Search arXiv⌕ Search

arXiv · 2610.32678

Probing the limits of forward screening and inverse design of crystal materials

Abstract

Crystal materials are designed either by screening known crystals for a target property or by generating new crystals with that property. Both routes are judged against public databases of known crystals, but these databases name the same crystal differently. Several materials announced as new by recent generative and autonomous discovery campaigns have since proved to be known phases or close variants of them. The limits of both routes remain unmeasured. Here we probe them by matching the crystals of 55 databases by structure into one self-extending database of 14.6 million crystals and 131.8 million property values, each traced to its source. Only 4.5% of its crystal-property pairs carry a value. To fill the empty pairs, we developed CGTNet, an equivariant graph transformer with state-of-the-art accuracy on several tasks of two public benchmarks. Trained on each task, it forms a library of property models whose predictions we release. The reach of forward screening has been underestimated. Known crystals hold tens of thousands of ultra-incompressible candidates without a recorded bulk modulus, and a post hoc audit of synthesis evidence finds among them ReN2, whose bulk modulus was measured at 428 GPa. The novelty claimed for inverse design has been overestimated. At an equal verification budget, a stiffness-conditioned generator returned more stiff hits than screening (100 versus 55), but all contained osmium and 82 repeated recorded compositions. Integrating and completing the databases of known crystals therefore moves the limits of both routes: it widens what screening can reach and sets the reference against which generated crystals are judged new.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhilong Song, Zijun Pang, Lixue Cheng. 2026-10-23. Probing the limits of forward screening and inverse design of crystal materials. https://arxiv.org/abs/2610.32678

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Laser Melting and Loss of Interlayer Coherence of the Charge Density Wave Order in TiSe${_2}$ observed by time-resolved neutron diffraction

Time-resolved elastic neutron scattering combined with laser-induced heating was used to probe the evolution of the periodic lattice distortion (PLD) associated with charge-density-wave (CDW) order in 1T-TiSe$_{2}$. Upon pulsed laser heating, the crystal undergoes a phase transition, where the transient response of the average lattice is isotropic. In contrast, the PLD exhibits an anisotropic behavior. The superlattice intensity is rapidly suppressed in-plane and out-of-plane upon heating, consistent with melting of the ordered state, but recovers more quickly in-plane than along the c-axis as the sample cools below the CDW transition temperature. The out-of-plane component shows pronounced broadening due to reduction of the interlayer correlation length while the in-plane correlations recover. The disparity between the nearly isotropic lattice response and the anisotropic recovery of the superlattice demonstrates that the CDW dynamics cannot be described solely by the evolution of the average lattice temperature. Instead, the results indicate that interlayer phase coherence represents the slowest-relaxing component of the ordered state. These findings support a picture in which the CDW instability is established primarily within the 1T-TiSe${_2}$ layers, while three-dimensional long-range order depends on weaker coupling between layers. More broadly, they demonstrate how direction-resolved time-resolved neutron diffraction can disentangle thermal and correlation-driven contributions to CDW dynamics in low-dimensional materials.

cond-mat.mtrl-sci↗

AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction

Metal-organic frameworks (MOFs) are promising materials for applications such as direct air capture, where performance depends on how adsorbates bind within the framework. Accurately predicting MOF-adsorbate configurations is therefore important for screening candidate materials. However, traditional approaches are computationally expensive and require a known host structure, while existing generative models rely on rigid-body assumptions and do not explicitly model adsorbates. We introduce AtomMOF, a scalable all-atom flow-matching model that jointly predicts MOF-adsorbate structures from discrete building blocks and adsorbates. Built on a Diffusion Transformer with a building block-based pairwise attention bias, AtomMOF operates in an unconstrained all-atom space and exhibits clear scaling behavior. To improve the structural validity of flexible all-atom models, we also propose Feynman-Kac (FK) steering with machine-learned interatomic potentials (MLIPs). On the BW dataset, AtomMOF achieves a 58.17% relative increase in match rate and a 31.84% relative reduction in RMSD compared with prior work. MLIP-guided steering further improves validity by 28.7% and reduces formation energy error by 86.5%. On ODAC25, AtomMOF generates adsorption configurations faster than GCMC and, when combined with MLIP relaxation, identifies lower-energy configurations than those in the reference dataset.

cond-mat.mtrl-sci↗

Limitations of Debye-Waller lattice temperature extraction under electronic excitation

Ultrafast diffraction is the cutting-edge technique to extract the atomic temperature at femtosecond timescales, and further related quantities - in particular, electron-phonon coupling strength at elevated electronic temperatures. The present work demonstrates limitations of such an analysis, emphasizing the importance of careful evaluation of the evolution of the Debye temperature. It is shown that, due to the sensitivity of the Debye-Waller analysis to this parameter, neglecting its changes under electronic excitation may lead to significant deviations of the atomic temperature extracted from its true values.

cond-mat.mtrl-sci↗