533 citations · 567 across the 11 of their papers we have counts for
13 papers
El Agente Potente: High-Throughput Agentic Atomistic Simulations
Tsz Wai Ko, Jiaru Bai, Thomas Swanick +6
Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction…
El Agente Sólido: A New Age(nt) for Solid State Simulations
Sai Govind Hari Kumar, Yunheng Zou, Andrew Wang +8
Quantum chemistry calculations are a key component of the materials discovery process. The results from first-principles explorations enable the prediction of material properties p…
El Agente Gráfico: A Semantic Execution Runtime for Scientific Agents
Jiaru Bai, Abdulrahman Aldossary, Thomas Swanick +12
Large language models (LLMs) can plan scientific workflows and generate code, but these capabilities do not specify how scientific state is validated, transferred and recorded acro…
El Agente Quntur: A research collaborator agent for quantum chemistry
Juan B. Pérez-Sánchez, Yunheng Zou, Jorge A. Campos-Gonzalez-Angulo +12
Quantum chemistry is a foundational enabling tool for the fields of chemistry, materials science, computational biology and others. Despite of its power, the practical application…
A Linear-Scaling, Charge-Aware Foundation Potential for Atomistic Simulations
Tsz Wai Ko, Runze Liu, Adesh Rohan Mishra +3
Electrostatics govern charge transfer and reactivity in materials. However, most foundation potentials (FPs) either neglect explicit electrostatic interactions or come at prohibiti…
Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
Emir Kocer, Andreas Singraber, Jonas A. Finkler +4
Machine learning potentials (MLP) allow to perform large-scale molecular dynamics simulations with about the same accuracy as electronic structure calculations provided that the se…
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…
Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
Tsz Wai Ko, Shyue Ping Ong
Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio potential energy surfaces…
Superionic surface Li-ion transport in carbonaceous materials
Jianbin Zhou, Shen Wang, Chaoshan Wu +17
Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. In this study, we have discovered an ultra-fast Li-ion trans…
Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling
Ji Qi, Tsz Wai Ko, Brandon C. Wood +2
Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computatio…
Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker +1
In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemist…
Neural Network Potentials: A Concise Overview of Methods
Emir Kocer, Tsz Wai Ko, Jörg Behler
In the past two decades, machine learning potentials (MLP) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of sy…
A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker +1
Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the establish…