Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration
Abstract
This work discusses and tests the potential of reaction graphs for material synthesis, in which events consist of reaction steps (actions) and their outputs (observations). The importance comes from the requirement to synthesize technologically useful materials, especially those that are kinetically stabilized. Often, it is a science that follows years of trial and error, culminating in (published) successful recipes, but is truly driven by far more (unpublished) failures. The entry into the era of AI provides an opportunity to use learning algorithms to encode rules from multi-dimensional synthesis pathways, regardless of their technological usefulness, and to decode both forward prediction and inverse learning modes. The main incentive is to accelerate material discovery by generating a global knowledge base that can be shared among data agents and contributed to by them. We will focus on several scenarios in the common synthesis process during solid-state synthesis, primarily because these reactions are kinetic in nature and require closer monitoring and control of the reaction history.
Related
- Symmetry Rules for Cavity Materials Engineering with Linearly Polarized Vacuum Fields
- High-efficiency solid-state electrochemical thermal switching through phonon engineering of 2D materials
- Quantitative Modelling of Solvation in 2D Materials via Alchemical Decoupling and Potential Energy Surfaces
- MASON: Materials Atomistic Structure builder Operated in Natural language
- Electronic structure, magnetic interactions, and magnonics of 2D trichloride materials
BibTeX
@misc{rakita2026complex,
title = {{Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration}},
author = {Yevgeny Rakita and Paul Todd and James R. Neilson and Simon J. L. Billinge},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.18314},
eprint = {2610.18314},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.18314},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration
AU - Yevgeny Rakita
AU - Paul Todd
AU - James R. Neilson
AU - Simon J. L. Billinge
PY - 2026
DA - 2026/10/13
PB - arXiv
DO - 10.48550/arxiv.2610.18314
UR - https://arxiv.org/abs/2610.18314
AB - This work discusses and tests the potential of reaction graphs for material synthesis, in which events consist of reaction steps (actions) and their outputs (observations). The importance comes from the requirement to synthesize technologically useful materials, especially those that are kinetically stabilized. Often, it is a science that follows years of trial and error, culminating in (published) successful recipes, but is truly driven by far more (unpublished) failures. The entry into the era of AI provides an opportunity to use learning algorithms to encode rules from multi-dimensional synthesis pathways, regardless of their technological usefulness, and to decode both forward prediction and inverse learning modes. The main incentive is to accelerate material discovery by generating a global knowledge base that can be shared among data agents and contributed to by them. We will focus on several scenarios in the common synthesis process during solid-state synthesis, primarily because these reactions are kinetic in nature and require closer monitoring and control of the reaction history.
ER -