AI-driven inverse design of materials: Past, present and future
XQ Han, XD Wang, MY Xu, Z Feng, BW Yao… - Chinese Physics …, 2024 - iopscience.iop.org
The discovery of advanced materials is the cornerstone of human technological
development and progress. The structures of materials and their corresponding properties …
development and progress. The structures of materials and their corresponding properties …
Data-efficient multifidelity training for high-fidelity machine learning interatomic potentials
J Kim, J Kim, J Kim, J Lee, Y Park… - Journal of the American …, 2024 - ACS Publications
Machine learning interatomic potentials (MLIPs) are used to estimate potential energy
surfaces (PES) from ab initio calculations, providing near-quantum-level accuracy with …
surfaces (PES) from ab initio calculations, providing near-quantum-level accuracy with …
Orb: A fast, scalable neural network potential
Self-assembly of architected macromolecules: Bridging a gap between experiments and simulations
Macromolecular self-assembly is essential in life and interfacial science. A macromolecule
consisting of chemically distinct components tends to self-assemble in a selective solvent to …
consisting of chemically distinct components tends to self-assemble in a selective solvent to …
Taming Multi-Domain,-Fidelity Data: Towards Foundation Models for Atomistic Scale Simulations
T Shiota, K Ishihara, TM Do, T Mori… - ar** machine learning models for crystal property predictions has been hampered by
the need for labeled data from costly experiments or Density Functional Theory (DFT) …
the need for labeled data from costly experiments or Density Functional Theory (DFT) …