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Accelerating and improving computational chemistry with artificial intelligence/machine learning

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Dral's Group

Accelerating and improving computational chemistry with artificial intelligence / machine learning

State Key Laboratory of Physical Chemistry of Solid Surfaces (PCOSS)
Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry
Department of Chemistry
College of Chemistry and Chemical Engineering
Xiamen University

News & Posts

  • MLatom joins Xiamen Atomistic Computing Suite
    by Pavlo Dral
    May 10, 2022
  • Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods
    by Wudi Yang
    April 14, 2022
  • Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics
    by Arif Ullah
    April 11, 2022
  • AIQM1 paper is top 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021
    by Pavlo Dral
    March 31, 2022
  • Speeding up quantum dissipative dynamics of open systems with kernel methods
    by Arif Ullah
    March 25, 2022

Artificial Intelligence / Machine Learning

MLatom 1.0Developing efficient machine learning-based methods for atomistic simulations using our package MLatom.
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Featured News

EALAIQM1 paper is among top 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021
Read more ›
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