Bryant Y. Li | Searcharxiv

arXiv2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.

physics.soc-ph↗

arXiv2021

Quantifying multi-point ordering in alloys

A central problem in multicomponent lattice systems is to systematically quantify multi-point ordering. Ordering in such systems is often described in terms of pairs, even though this is not sufficient when three-point and higher-order interactions are included in the Hamiltonian. Current models and parameters for multi-point ordering are often only applicable for very specific cases and/or require approximating a subset of correlated occupational variables on a lattice as being uncorrelated. In this work, a cluster order parameter (ClstOP) is introduced to systematically quantify arbitrary multi-point ordering motifs in substitutional systems through direct calculations of normalized cluster probabilities. These parameters can describe multi-point chemical ordering in crystal systems with multiple sublattices, multiple components, and systems with reduced symmetry. These are defined within and applied to quantify four-point chemical ordering motifs in platinum/palladium alloy nanoparticles that are practical interest to the synthesis of catalytic nanocages. Impacts of chemical ordering on alloy nanocage stability are discussed. It is demonstrated that approximating 4-point probabilities from superpositions of lower order pair probabilities is not sufficient in cases where 3 and 4-body terms are included in the energy expression. Conclusions about the formation mechanisms of nanocages may change significantly when using common pair approximations.

cond-mat.mtrl-sci↗