open force field

An open and collaborative approach to better force fields

news

Welcome, Pavan!

Posted on 18 Sep 2020 by Karmen Čondić-Jurkić

We are excited to welcome Pavan Kumar Behara to our team!


Open Force Field Newsletter August 2020

Posted on 28 Aug 2020 by Karmen Čondić-Jurkić

This is the first quarterly Open Force Field Newsletter released as a double issue for Q2 and Q3 2020.


Benchmark: OpenFF performance on small molecule energies and geometries

Posted on 25 Jul 2020 by David Hahn

Force Field benchmark on small molecule energies and geometries.


Lee-Ping Wang stepping down from a leadership role

Posted on 22 Jul 2020 by Karmen Condic-Jurkic

Lee-Ping Wang has decided to step down from his role as an OpenFF primary investigator and to continue his involvement as a co-investigator.


Open Force Field Consortium Virtual Meeting

Posted on 23 Apr 2020 by Karmen Condic-Jurkic

The Third Open Force Field Consortium Workshop in Zoomverse on May 4-5, 2020.


Open Force Field Initiative receives NIH funding

Posted on 6 Apr 2020 by Michael Shirts

Open Force Field Initiative receives NIH funding


The Third Open Force Field Consortium Workshop

Posted on 25 Feb 2020 by Karmen Condic-Jurkic

The Third Open Force Field Consortium Workshop in Boston on May 4-5, 2020.


Webinar by Andreas Krämer: Automated Optimization Approaches for the CHARMM Lipid Force Field (Dec 17, 2019)

Posted on 2 Dec 2019 by Karmen Condic-Jurkic

Andreas Krämer will talk about automated optimization of the CHARMM36 lipid force field on Dec 17 at 2 pm (ET).


The Open Force Field 1.0 small molecule force field, our first optimized force field (codename "Parsley")

Posted on 10 Oct 2019 by David Mobley, Yudong Qiu, Simon Boothroyd, Lee-Ping Wang, and John Chodera

At the end of our first year, the Open Force Field Consortium releases its first optimized force field: the Open Force Field 1.0 (codename “Parsley”) small molecule force field


Webinar by Yuanqing Wang: Graph Nets for partial charge prediction (Oct 14, 2019)

Posted on 4 Oct 2019 by Karmen Condic-Jurkic

Yuanqing Wang (MSKCC) will talk about his ongoing work on applying machine learning techniques for fast prediction of atomic charges on Oct 14 at 1 pm (ET).


1 of 3 Next Page