Lessons on learning

I’m in a new team, and I’m learning new things. I’ve moved with the aim to further a project I started in which previously I was working alongside computational chemists, whereas now I’d like to run some dry-lab experiments of my own. Therefore, I’ve ‘joined the dark side’ (as one new teammate put it), infiltrated their group, and am now learning computational chemistry. With this, I am putting my maxim of lifelong learning into practice.

I’ve learned a huge amount over my post-student years, including in a couple of short, formal courses, but it’s been some time since I sat down to try and grasp an entire new field of chemistry in a highly structured way. Alongside the rest of my job, It is difficult to find time to learn alongside the rest of my job, but I have extra energy to do it with as it’s quite exciting. I am trying to study deeply because I know what I learn now is an investment in what I may need later.

Building on experience

This inevitably makes me consider parallels to when I started studying chemistry. Compared to then, I have a more holistic understanding of chemistry and how it’s used, which makes it immediately clear why each new concept is important and what I could do with it. I’m also surrounded by helpful experts, instead of fellow newbies fumbling their way through. For each new topic, there is a colleague one video call away who’s a specialist in that area.

The downside is that I have no time to do any of this. I had no computational experience before, and now I need to upskill simultaneously on practical applications and deep theory – and it’s also been a while since I last considered Taylor expansions and Hessian matrices. However, like the proverbial bike-riding, having studied the maths once means I am far from starting completely from scratch.

A mixed upside and challenge is that there’s just so much to learn, especially in pharma where we use computation for many different purposes. Interestingly, I am using my existing organic chemistry expertise more than when I was in a synthesis team: my teammates come across many detailed organic chemistry problems.

The barrage of advice advocating any singular true way to learn can ultimately be unhelpful

Compared to studenthood, it’s notable how quickly I can step into my own learning, now that I have gained a better understanding of how to absorb information. Taking copious notes has always been highly beneficial to how my brain works (it may not be to yours). I write energetically throughout seminars, even if I’ll never return to re-read the words: it greatly improves my recall. What didn’t help was vibrantly colour-coding these notes. I think I expected that the potential for pattern recognition would function as a memory aid, but it doesn’t seem it bore true.

To learn, I am using a specialist textbook, alongside running dry-lab simulations under the guidance of a master in the field. A digital version of the textbook is easier to search and scroll through; however I also open the bulky, physical book when invited to recall specific, numbered equations on earlier pages. It seems much more effective to have both available.

I make lots of black ink notes – on actual paper. While all the maths may be perfectly writeable by computer, it’s still slower than by hand. I remember sarcastic warnings at university about ‘pointlessly’ re-writing an abridged version of the book, but having done the control experiment, I still believe I grasp material more intimately when my hand and brain connect to write. I really think that the barrage of advice for students advocating any singular true way to learn can ultimately be unhelpful, because we are all different.

Different definitions

Computation is a different take on organic chemistry, and not just a more precise perspective on geometry and mechanisms. Only now have I realised why computational chemists refer to ‘chiral atoms’ (when mathematically, chirality is a property of the whole molecule). A modeller may well treat an isolated atom as if it is chiral, because they deal much more with the local atomic environment, and helpful surrogate models do not completely match reality.

Likewise, ‘organometallics’ is seen as a useful group for which computational techniques that are good for both organic and metal components are needed, but whether carbon is directly bonded to the metal is much less relevant. It seems wrong at first to an organic chemist, but it’s just a set of differing definitions.

Juggling study alongside the admin, initiative leadership and matrix teamwork of my normal job isn’t going to be easy. But I think a chemical science education has stood me in good stead for moving beyond my existing background.