The pace at which science and engineering is moving is pretty hard to keep up with. I've always felt things move really fast, or appear that way, because of the real time updates we get through various outlets like arXiv, LinkedIn, and other platforms. The problem is that as a researcher/scientist/engineer/technologist it's pretty hard to keep up in a competent and competitive way. From my perspective, I'm always trying to ensure my understanding and technical ability stays as close as possible with what's coming into the spotlight. This year was no different, but it was really hard to stay on top of all the emerging developments in computational science, self-driving labs, AI for science, materials science, etc.
In terms of the category coverage of my posts this is what the 2025 treemap looks like:
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| 2025 category coverage |
Top 5 labels were: Machine Learning, Research, Atomistics, Tools, and AI. The research label must be related to research papers I've read or looked at, not research I'm doing myself2. I'm not surprised about ML and AI as I've had an interest in this more and more over time. The tooling is interesting since most people probably don't think to write about this, but I'm finding this blog a good place to document things related to Linux and Python tooling.
I missed a lot of good stuff this year but I'm going to try to summarize what I thought were some of the most interesting things I came across and wrote about, as well as what I am hoping to catch up on.
Self-driving labs
My hope for this year was to cover the rise and acceleration of self-driving lab research, development, and deployment. There was a lot of good stuff from topics brought up by people like Sterling Baird, who will now lead a new vertical cloud lab at Bringham Young University. In general the field has a lot of good momentum; the challenge will of course be orchestration and integration of different synthesis, characterization, and testing equipment to form self-driving loops that are either fully autonomous or have limited human intervention. I think I only wrote one post in the beginning of the year where I tried to argue that 2025 was the year of robotics in labs. I think this wasn't a bad prediction, as the number of publications on the topic increased by ~54% over 2024 1. So the growth is strong and led by some really capable academics and start-ups in my opinion.
What I'm hoping to see is more open standards by hardware vendors to allow more rapid integration. For robotic manipulation systems, this is probably in a good place and not the major issue, but rather characterization and testing equipment don't have easy IO and primitives to make integrating with decision making loops easy. I could be wrong since I'm not an active researcher or developer in this space but rather just a tinkering enthusiast, but from what I've done, unless you're building your own equipment (i.e. think open source optical microscope), it's going to be hard to fully integrate and automate SEM characterization in a decision making loop.
Materials discovery
This in my opinion is the topic du jour in computational materials science and this is another area where the number of publications is exploding, not so much on actual new materials but rather on the computational tools and techniques to accelerate the discovery and design of new materials. I'm not going to opine so much on whether this is legit or fraught with false promises, for that there are a lot of good people who are opining on that [1]. The main thing is this is here to stay for some time as both big companies, start-ups, and academics are all racing to make their impact in the computational and lab aspects of materials discovery.
I don't think this will slow down at all in 2026; it will probably see more growth, although I don't think it will be so much on a slew of new GNN or transformer model architectures, but probably rather more data and improved integration in workflows. Fine-tuning and distillation of pre-trained models is my guess where a lot of the research action will be.
Computing & simulations
I wrote a fair amount about tooling this past year, I'm not sure why but I think it was mainly as a way to document to myself things I always come back to and am tired of figuring out over and over again. Truth is I tend to refresh my Linux machine fairly often just to keep me from getting stale or too comfortable with forgetting how to do things. I wrote a post on the awesome uv package manager for python. I strongly suggest people check out uv for their python projects. I also wrote about backing up headless Linux machines using rclone and restic/borg/rsnapshot here ... to be honest I've used deduplicated backups several times to recover things I messed up.
On the simulation side I played around with g-xTB [2] and then wrote an ASE wrapper for it and a simple react UI that I wrote a post on. This was a nice little weekend project that I enjoyed doing, though I wish I could have spent more time making it robust and generally useful for scientific work.
I also very much enjoyed the paper on Boron carbide by Ghaffari et al. [3] which I summarized in the post I now know why boron carbide shatters. It just showed the good progress data-driven methods are making in terms of capturing the mechanisms behind material response and behavior.
AI for science
I spent quite a bit of time writing posts on AI for science, as it's just so active and interesting to follow. Most of my posts are improving my understanding and knowledge of the techniques and methods being developed and used. I started off the year by looking at graph-pes since I thought it was one of the cleanest implementations to quickly build and train MLIP models. I haven't played around with it in a while but hoping to find some fun use cases for it in 2026. Related to this was a post mentioning that mace uses the total energy rather than cohesive/binding energies so this subtle point should be kept in mind when using it.
Then I wrote about the ORB v3 MLIP model and my perspective on their equigrad loss regularization technique. I thought this was a really clever/efficient way to learn the rotational equivariance without having to use any SO(3) equivariant layers, which are computationally intensive and architecturally complex. In truth it may not be as necessary if libraries like cuEquivariance and FlashTP make the tensor products faster and more efficient.
Related to MLIPs I wrote a post on synthetic datasets & model distillation and how one can efficiently create synthetic datasets from fine-tuned foundation models to train cheaper student/surrogate models. I actually really think this is the direction things will head once foundation models all start to saturate the benchmarks. We will see most researchers taking them, fine-tuning, and then building more efficient models. I even believe some of these models will be fitted to physics-based potentials rather than just pure data-driven models.
I also wrote about the Skala XC functional and how it was able to achieve near chemical accuracy while doing it at computational cost comparable to meta-GGA functionals. This is probably going to continue getting more attention and improve. As discussed in computing & simulation, the post on g-xTB discussed how semi-empirical methods for molecules are showing DFT level accuracy but at tight-binding computational cost.
More on the ML/AI side I wrote a few posts on some topics I wanted to improve my foundational understanding. One of them was writing a post that was akin to me taking notes based on Petar Veličković's review talk on GNNs. I enjoyed this because it made me feel more confident in my understanding; but as always I need to ensure I truly understand the maths and the implementation. There was also the post on conformal prediction, which was something I had not been familiar with before and after reading and posting on this topic I see the value in this approach. Finally I wrote a post on trying ChatGPT's image generation tool for science illustrations and how it was a bit of a let down, not awful just not good enough for making scientific illustrations that one would want to use in a paper.
I also wrote in the beginning of the year a blog post on Bayesian parameter estimation and how it can be used to estimate the parameters of a line or other simple models. I just like these basic posts even though for many they are elementary topics, but for me it makes me ensure I understand things from basic principles. I say this because it is very easy to get confused in the advanced aspects of these topics if you can't tie it back to the basic things. Additionally, I'm sure I'll come back to posts like this in the future and say "wait, maybe I don't understand" or "what was I saying this is wrong". The beauty of a personal blog is it is not a formal publication or communication (as long as you indicate such), and so mistakes are less embarrassing3.
Books
This was the area that I'm a little down about since I didn't get to reach my reading goal for this year. I read a few books as listed in my reading list, but far from the 10 books I wanted to read. The issue is that many of them are dense technical monographs that take a lot of concentration to sit down and read, whereas the ones I finished were more the kind you pick up and read in a few hours during those random lull moments in my life. For sure this is something I will work on in 2026.
Right now, my reading list target for 2026 that are not technical books is:
- The Scaling Era: An Oral History of AI 2019-2023 by Dwarkesh Patel with Gavin Leech
- Invention and Innovation: A Brief History of Hype and Failure by Vaclav Smil
- Range: Why Generalists Triumph in a Specialized World by David Epstein
- The Technological Republic: Hard Power, Soft Belief, and the Future of the West by Alex Karp
- The Book of Why: The New Science of Cause and Effect by Judea Pearl
I'll be lucky if I get through 2 of these books in 2026, but I'll aim for all as I'm really eager to read these in addition to the technical books I have stacked up 😞.
Statistical mechanics & quantum mechanics
This was a bit off-topic I wrote about but it's just something that throughout the years I feel like I've become less strong in. From 2013-2016 I was in tip-top shape with my knowledge of stat-mech. I wrote towards the end of the year a post on the two roads to thermal equilibrium and how they relate. Then I wrote two posts on quantum related thoughts and topics. I felt like I need to write on this since in 2019-2021 quantum computing was my main research focus. But in recent years it's been off and on and I just feel like I've lost my sharpness in these areas. So I wanted to just write about them either in review of what I had done or just one of the topics to refresh my thinking. I'm sure mistakes were made in the post but at least I tried to put something together.
My hope is to continue on writing about these topics in 2026 to maintain my knowledge and understanding; you never know when this might come back into my main focus and thus good to ensure I'm sharp and knowledgeable about these foundational topics.
Outlook for 2026
So question is what will I try to write about in 2026? Don't know, but I'm hoping some good stuff. For one I will probably try to write posts similar to the ones that discuss papers I read. These posts help me dissect these dense papers and keep a nice log so I can refer back to them later and read something in my own words. I'd like to write some more blogs on weekend coding projects but I just don't get that much time to do this anymore. For example I have some synthetic data workflow that uses CrystaLLM and I'm hoping to write a post on it and post the dataset on Zenodo for others to explore and gauge its usefulness. I'm also hoping to write a little more on thermodynamic computing and of course self-driving labs.
Also I'm going to try to write/code a bit more on causal and Bayesian related topics since I want to become more proficient in actually building tools around these topics. Towards the other end of things I'm going to try to update my online presence, profiles, and such to keep my engagement with others in the community. So with that happy new year and see you in 2026! 🥳
Footnotes
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I used OpenAlex API to query the number of publications on the topic of self-driving labs. These were the query results for 2024 and 2025 as of 12/24/2025. ↩
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I don't post my work research here as this is my personal blog. Sometimes I'll post weekend projects or research-related efforts but always within my own time and not using any resources or results from work. ↩
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I never understood why making mistakes in science or technical research is viewed so negatively. I understand feeling embarrassed, but it seems like mistakes can ruin your reputation. It hasn't happened to me yet, but I've always felt that if I published a paper with a major mistake that needed to be retracted, I would (1) be grateful if someone caught and pointed it out... since we (I) don't want erroneous research to mislead others and (2) feel embarrassed, but as long as my intentions were unbiased, objective, and honest, I would acknowledge the mistake and learn from it. ↩
References
[1] J. Leeman, Y. Liu, J. Stiles, S. Lee, P. Bhatt, L. Schoop, R. Palgrave, Challenges in high-throughput inorganic material prediction and autonomous synthesis, (2024). https://doi.org/10.26434/chemrxiv-2024-5p9j4.
[2] T. Froitzheim, M. Müller, A. Hansen, S. Grimme, g-xTB: A General-Purpose Extended Tight-Binding Electronic Structure Method For the Elements H to Lr (Z=1–103), (2025). https://doi.org/10.26434/chemrxiv-2025-bjxvt.
[3] K. Ghaffari, S. Bavdekar, D.E. Spearot, G. Subhash, Influence of Crystal Orientation and Polymorphism on the Shock Response of Boron Carbide, SSRN Preprint, (2025). https://ssrn.com/abstract=5186595.
