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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)

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News

Job Offers, News

Post-doctoral Position Opening in Machine Learning in Quantum Chemistry

Pavlo Dral Oct 22, 2019 Tagged with job offer, ML, post-doc position, QC

A post-doctoral position is open in the group of Dr. Pavlo Dral in College of Chemistry …

Post-doctoral Position Opening in Machine Learning in Quantum Chemistry Read more →

News

Joining Xiamen University

Pavlo Dral Sep 30, 2019

I am happy to announce that I am joining Xiamen University as an Associate Professor.

News, Posts

Walter Thiel

Pavlo Dral Aug 27, 2019 Tagged with Walter Thiel

Prof. Walter Thiel passed away unexpectedly on August 23, 2019.

Machine Learning in Chemistry, News

Paper on MLatom

Pavlo Dral Jun 20, 2019 Tagged with JCC, ML, MLatom

The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations …

Paper on MLatom Read more →

Method Development, Semiempirical Methods

Next Step: TB-SEMO Methods?

Pavlo Dral Apr 25, 2019 Tagged with JMM, JMolModel, NDDO, OM2, SQC

Johannes Margraf and I have published our perspective on what semiempirical molecular orbital (SEMO) methods are …

Next Step: TB-SEMO Methods? Read more →

Machine Learning in Chemistry, Method Development, News

MLatom 1.0

Pavlo Dral Apr 19, 2019 Tagged with ML, MLatom

MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.

Method Development, News, Semiempirical Methods

ODMx: New Consistent Semiempirical Methods

Pavlo Dral Feb 28, 2019 Tagged with JCTC, ODM2, ODM3, ODMx, OM2, OM3, OMx, SQC

We have introduced two new NDDO-based semiempirical quantum-chemical methods ODM2 and ODM3, which are more consistent …

ODMx: New Consistent Semiempirical Methods Read more →

Method Development, News, Semiempirical Methods

How Valid Is the NDDO Approximation?

Pavlo Dral Dec 14, 2018 Tagged with Big Data, JCC, NDDO, OMx, SQC

We comprehensively analyzed the validity of the NDDO (neglect of diatomic differential overlap) approximation, which forms …

How Valid Is the NDDO Approximation? Read more →

Machine Learning in Chemistry

Nonadiabatic Dynamics with Deep Learning

Pavlo Dral Nov 15, 2018 Tagged with excited states, J. Phys. Chem. Lett., ML, nonadiabatic dynamics

We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics …

Nonadiabatic Dynamics with Deep Learning Read more →

Machine Learning in Chemistry, News

Machine Learning Accelerates Excited-State Dynamics

Pavlo Dral Sep 14, 2018 Tagged with excited states, J. Phys. Chem. Lett., ML, nonadiabatic dynamics

Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.

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