How new computational workflows can make it possible to design complex modifications to heterocyclic cores.

expanding Rowan's reaction modeling toolkit; verifying transition states; reaction mechanism insights

Building up an understanding of how energy barriers and the potential energy surface affect the rate of a reaction.

cultural barriers in science; integrating RDKit with quantum chemistry; Rowan's new API; changes to billing

not all hydrogen-bond donors are created equal; the pKBHX scale; predicting pKBHX in Rowan; case studies & a preprint

Here, we report a robust black-box workflow for predicting site-specific pKBHX values in organic molecules with minimal computational cost.

benchmarking as driver of systematic methodological improvement; our new benchmarking website; new NNPs on Rowan; GPU-based inference coming to more users

Ready to test your knowledge of density-functional-theory functionals in a multiple-choice game of "real or fake"?

We introduce Wiggle150, a benchmark comprising 150 highly strained conformations of adenosine, benzylpenicillin, and efavirenz, to validate computational protocols involving non-equilibrium systems and guide the development of new density functionals and neural network potentials.

A layman's guide to cutting your way through the web of DFT functionals, explaining GGAs, mGGAs, hybrids, range-separated hybrids, double hybrids, and dispersion corrections.

A layman's guide to neural network potentials (NNPs), which can run high-accuracy atomistic simulations many orders of magnitude faster than traditional quantum mechanics (QM) simulations.

Computational chemistry is one of the toughest and most expensive simulation problems in all of science.

The first post in a series focused on working with Rowan's new orbitals workflow.

Startup aims to replace expensive, slow, quantum mechanics simulations with inexpensive, fast machine-learned potentials.

visualizing molecular orbitals and isosurfaces and spin densities; bond orders, charges, and multipole moments; a recent podcast appearance

Understanding the two fundamental paradigms in computational chemistry, and when each is appropriate.

collaboration in organizations; predicting slow jobs; myriad ui/ux improvements; faster conformer searching; new AIMNet2; vDZP preprint

Here, we report that the vDZP basis set can be used with a wide variety of density functionals to produce efficient and accurate results comparable to those of composite methods without reparameterization.

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

Some commonplace errors in density-functional-theory calculations, and how to fix them.

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

A look at predicting the spin states of metal-organic complexes and its use in synthesis and modeling.

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

Adding intramolecular hydrogen-bond acceptors can help otherwise recalcitrant drugs make it past the blood–brain barrier.

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

In this study, we investigate AIMNet2, a machine-learned interatomic potential, as a low-cost surrogate for electronic structure theory in pKa prediction.

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.