more efficient + accurate ways to compute redox potentials; using Rowan in the classroom; improving our documentation

periodic vs molecular; why periodic matters; tblite; open materials 2024 on GPU; more in the future

iteration cycles in chemistry; generative AI for linker design; forcefield optimizations; handling common DFT errors

empirical vs physics-based modeling; molecular representations; our descriptors workflow; PCA for visualizing chemical space; metal-organic complex spin states; input structures with PubChem; AI Grant

our vision for Rowan; new pricing model; academic plans; bond-dissociation-energy workflow; improvements to our API

pros and cons of global ADME/toxicity prediction; some case studies

why it matters; our approach; some case studies

making rowan faster: approximate guess Hessians; new compute backends, &c

Psi4 now available; new structure editor features; conformer search supports metals

predicting regioselectivity + reactivity; applications to synthesis & metabolism; updates to our team

why it matters; insensitivity to theoretical precision; our workflow; applications

new ui/ux; little calculations are now free; updates to our team

importance of tautomers and conformers; how it works; benchmarks

GPU fundamentals; adapting quantum chemistry to GPUs; how to run TeraChem

external vs internal security; what we've done wrong; API changes and AMI listing; where we hope to go

why we chose this problem; how Rowan pKa works; benchmarking on real systems; the future of ML + quantum chemistry

two orders of magnitude faster than conventional DFT; glimpsing the future of computational chemistry

When trying to explain what Rowan is, I often find that other scientists struggle to understand what quantum chemistry is or how it can be useful.

Five months later, here we are.

How it's used at present, and opportunities for the future.

Here's an unfortunate truth: when it comes to software, science is decades behind other fields.