Quantum 3xplorer: AI-Native Engine for Next-Generation Quantum Materials Modeling
Abstract
Quantum-mechanical approaches, such as density functional theory (DFT), underpin much of modern computational materials science and are the primary source of data for AI-driven discovery. Yet the established electronic-structure codes today represent decades-old software architectures, making new physical models slow and costly to implement, validate, and deploy on emerging hardware. We present Quantum 3xplorer (Q3, pronounced "Quantum Explorer"), an AI-native quantum simulation engine written from scratch by AI coding agents under the direction and supervision of domain scientists. As a first and demanding validation case, we implement plane-wave pseudopotential DFT end to end: norm-conserving, ultrasoft, and projector-augmented-wave formalisms, spin polarization, DFT+U, and dispersion corrections, together with forces, stresses, structural relaxation, and electronic-structure analysis. Q3 reproduces established reference implementations, runs faster on a single core in representative benchmarks, and scales across GPU-accelerated supercomputers. It was developed in weeks rather than years. Its layered, physics-first architecture separates abstract quantum-mechanical concepts from their numerical representations, making its framework extendable beyond plane waves to other basis sets and levels of theory. Its inputs and outputs follow structured, schema-validated data standards, making every result AI-ready. Q3 is open source under Apache 2.0 and built to be reused, extended, and improved by the research community. Q3 points to a paradigm shift in how computational quantum science is done. Scientists define the physics, formulate the models, and set the criteria for validation; AI agents write, test, and optimize the code. The decades-long path from a new theory to a trusted, high-performance implementation can thus collapse to days, significantly accelerating scientific discovery.
Related
- Quantum Theory of Third-harmonic Generation in Epsilon-Near-Zero Materials
- Symmetry-Resolved Second Harmonic Generation in Quantum and Functional Materials
- Quantum Spin Wave Theory with Multi-Spin Interactions for Magnetic Materials Modeling
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- Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design
BibTeX
@misc{biryukov2026quantum,
title = {{Quantum 3xplorer: AI-Native Engine for Next-Generation Quantum Materials Modeling}},
author = {Vsevolod Biryukov and Sergey V. Barabash and Pranab Kumar Das and Jack Deslippe and Mauro Del Ben and Felipe H. da Jornada and Yuming Shi and Johannes Voss and Zhenglu Li and Piero Altoe and Kamal Choudhary and Ruth Pachter and Steven G. Louie and Marvin L. Cohen and Timur Bazhirov},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.42108},
eprint = {2610.42108},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.42108},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum 3xplorer: AI-Native Engine for Next-Generation Quantum Materials Modeling
AU - Vsevolod Biryukov
AU - Sergey V. Barabash
AU - Pranab Kumar Das
AU - Jack Deslippe
AU - Mauro Del Ben
AU - Felipe H. da Jornada
AU - Yuming Shi
AU - Johannes Voss
AU - Zhenglu Li
AU - Piero Altoe
AU - Kamal Choudhary
AU - Ruth Pachter
AU - Steven G. Louie
AU - Marvin L. Cohen
AU - Timur Bazhirov
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.42108
UR - https://arxiv.org/abs/2610.42108
AB - Quantum-mechanical approaches, such as density functional theory (DFT), underpin much of modern computational materials science and are the primary source of data for AI-driven discovery. Yet the established electronic-structure codes today represent decades-old software architectures, making new physical models slow and costly to implement, validate, and deploy on emerging hardware. We present Quantum 3xplorer (Q3, pronounced "Quantum Explorer"), an AI-native quantum simulation engine written from scratch by AI coding agents under the direction and supervision of domain scientists. As a first and demanding validation case, we implement plane-wave pseudopotential DFT end to end: norm-conserving, ultrasoft, and projector-augmented-wave formalisms, spin polarization, DFT+U, and dispersion corrections, together with forces, stresses, structural relaxation, and electronic-structure analysis. Q3 reproduces established reference implementations, runs faster on a single core in representative benchmarks, and scales across GPU-accelerated supercomputers. It was developed in weeks rather than years. Its layered, physics-first architecture separates abstract quantum-mechanical concepts from their numerical representations, making its framework extendable beyond plane waves to other basis sets and levels of theory. Its inputs and outputs follow structured, schema-validated data standards, making every result AI-ready. Q3 is open source under Apache 2.0 and built to be reused, extended, and improved by the research community. Q3 points to a paradigm shift in how computational quantum science is done. Scientists define the physics, formulate the models, and set the criteria for validation; AI agents write, test, and optimize the code. The decades-long path from a new theory to a trusted, high-performance implementation can thus collapse to days, significantly accelerating scientific discovery.
ER -