From owner-chemistry@ccl.net Tue Jun 20 12:09:00 2023 From: "Fedor Goumans goumans!^!scm.com" To: CCL Subject: CCL: Introducing AMS2023: new DFT features, machine learning, reaction discovery Message-Id: <-54941-230620083049-30495-ml7wDo7lnGCCvO5H0WS/1A()server.ccl.net> X-Original-From: Fedor Goumans Content-Language: en-US Content-Type: multipart/alternative; boundary="------------p3W7y2AP8RI7Q0dFIy7v0HPV" Date: Tue, 20 Jun 2023 14:30:34 +0200 MIME-Version: 1.0 Sent to CCL by: Fedor Goumans [goumans|*|scm.com] This is a multi-part message in MIME format. --------------p3W7y2AP8RI7Q0dFIy7v0HPV Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 8bit Dear CCL’ers, The SCM team is thankful to our collaborators with whom we continue to improve our software. We encourage you to try out the Amsterdam Modeling Suite 2023 release: www.scm.com/2023 Enhanced DFT capabilities - overcome typical DFT pitfalls for excited states with (spin-orbit coupling) qsGW+BSE - get more accurate energies with the sigma-functional, the efficient and robust composite r2SCAN-3c(STO) method, and TASKCC in combination with the TASKxc functional - perform easy and difficult tasks on the potential energy surfaces with the integrated of Quantum ESPRESSO 7.1 to our AMS driver - run many other external codes through the new ASE interface Improved discovery tools: - easily explore reaction pathways with the ACE-Reaction graphical interface - explore many potential reactions with the Molecular Dynamics nanoreactor - create workflows from reaction exploration to kinetic Monte Carlo, generating a machine learned surrogate model for CatalyticFOAM reactor-scale modeling Furthermore, AMS2023 also provides improved training and parametrization methods in ParAMS, featuring multiple algorithms and stopping and restarting criteria to find the best DFTB and ReaxFF parameters. Chen and Ong’s universal graph neural network machine learning potential M3GNet-UP-2022 can be used with AMS to optimize almost any material, calculate stress tensors, or run non-equilibrium molecular dynamics (NEMD) for tribology and viscosity calculations. You will also enjoy many other usability improvements in our python scripting and graphical user interfaces. We look forward to your feedback and suggestions when you have tested AMS2023: www.scm.com/trial With kind regards, on behalf of the SCM team, Fedor Goumans -- Dr. T. P. M. (Fedor) Goumans Chief Customer Officer Software for Chemistry & Materials BV De Boelelaan 1083 1081 HV Amsterdam, The Netherlands https://www.scm.com https://twitter.com/SCM_Amsterdam https://www.linkedin.com/company/software-for-chemistry-&-materials --------------p3W7y2AP8RI7Q0dFIy7v0HPV Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: 8bit

Dear CCL’ers,

The SCM team is thankful to our collaborators with whom we continue to improve our software. We encourage you to try out the Amsterdam Modeling Suite 2023 release: www.scm.com/2023

Enhanced DFT capabilities
- overcome typical DFT pitfalls for excited states with (spin-orbit coupling) qsGW+BSE
- get more accurate energies with the sigma-functional, the efficient and robust composite r2SCAN-3c(STO) method, and TASKCC in combination with the TASKxc functional
- perform easy and difficult tasks on the potential energy surfaces with the integrated of Quantum ESPRESSO 7.1 to our AMS driver
- run many other external codes through the new ASE interface

Improved discovery tools:
- easily explore reaction pathways with the ACE-Reaction graphical interface
- explore many potential reactions with the Molecular Dynamics nanoreactor
- create workflows from reaction exploration to kinetic Monte Carlo, generating a machine learned surrogate model for CatalyticFOAM reactor-scale modeling

Furthermore, AMS2023 also provides improved training and parametrization methods in ParAMS, featuring multiple algorithms and stopping and restarting criteria to find the best DFTB and ReaxFF parameters.

Chen and Ong’s universal graph neural network machine learning potential M3GNet-UP-2022 can be used with AMS to optimize almost any material, calculate stress tensors, or run non-equilibrium molecular dynamics (NEMD) for tribology and viscosity calculations.

You will also enjoy many other usability improvements in our python scripting and graphical user interfaces.
We look forward to your feedback and suggestions when you have tested AMS2023: www.scm.com/trial

With kind regards, on behalf of the SCM team,

Fedor Goumans

-- 
Dr. T. P. M. (Fedor) Goumans
Chief Customer Officer
Software for Chemistry & Materials BV
De Boelelaan 1083
1081 HV Amsterdam, The Netherlands

https://www.scm.com
https://twitter.com/SCM_Amsterdam
https://www.linkedin.com/company/software-for-chemistry-&-materials

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