AutoPot: New Software Automates Construction of Machine-Learning Potentials for Computational Physics
Researchers have developed AutoPot, a software tool that automates the construction and management of machine-learning interatomic potentials (MLIPs) used in computational physics simulations. MLIPs enable atomistic modeling with near-quantum mechanical accuracy, but creating effective training datasets for different applications has been complex and difficult to reproduce. AutoPot addresses this by automating training protocols and making them easier to implement, analyze, and share across research groups.
AutoPot is a new software framework designed to streamline the creation of machine-learning interatomic potentials, which are computational models that predict atomic interactions with accuracy approaching quantum mechanics. The tool automates the selection of training configurations from large candidate datasets and can dynamically select configurations during molecular dynamics simulations, addressing a key challenge in MLIP development: ensuring training data covers the atomic neighborhoods encountered in actual simulations. Built on existing software infrastructure (BlackDynamite for parallelized task execution and Motoko for workflow orchestration), AutoPot currently supports Moment Tensor Potentials and VASP calculations but is designed for extensibility. The software's Python-based architecture allows researchers to integrate their own code without complex parsing, and the authors indicate it will be straightforward to add support for other MLIP and ab initio codes. This approach aims to reduce the complexity and improve reproducibility of MLIP training protocols, which have become increasingly convoluted as researchers layer active learning and fine-tuning strategies.
What different sources said
- arXiv cs.LGCenter
Inverse design of bespoke interatomic potentials via active learning by information-matching
Related
Gut Bacteria Enzyme Found to Break Down Heat-Processed Food Compounds, Producing Novel Biogenic Amines
Researchers have discovered that an enzyme in common gut bacteria can degrade N-epsilon-carboxymethyllysine (CML), a compound formed during thermal food processing, producing previously unknown biogenic amines. The enzyme, ornithine decarboxylase SpeC from enterobacteria, acts on CML and related modified lysine derivatives through a low-level 'underground' catalytic activity. This finding suggests a previously unrecognized communication axis between thermally processed dietary compounds and gut microbial physiology, with potential implications for host health.
Full-Length Gene Sequencing Reveals Two Distinct Bacterial Communities in Black-Legged Ticks Expanding Into Canada
Researchers used Oxford Nanopore full-length 16S rRNA gene sequencing to characterize the microbiome of Ixodes scapularis black-legged ticks collected in Nova Scotia, Canada, distinguishing between tick-adapted bacteria and environmentally acquired bacteria. The study comes as I. scapularis — the primary vector of Lyme disease — is rapidly expanding northward into Canada due to climate change. The findings suggest that environmentally derived bacteria in tick microbiomes are not mere contamination, which has implications for how tick microbiome data is collected and interpreted across surveillance studies.
Study Identifies Metabolic Link Between Cell Envelope Stress and Biofilm Formation in Bacteria
Researchers have discovered that the metabolite acetyl-CoA directly inhibits enzymes that degrade the bacterial signaling molecule c-di-GMP, connecting cell envelope biosynthesis stress to biofilm formation in Pseudomonas aeruginosa. The study found that sub-inhibitory concentrations of antibiotics targeting early peptidoglycan biosynthesis — but not other antibiotic classes — elevate c-di-GMP levels by reducing phosphodiesterase activity, with acetyl-CoA competing for the enzyme active site. Because the relevant enzyme domain is broadly conserved across bacterial species, this checkpoint mechanism may be widespread and could have implications for understanding antibiotic-induced biofilm responses.