Optimizers in ASE

Optimising even simple intermediate on metal surfaces might be tricky. They tend change the adsorption site. OOH also very flexible in changing geometry. Thus, some optimizers get stuck (like BFGSLineSearch) while others just crush (like GPMin).

Here is my test with OCPCalculator and EquiformerV2-31M-S2EF-OC20-All+MD (on 1 CPU in Google Colab). Optimisation of OH on Pt(111) is simple.

Here SciPyFminBFGS failed and other optimizers ended up with the same structure, shown below.

Optimisation of OOH on Pt(111) is challenging. GoodOldQuasiNewton, FIRE, FIRE2, MDMin, and BFGSLineSearch (QuasiNewton) do not converge in 95 cycles (which I set as a maximum number of cycles). GPMin, SciPyFminBFGS, SciPyFminCG, and LBFGSLineSearch failed.

Geometry obtained with FIRE2 (similar to BFGS and FIRE)
Geometry obtained with LBFGS. It is clearly far from the optimum.

ResearchCOMP – the European Competence Framework for Researchers

The new Framework for Researchers is a cool tool for planning your career. However, it is so hard to understand and remember. That is why I suggest using the following tree analogy.

ResearchCOMP is an essence like a tree. Roots of critical thinking (1) with trunk of self-management (2) and branches of collaboration (3) hold leaves as research (4) and fruits as impact (5). Water = management 6) and sun = tools (7) nourish the research tree.
The following illustration has been prepared by my son:

Art created by Naran Pavanello, 9 years old

ResearchCOMP, Like a Tree

Roots of critical thinking, deep and strong,
The trunk of self-management keeps you moving along.
Branches of collaboration, reaching far,
Leaves of research, showing who you are.

Fruits of impact, growing bright,
Water of management keeps it right.
Sunlight from tools makes it thrive,
Nourishing the research, keeping it alive.

“Content created with assistance from ChatGPT, an AI language model by OpenAI.”

With this analogy it is now easy to explain what a PhD. student should focus on.

Cognitive Abilities (Roots):
As a Ph.D. student, you first build your thinking skills like problem-solving and creativity. These skills are like roots that hold you on the ground.

Self-Management (Trunk):
You need to manage your time and stress, set goals, and balance your workload. This helps you stay focused and handle challenges, like a trunk.

Working with Others (Branches):
Collaboration is key. Branch with supervisors and teams, seek feedback, and build connections to grow your research.

Managing Research (Water):
You manage your PhD project. This includes organizing resources, meeting deadlines, and planning your work. It is like a flow.

Managing Research Tools (Sun):
Learn to use research tools like data management and software. These help keep your work organized. It is like external source of energy = sunlight.

Doing Research (Leaves):
This is your core work – running experiments, analyzing data, and writing papers to build expertise in your field. Leaves are as dynamic as your research.

Making an Impact (Fruits):
Your research creates impact. Publish, attend conferences, and share your findings as fruits to the society.

And now you can compare a young tree to a mature one. The difference is that the young one is expected to “bloom”, whether the mature one is expected to give “fruits”, as illustrated below.

MD simulation of BMPyrDCA between graphene walls

Simple demonstration of a molecular dynamics simulation of 408 BMPyrDCA ionic pairs between two graphene walls.

Inputs (packmol.inp, STEEP.mdp, RUN.mdp, topol.top) and force field parameters: github.com/vilab-tartu/LOKT.02.048/tree/master/MD_Gr-BMPyrDCA_pbc. The force fields are taken from github: github.com/vladislavivanistsev/RTIL-FF. References are given within the files.

Continue reading “MD simulation of BMPyrDCA between graphene walls”

MD simulation of bulk BMPyrDCA ionic liquid

Simple demonstration of a molecular dynamics simulation of 25 BMPyrDCA ionic pairs in a box.

Inputs (packmol.inp, STEEP.mdp, RUN.mdp, topol.top) and force field parameters: github.com/vilab-tartu/LOKT.02.048/tree/master/MD_BMPyrDCA_box. The force fields are taken from github: github.com/vladislavivanistsev/RTIL-FF. References are given within the files.

Continue reading “MD simulation of bulk BMPyrDCA ionic liquid”