In this preprint on arxiv I explored how agents based on reasoning models perform in ALD process optimization tasks. The results are both impressive and somewhat mixed. One one hand, reasoning models were able to understand and execute elementary atomic layer deposition process optimization tasks. On the other, they sometime either fail or struggle to find the optimal conditions, which makes them borderline usable for real world application.
For this work I didn’t use a finetuned model, but a commercially available model. The motivation is that these models are widely used, and therefore there is a lot of value in understanding how they perform. The other more prosaic reason is that there are limitations to the type of models that we can access. This left out some good open weight models such as DeepSeek-R1.
For the test, I designed a few ideal and non-ideal self-limited processes that representative of the type of ALD processes a researcher is likely to encounter in the wild. For each of these processes, the task was very simple: find the optimal dose time for the precursor and co-reactant that leads to a saturated growth per cycle with a process time that is as low as possible. This is a very much an idealized version of the type of optimization that is carried out over and over again.
The work is currently under review. As soon as the paper is published I will release the code required to run these challenges. Hopefully someone can take on the challenge of designing performant agents based on open weights models that can be run locally.
]]>Not sure what reference manager people use these days but, if you use something like Zotero or have downloaded any reference file from Springer Nature, you most likely have come across a RIS file.
The RIS format is old, so old that I had to use the Wayback machine to find an original specification. Now I keep a copy in the gris documentation for reference.
It is also a format that publishers tend to play with, many years back WebOfScience used a RIS format with different tag formats when you downloaded references as other. Recently, AIP has messed up with the format adding headers that were not part of the original specification.
The new version of gris now doesn’t break when reading files with headers that should not be there. It now has proper documentation and a bit of testing.
The main purpose of gris is to parse these old files into Python objects that can be then manipulated or converted into more universal formats, such as JSON. While it is completely unrelated to my daily work, as it happens it has made its way to some of my papers as well.
There are a few items in my to do list:
This is an issue when trying to write code dealing with physical systems. A quick perusal of Python packages shows a zoo of possible solutions designed to help you do operations with physical magnitudes. To add to the confusion, here is a new stable release of my side project: properunits.
The core idea of properunits is that dealing with physical magnitudes should simply be an IO problem. A user (me) wants to work with a specific unit and my code wants to work with its own unit. Once you do that transformation, you should be good to go because you have transformed your 5 gallons into a number using the proper units your code can work with. In my case, this means working with SI+ units, which to me it means SI units plus maybe one or two exceptions.
So this is how it works:
from properunits import Temperature
my_temp = Temperature(20, 'C')
the_proper_temperature = my_temp.x
What properunits does is provide a consistent interface to transform an input with user-defined units into a proper value (in the example K) that my code can deal with.
You can know which unit is considered a “proper unit” by querying:
my_temp.unit
which would return the string 'K'.
Or, if you need to remind yourself which unit did you use, you can retrieve the original value through:
temp, unit = my_temp.value
You can check the list of physical units available via:
my_temp.list_units()
The documentation for propertunits can be found here
Basically anything else. I wanted to have something that would allow me to connect the experimental side of my research with the models and simulations I create. I just needed to solve the IO problem, not to be able to work with arbitrary units. I may entertain in the future the idea of being able to use other units as the “proper unit”, but I must admit it is fairly low in my priority list. What I may do is expand the range of units to include specific use cases, like nautical miles and the like.
]]>After more than 20 years this year I published a new paper on the journal Physics of Plasmas where I explored how we can leverage machine learning to optimize processes in plasma enhanced chemical vapor deposition. Two relevant things about this paper:
This is a work that was a few years in the making. I had the chance to collaborate with some of the most talented researchers in my field to write an introduction to atomic layer deposition. The link to the paper is here. It is, alas, behind a paywall.
SC2025 is a conference focused on high performance computing. However, it has a post-Moore computing component, and as a member of the program committee for 2025 I was asked to chair one of the sessions in quantum computing
Overall, a busy couple of months.
]]>Mis dos sitios en español son:
cienciaypython: tutoriales usando jupyter book y MyST.