chemistry, structure and temperature
Ruosong Shi, Chengbing Chen, Bicheng Liu +15
Controlling magnetic phases and properties through composition and external conditions is central to the development of magnetic materials. Atomistic foundation models offer a rout…
Learning Electron Density with Virtual Atoms for Transferable Atomistic Representations
Yuzhi Liu, Xinzijian Liu, Chengqian Zhang +3
Electron density is a fundamental quantity of electronic structure, yet most machine learning density models treat it solely as a reconstruction target and are developed separately…
Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day
Yi Wang, Zhenting Huang, Zhaohan Ding +6
Open-source scientific software is abundant, yet most tools remain difficult to compile, configure, and reuse, sustaining a small-workshop mode of scientific computing. This deploy…
Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale
Linfeng Zhang, Siheng Chen, Yuzhu Cai +46
AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning with tool use and verification, pointing to a shift from isolated AI-assi…
Inverse Knowledge Search over Verifiable Reasoning: Synthesizing a Scientific Encyclopedia from a Long Chains-of-Thought Knowledge Base
Yu Li, Yuan Huang, Tao Wang +19
Most scientific materials compress reasoning, presenting conclusions while omitting the derivational chains that justify them. This compression hinders verification by lacking expl…
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng +44
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…
The OpenLAM Challenges
Anyang Peng, Xinzijian Liu, Ming-Yu Guo +2
Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep…
Dflow, a Python framework for constructing cloud-native AI-for-Science workflows
Xinzijian Liu, Yanbo Han, Zhuoyuan Li +15
In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable…
An Extendable Cloud-Native Alloy Property Explorer
Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang +8
The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials ide…
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…
DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation
Duo Zhang, Hangrui Bi, Fu-Zhi Dai +3
Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality el…
Critical role of quantum dynamical effects in the Raman spectroscopy of liquid water
Xinzijian Liu, Jian Liu
Understanding the Raman spectroscopy at the atomistic level is important for the elucidation of dynamical processes in liquid water. Because the polarizability (or its time derivat…
Path Integral Molecular Dynamics for Exact Quantum Statistics of Multi-Electronic-State Systems
Xinzijian Liu, Jian Liu
An exact approach to compute physical properties for general multi-electronic-state (MES) systems in thermal equilibrium is presented. The approach is extended from our recent prog…
A unified thermostat scheme for efficient configurational sampling for classical/quantum canonical ensembles via molecular dynamics
Zhijun Zhang, Xinzijian Liu, Zifei Chen +3
We show a unified second-order scheme for constructing simple, robust and accurate algorithms for typical thermostats for configurational sampling for the canonical ensemble. When…
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat
Jian Liu, Dezhang Li, Xinzijian Liu
We introduce a novel simple algorithm for thermostatting path integral molecular dynamics (PIMD) with the Langevin equation. The staging transformation of path integral beads is em…