92 citations · 101 across the 3 of their papers we have counts for
4 papers
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
Dongjin Kim, Xiaoyu Wang, Peichen Zhong +3
Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recent…
System of Agentic AI for the Discovery of Metal-Organic Frameworks
Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan +12
Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical…
Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems
Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly +9
We present the extension of the Tinker-HP package (Lagardère et al., Chem. Sci., 2018,9, 956-972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamic…