Rowan Reads

Why Pairwise Force Fields Can Reproduce Ionic Conductivity but Not Solvation Shells | ChemRxiv

Pairwise force fields with scaled ionic charges are routinely validated against ionic conductivity or other collective transport measurements and used to interpret Li+ solvation and transport in...

Rowan Reads · Nov 13, 2026 · 2:26 PM UTC

Rowan Reads@RowanReads2h

arxiv.org/abs/2611.15939

Atomistic simulations of electrochemical systems with machine...

Modern machine learning potentials (MLPs) enable large-scale atomistic simulations at unprecedented accuracy. The incorporation of explicit physical interactions such as long-range electrostatics...

643

Rowan Reads · Nov 13, 2026 · 2:25 PM UTC

Rowan Reads@RowanReads2h

arxiv.org/abs/2611.15603

Are Scaling Laws of Universal MLIPs Universal?

Neural scaling laws enable the prediction of returns from larger training datasets, but machine-learned interatomic potential (MLIP) datasets are heterogeneous and expensive to generate, and equal...

Rowan Reads@RowanReads21h

503

Rowan Reads · Nov 12, 2026 · 7:37 PM UTC

arxiv.org/abs/2611.14511

Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents

Predicting how a ligand binds to its target is central to small-molecule drug discovery. Modern cofolding models can generate diverse candidate poses, but reliably ranking these candidates remains...

Rowan Reads@RowanReadsNov 12

288

Rowan Reads · Nov 12, 2026 · 3:57 PM UTC

a herald of things to come arxiv.org/abs/2611.13329

Agent-Guided Discovery of Exchange-Correlation Functionals with...

Accurate, compact exchange--correlation functionals are central to predictive density functional theory. Symbolic search and language-model agents can propose new functional forms, but it remains...

Rowan Reads@RowanReadsNov 12

3,606

Rowan Reads · Nov 12, 2026 · 3:43 PM UTC

arxiv.org/abs/2606.12326

Transferable Machine Learning of Electronic Hamiltonians with...

Machine learning (ML) of electronic Hamiltonians offers a unified route to electronic wave functions and physical observables. We introduce a Hamiltonian learning framework built on electronic...

Rowan Reads@RowanReadsNov 11

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Rowan Reads · Nov 11, 2026 · 9:51 PM UTC

How Ready Are Universal Machine-learned Interatomic Potentials for Carbon Capture Simulations? |...

Nanoporous materials offer a tunable platform for selective carbon capture through adsorption, and identifying the best candidates requires computational screening built on accurate and transferable...

Rowan Reads@RowanReadsNov 11

1,072

Rowan Reads · Nov 11, 2026 · 9:15 PM UTC

arxiv.org/abs/2611.14287

ANTIOCH-VS: From Sequence to Hits with Modular, Open-Source...

Structure-based virtual screening needs a receptor conformation that fits the ligand class, but for most targets no ligand-bound crystal is available -- and a single predicted model or an apo...

Rowan Reads@RowanReadsNov 10

379

Rowan Reads · Nov 10, 2026 · 9:54 PM UTC

GPU-accelerated analytic Hartree–Fock gradients | ChemRxiv

Analytic nuclear gradients underpin much of computational quantum chemistry, driving geometry optimization and supplying the forces of ab initio molecular dynamics. Yet on graphics processing units...

Rowan Reads@RowanReadsNov 10

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Rowan Reads · Nov 10, 2026 · 2:15 PM UTC

today in betteridge's law of headlines arxiv.org/abs/2611.10378

Can AI Agents Reliably Optimize Computational Chemistry Codes?

Artificial intelligence (AI) approaches have opened new possibilities in computational and theoretical chemistry. AI agents can now execute computational workflows autonomously, and their use in...

Rowan Reads@RowanReadsNov 10

1,424

Rowan Reads · Nov 10, 2026 · 2:15 PM UTC

arxiv.org/abs/2611.12002

SATE: Transferable Spin-Adapted Neural Wavefunctions

Accurate prediction of molecular reactivity requires both accurate and computationally efficient description of electron correlation and spin across various molecular geometries and electronic...

603

Rowan Reads · Nov 9, 2026 · 8:06 PM UTC

arxiv.org/abs/2605.15630

Reweighting free energy profiles between universal machine...

Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the free energy profile along a reaction...

1,167

Rowan Reads · Nov 9, 2026 · 8:00 PM UTC

Polarization Is Required to Model Electrostatic Preorganization: The Case of Chorismate Mutase |...

Electrostatic preorganization contributes to enzymatic catalysis through transition state stabilization and is often investigated using classical molecular dynamics (MD) simulations that employ...

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Rowan Reads · Nov 9, 2026 · 7:51 PM UTC

chemrxiv.org/doi/pdf/10.2643…

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Rowan Reads · Nov 9, 2026 · 7:51 PM UTC

arxiv.org/abs/2611.05103

Molecular Prior Networks

Predicting affinity between small molecules and their protein targets remains one of the central problems in drug discovery. Due to the substantial cost of wet-lab experiments, machine learning...

1,650

Rowan Reads · Nov 9, 2026 · 4:45 PM UTC

arxiv.org/abs/2611.08874

Fortuitous Cancellation of Competing Effects in the Free Energy of...

Few molecular processes are more fundamental to chemistry than the autoionization of water, whose equilibrium constant, $K_w \approx 10^{-14}$, underlies acid-base equilibria and the chemistry of...

589

Rowan Reads · Nov 9, 2026 · 4:25 PM UTC

Training of Neural Network Machine Learning Potentials for Activation Barrier Calculations in...

Machine-learning interatomic potentials (MLIPs) are opening many new opportunities to extend the capabilities of computational materials science. MLIPs deliver increased throughput and simulations at...

1,326

Rowan Reads · Nov 9, 2026 · 4:22 PM UTC

arxiv.org/abs/2611.08151

Machine learning quantum data to accelerate quantum Monte Carlo

Simulating strongly correlated electrons in molecules and materials remains a central goal for quantum computing. A promising strategy uses quantum processors to prepare trial wavefunctions that...

452

Rowan Reads · Nov 9, 2026 · 4:22 PM UTC

arxiv.org/abs/2611.07133

Context-Aware Automated NMR Structure Elucidation

Machine learning approaches are rapidly revolutionizing the ability to analyze and extract structural insights from chemical data, enabling high-throughput chemical discovery. While existing...