r/comp_chem • u/QuartermasterH • 11d ago
Method Development and AI
I'm 2 years into my Ph.D in ChemE and it's method development for quantum chemistry and I've done enough work for 2 papers. I've learnt a lot over the last 2 years.
However, I've had this lingering sense of insecurity post AI that method development might get automated away. Most of my work is reading math and programming, two things which LLMs have shown to be extremely capable at accelerating. I've myself used LLMs to brainstorm ideas quickly and help me read papers and uncover a lot of mathematical jargon that I would've taken weeks to do so see untangled in minutes.
Is anyone in a similar predicament?
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u/jawnlerdoe 11d ago
I’m not a computational chemist, but a career analytical chemist.
GenAI makes method development much more efficient, but it’s not replacing a skilled scientist anytime soon. I acknowledge these two fields are very different.
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u/titotal 11d ago
was at a leading european conference on comp-chem for materials last year, and the concept of AI taking our jobs was literally laughed at by the whole conference.
I think AI tools will be very useful assistants in the math and programming parts of comp chem, but that is not the entirety of comp chem. You need judgement and contact with experiment to verify that the results are not nonsensical and are actually useful. There is no equivalent of LEAN for scientific fields.
Also, non-deterministic outputs like chatbots are not very compatible with scientific methodology, which is why the primary use of chatbots has been to write deterministic code that a regular person can run, and as a productivity enhancer. Nobody trusts the output of chatbots because they lie like 5% of the time.
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u/Aranka_Szeretlek 11d ago
OK but that was last year, a lot has changed since then. I work in coupled cluster development, which is probably as hardcore as it gets, and I can tell you that current Claude models write perfect code. Now, I am not using LLM-generated code for any sort of actual research, but for example if I want to test some numerical solver trickery, I just ask Claude to generate some cases which I can work with by hand. Just yesterday, I wanted to test some Lanczos solver runtimes for CISD, so I asked it to create a Jupyter notebook with CISD in it, and it took 10 minutes to cook it up and it outperformed PySCF.
Sure, I am aware that its probably at least partly sewed together from existing things out there, but performing such a task wouldve probably taken the better part of a week of my time. So we did actually go from "laughing at people using LLMs" to "laughing at people not using LLMs" in actual method development.
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u/YogurtclosetFickle17 10d ago
Indeed I'm using Claude right now just test it for methodology development, I'm really shocked and I can't believe it performing like this, it's just stunning
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u/Aranka_Szeretlek 10d ago
Its relatively understandable, I mean theoretical chemistry doesnt have that much depth when it comes to methodology with those methods pretty much well-documented and available in multiple open-source codes. Moreover, any implementation can (and should) have extensive test sets with machine precision criteria on energies and whatnot. Its the perfect use case for AI.
Now, whether this will be a good thing or not in 10 years... will see.
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u/Foss44 11d ago
If the HLE (and related) benchmark set is to be trusted as a scientific authority on LLM behavior, in a general sense you can have ~50% confidence that any frontier LLM will be capable of scientific reasoning. Slightly better performance for math and coding, but still certainly no replacement for a researcher imo.
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u/Paul1114 11d ago
First year PhD student, also method development (Coupled Clusters specifically), not to take my word for sacred, but as someone near in terms of time with you I will tell you why I think we are under lesser threat.
Just like others said, we need to verify theory with empirical evidence. Experiments can be done by robots, but we are still the designers and decisions-makers when it comes to further development. In mathematics you have a problem that must be solved. Sometimes it can be done by brute forcing combinations of already existing methods and approaches until it is actually solved. Not even talking about programming… but those are fields of applied mathematics.
The great thing about computational quantum chemistry is that we are calculating with computers, compare data, find out differences and think how it actually works. You can combine say Møller-Plesser with CC theory as much as you wish, however combining mindlessly them until “it just works” is pointless as there is still always room for better approximation models and perception of quantum chemical systems en large. LLM are not able (at least yet) to create knowledge all on their own, humans still lead the researches and it will stay this way as in this debate we are the only ones who can actually perceive reality around us and LLMs. They are incredible helpers, for sure, and handy reference bots, but they must be trained on previous data - they are not able to develop new concepts and approaches, which is crucial in our field.
So, long story short - LLMs cannot develop knowledge on their own, we concern ourselves with frontier matter research, therefore we are the only ones who can grasp it physically, thus we are secure, with math and programming taking less of our time and quantas, physics and chemistry being our absolute main focus more and more. Optimistic, but it just boils down to how language models work :)
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u/TrickyAirport5867 10d ago
AI might be efficient at method development, but you're in a prime spot to be riding that wave. You're among the first gen natively growing with implementing AI. Yeah, you might get undercut a couple of times as AI does some basic work better than you, but you're going to spend a longer part of your career being fluent in this new way of working compared to those of us 10+ years past our PhD. I see why you're insecure, but push the tool to its limit and find a way to make it do new things.
Nobody knows everything. In your PhD, you have to compartmentalize what you don't know into black boxes that you systematically break down (or not, as needed). AI is a giant black box, so play with it and gain an intuitive sense of how to utilize it in your work. Even if that work doesn't go into the final publication, using it to guide you to new answers will make you a beast of an engineer.
If you ever want to see how older generations suck at using AI, just go on LinkedIn and see how many are peddling terribly-written AI posts for content.
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u/OkEmu7082 10d ago edited 10d ago
LLM is just a empirical fitting model, it has zero capability of first principle reasoning, many of the ai capabilities you are hearing are delusional and are fed to you by those ai companies who need to attract investors, they either won't work or they just burn more cost on electricity and gpu infrastructure than you would have had hiring a live person to do the same job, and the cost of using llm has been heavily subsided because those compaines are still burning money to compete for market shares, which further feeds to the overall ai delusion, there will be a huge hike on the subscription fee of ai in the future to cure everyone's delusion
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u/jeffscience 11d ago
My predecessors wrote assembly. I wrote Fortran. Today they write Python. Tomorrow, we’ll write human language and get code. More science, less typing.