↓ Skip to Main Content
Dral's Group AI-enhanced computational chemistry | LOOKING FOR POSTDOCS WITH DIFFERENT EXPERTISE (QM AND ML METHOD DEVELOPMENT, SOLID-STATE MEHOD DEVELOPMENT, APPLICATIONS AND MORE)

Main Navigation

  • Group
  • About Pavlo
    • Contact and Legal Notice
    • Privacy Statement
    • Cookie Policy
  • People
  • Publications
  • Research
  • Online Course
  • Conferences
  • News & Posts
    • News
    • Posts
Dral's Group AI-enhanced computational chemistry | LOOKING FOR POSTDOCS WITH DIFFERENT EXPERTISE (QM AND ML METHOD DEVELOPMENT, SOLID-STATE MEHOD DEVELOPMENT, APPLICATIONS AND MORE)

Off Canvas Menu

  • Group
  • About Pavlo
    • Contact and Legal Notice
    • Privacy Statement
    • Cookie Policy
  • People
  • Publications
  • Research
  • Online Course
  • Conferences
  • News & Posts
    • News
    • Posts

Method Development

Machine Learning in Chemistry, Method Development, News

MLatom 3.2.0 released

Pavlo Dral Mar 20, 2024 Tagged with MLatom, MLatom release

We are happy to announce that MLatom 3.2.0 is released on 19.03.2024. See the full release …

MLatom 3.2.0 released Read more →

Chem. Commun. Feature Article: “AI in computational chemistry through the lens of a decade-long journey”
Machine Learning in Chemistry, Method Development

Chem. Commun. Feature Article: “AI in computational chemistry through the lens of a decade-long journey”

Pavlo Dral Mar 14, 2024 Tagged with AI, Chem. Commun., ML, publications, review

My review ‘AI in computational chemistry through the lens of a decade-long journey’ was published open …

Chem. Commun. Feature Article: “AI in computational chemistry through the lens of a decade-long journey” Read more →

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra
Machine Learning in Chemistry, Method Development, News

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra

Pavlo Dral Feb 28, 2024 Tagged with AI, excited states, ML, MLatom, publications, spectra

AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We …

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra Read more →

MLatom 3 for AI-enhanced computational chemistry: JCTC paper and online tutorial
Machine Learning in Chemistry, Method Development, News

MLatom 3 for AI-enhanced computational chemistry: JCTC paper and online tutorial

Pavlo Dral Feb 7, 2024 Tagged with AI, JCTC, ML, MLatom, MLatom PyAPI, tutorial

The capabilities of MLatom 3 are described in the paper published in the J. Chem. Theory …

MLatom 3 for AI-enhanced computational chemistry: JCTC paper and online tutorial Read more →

Energy-conserving molecular dynamics is not energy conserving!
Electronic Structure Calculations, Machine Learning in Chemistry, Method Development, News

Energy-conserving molecular dynamics is not energy conserving!

Pavlo Dral Aug 25, 2023 Tagged with dynamics, ML, PCCp, publication

Molecular dynamics simulations are widely used to study molecules and materials and lots of effort is …

Energy-conserving molecular dynamics is not energy conserving! Read more →

Beyond 3D-Machine Learning Interatomic Potentials: Meet 4D-Spacetime Atomistic Artificial Intelligence Models
Machine Learning in Chemistry, Method Development, News

Beyond 3D-Machine Learning Interatomic Potentials: Meet 4D-Spacetime Atomistic Artificial Intelligence Models

Pavlo Dral Aug 24, 2023 Tagged with 4D, dynamics, ML, MLatom, publication

We have introduced a concept of 4D-spacetime atomistic AI models that learn how the molecule changes …

Beyond 3D-Machine Learning Interatomic Potentials: Meet 4D-Spacetime Atomistic Artificial Intelligence Models Read more →

Machine Learning in Chemistry, Method Development, News, Posts

QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets

Arif Ullah Aug 10, 2023 Tagged with dynamics, Frtontiers in Physics, ML, QD3SET-1, quantum dissipative dynamics

In a recent article published in Frontiers in Physics, we introduce QD3SET-1, a database consisting of …

QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets Read more →

Explaining and Predicting Two-Photon Absorption with Machine Learning
Machine Learning in Chemistry, Method Development, News

Explaining and Predicting Two-Photon Absorption with Machine Learning

Fuchun Ge Feb 7, 2023 Tagged with ML-TPA, MLatom, publications

Materials can simultaneously absorb not just one but two photons and molecules with strong two-photon absorption …

Explaining and Predicting Two-Photon Absorption with Machine Learning Read more →

A comparative study of different machine learning methods for dissipative quantum dynamics
Machine Learning in Chemistry, Method Development, News

A comparative study of different machine learning methods for dissipative quantum dynamics

Yaohuang Huang Oct 19, 2022 Tagged with ML, MLatom, MLST, QD

Recently, Machine Learning (ML) is increasingly used for fast and accurate propagation of quantum dissipative dynamics …

A comparative study of different machine learning methods for dissipative quantum dynamics Read more →

The Newton-X platform for surface hopping and nuclear ensembles
Electronic Structure Calculations, Machine Learning in Chemistry, Method Development, News

The Newton-X platform for surface hopping and nuclear ensembles

Pavlo Dral Oct 5, 2022 Tagged with excited states, JCTC, ML, MLatom, nonadiabatic dynamics

Mario Barbatti, his group and collaborators published an update on Newton-X – a popular open-source platform…

Read more →

Posts pagination

Previous 1 2 3 4 Next
Copyright © 2026 Dr. Pavlo O. Dral | Powered by Responsive Theme
Copyright © 2026 Dr. Pavlo O. Dral | Powered by Responsive Theme