arXiv subjects

Tenglong Lu

Publications and source records attributed to Tenglong Lu.

12 recordsLinked to original sources

arXiv2026

Aligning Heterogeneous DFT Datasets: A Graph Neural Network Approach to Cross-Functional Formation Energies

Heterogeneous density functional theory (DFT) calculations, particularly plane-wave implementations, introduce systematic formation energy errors ranging from tens to hundreds of meV/atom, depending on the selection of exchange-correlation functionals, kinetic energy cutoffs, pseudopotentials, and dispersion corrections. As demonstrated by the MatPES dataset, identical structures can exhibit an average energy discrepancy of 107 meV/atom between PBE and r2SCAN calculations. Such method-dependent discrepancies hinder the integration of multi-source DFT data, greatly limiting the scale and quality of datasets for training robust materials AI models. Here, we resolve this fundamental data silo barrier via graph-based transfer learning. Leveraging 380,190 structurally paired PBE-r2SCAN entries from the MatPES database, we train a structure-aware graph neural network to predict cross-functional energy residuals and align inconsistent DFT energy scales. By adopting GPTFF model architecture, the model converts conventional PBE energies to r2SCAN-level accuracy with a mean absolute error of 14.3 meV/atom, compared with 18.2 meV/atom achieved by CHGNet. This versatile approach effectively upgrades massive legacy PBE datasets to high-precision r2SCAN standards. It enables reliable predictions of phase stability, battery voltage profiles, and reaction thermodynamics, while allowing the integration of multi-source DFT data to advance the development of high-performance materials foundation models.

cond-mat.mtrl-sci↗

arXiv2026

Graph Neural Network Force Fields (GPTFF-mol) for Organic Molecules from Optimization Trajectories (OpenGEM26)

Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accuracy and efficiency. In this work, we release OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms. All calculations are carried out at the {\omega}B97X-D/Def2-SVP and Def2-TZVP levels with dispersion corrections, and complete structural optimization trajectories and abundant non-equilibrium structures are recorded. Statistical analyses confirm that this dataset covers a broader conformational space than QM9 in terms of energy, bond lengths and bond angles. A graph neural network-based potential GPTFF-mol is trained using the new dataset, achieving an energy mean absolute error of 16 meV/molecule, which is equivalent to 0.82meV/atom, and superior force prediction performance compared with ANI-2x. Validated by butane rotation and keto-enol tautomerization tests, the model accurately describes molecular dynamical behaviors and reaction barriers at distorted geometries. This work provides a high-quality resource and robust ML potential for efficient simulations of sulfur- and chlorine-containing organic molecules.

physics.chem-ph↗

arXiv2026

Are Machine Learning Interatomic Potentials Truly Practical? A Benchmark of 23 Mainstream Models

Most MLIP benchmarks reward static accuracy while ignoring inference efficiency and hardware scalability -- driving model bloat with unclear real-world value. We benchmark 23 mainstream open-source MLIPs on a low-cost NVIDIA DGX Spark (128 GB native memory, capped at 80 GB to mimic ordinary lab hardware), using a fixed 192-atom system under a unified ASE-based pipeline. We evaluate three dimensions: predictive accuracy, MD simulation throughput, and atomic scalability. Our results expose a sharp accuracy-efficiency trade-off: large SOTA models deliver only 3-5 meV/atom more accuracy than lightweight ones, but lose orders of magnitude in throughput -- in the worst case, becoming only marginally faster than DFT itself. Lightweight MLIPs, by contrast, sit on the Pareto frontier and run on modest hardware. The lesson is that single-dimensional benchmarks mislead the field, and that future MLIP development should value efficiency and scalability alongside accuracy.

cond-mat.mtrl-sci↗

arXiv2025

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential

We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy surface sampling and interpolation. Our method suppresses periodic self interactions via supercell expansion, builds a continuous PES from MLFF energies on a spatial grid, and extracts minimum energy pathways without predefined NEB images. Across twelve benchmark electrode and electrolyte materials including LiCoO2, LiFePO4, and LGPS our MLFF-derived barriers lie within tens of meV of DFT and experiment, while achieving ~10^2 x speedups over DFT-NEB. We benchmark GPTFF, CHGNet, and MACE, show that fine-tuning on PBE/PBE+U data further enhances accuracy, and provide an open-source package for high-throughput materials screening and interactive PES visualization.

cond-mat.mtrl-sci↗

arXiv2024

Imaging the Meissner effect in pressurized bilayer nickelate with integrated multi-parameter quantum sensor

Recent reports on the signatures of high-temperature superconductivity with a critical temperature Tc close to 80 K have triggered great research interest and extensive follow-up studies. Although the zero resistance has been successfully achieved under improved hydrostatic pressure conditions, the Meissner effect of $\mathrm{La_{3}Ni_{2}O_{7-\delta}}$ under high pressure remains controversial. Here, using shallow nitrogen-vacancy centers implanted on the culet of diamond anvils as in-situ quantum sensors, we observe compelling evidence for the Meissner effect in polycrystalline bilayer nickelate samples: the magnetic field expulsion during both field cooling and field warming processes. In particular, we explore the multiparameter measurement capacity of the diamond quantum sensors to extract the weak demagnetization signal of $\mathrm{La_{3}Ni_{2}O_{7-\delta}}$. The correlated measurements of Raman spectra and magnetic imaging indicate an incomplete structural transformation related to the displacement of oxygen ions emerging in the non-superconducting region. Our work clarifies the controversy about the Meissner effect of $\mathrm{La_{3}Ni_{2}O_{7-\delta}}$ and contributes to the development of quantum sensing of weak signals under high-pressure conditions.

cond-mat.supr-con↗

arXiv2024

Chemical versus physical pressure effects on the structure transition of bilayer nickelates

The observation of high-$T_c$ superconductivity (HTSC) in concomitant with pressure-induced orthorhombic-tetragonal structural transition in the bilayer La$_{3}$Ni$_2$O$_7$ has sparked hopes of achieving HTSC by stabilizing the tetragonal phase at ambient pressure. To mimic the effect of external physical pressures, the application of chemical pressure via replacing La$^3$$^+$ with smaller rare-earth R$^3$$^+$ has been considered as a potential route. Here we clarify the distinct effects of chemical and physical pressures on the structural transition of bilayer nickelates through a combined experimental and theoretical investigation. Contrary to general expectations, we find that substitutions of smaller R$^3$$^+$ for La$^3$$^+$ in La$_{3-x}$R$_x$Ni$_2$O$_{7-\delta}$, despite of an overall lattice contraction, produce stronger orthorhombic structural distortions and thus require higher pressures to induce the structural transition. We established a quantitative relationship between the critical pressure $P_c$ for structural transition and the average size of $A$-site cations. A linear extrapolation of $P_c$ versus <$r_A$> yields a putative critical value of <$r_A$>$_c$ ~ 1.23 angstrom for $P_c$ ~ 1 bar. The negative correlation between $P_c$ and <$r_A$> indicates that it is unlikely to reduce $P_c$ to ambient by replacing La$^3$$^+$ with smaller R$^3$$^+$ ions. Instead, partial substitution of La$^3$$^+$ with larger cations such as alkaline-earth Sr$^2$$^+$ or Ba$^2$$^+$ might be a feasible approach. Our results provide valuable guidelines in the quest of ambient-pressure HTSC in bilayer nickelates.

cond-mat.str-el↗

arXiv2024

GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials

This study introduces a novel AI force field, namely graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and generalizability. Harnessing a large trove of the data and the attention mechanism of transformer algorithms, the model can accurately predict energy, atomic forces, and stress with Mean Absolute Error (MAE) values of 32 meV/atom, 71 meV/{\AA}, and 0.365 GPa, respectively. The dataset used to train the model includes 37.8 million single-point energies, 11.7 billion force pairs, and 340.2 million stresses. We also demonstrated that GPTFF can be universally used to simulate various physical systems, such as crystal structure optimization, phase transition simulations, and mass transport.

cond-mat.mtrl-sci↗

arXiv2023

Weberite Na$_2$MM'F$_7$ (M,M'=Redox-Active Metal) as Promising Fluoride-Based Sodium-Ion Battery Cathodes

Sodium-ion batteries are a viable alternative to lithium-ion technology due to the plentiful sodium resources. However, certain commercialization challenges, such as low specific energies and poor cycling performance of current Na-ion cathodes, still need to be addressed. To overcome these hurdles, this study explored the potential of a novel class of fluoride-based materials, specifically trigonal-type Na$_2$MM'F$_7$ (M and M' are redox-active metals) belonging to the weberite-type compounds, as promising candidates for Na-ion cathodes. Through a comprehensive assessment utilizing ab initio calculations, twelve prospective compounds were identified, demonstrating high thermodynamic stability, large gravimetric capacities (>170 mAh/g), and low net Na-ion migration barriers (<600 meV). Significantly, ten out of the twelve screened compounds exhibit high specific energies exceeding 580 Wh/kg (approximately equals to the specific energy of LiFePO$_4$), indicating their exceptional electrochemical performance. This study will pave the way for further advancements in fluoride-based electrode materials.

physics.chem-ph↗

arXiv2023

Electron-phonon interactions in LuH$_2$, LuH$_3$, and LuN

This paper presents the calculation results of electron-phonon interactions within the LuH$_2$, LuH$_3$, and LuN systems under 0 GPa and 10 GPa via density functional theory at the GGA-PBE level. The purpose of this work is to provide useful data that may be of the interests of the superconducting community as it was reported that the Lu-H-N compound is likely to be a room-temperature superconductor under 1 GPa [Nature, 615, 244 (2023)].

cond-mat.supr-con↗

arXiv2023

Lu-H-N phase diagram from first-principles calculations

Using a comprehensive structure search and high-throughput first-principles calculations of 1483 compounds, this study presents the phase diagram of Lu-H-N. The formation energy landscape of Lu-H-N was derived and utilized to assess the thermodynamic stability of compounds. Results indicate that there are no stable Lu-H-N ternary structures in this system, but metastable ternary structures, such as Lu20H2N17 (C2/m), Lu2H2N (P3-m1), were observed with small Ehull (< 100 meV/atom). Moreover, applying hydrostatic pressure up to 10 GPa causes the energy convex hull of the Lu-H-N to shift its shape and stabilizes binary phases such as LuN9 and Lu10H21. Additionally, interstitial empty sites in LuH2 were noted, which may explain the formation of Lu10H21 and LuH3-xNy. To provide a basis for comparison, X-ray diffraction patterns and electronic structures of some compounds are also presented.

cond-mat.supr-con↗

arXiv2022

Screening promising CsV3Sb5-like kagome materials from systematic first-principles evaluation

CsV3Sb5 kagome lattice holds the promise for manifesting electron correlation, topology and superconducting. However, by far only three CsV3Sb5-like kagome materials have been experimentally spotted. In this work, we enlarge this family of materials to 1386 compounds via element species substitution, and the further screening process suggests that 28 promising candidates have superior thermodynamic stability, hence they are highly likely to be synthesized. Moreover, these compounds possess several identical electronic structures, and can be categorized into five non-magnetic and three magnetic groups accordingly. It is our hope that this work can greatly expand the viable phase space of the CsV3Sb5-like materials for investigating or tuning the novel quantum phenomena in kagome lattice.

cond-mat.mtrl-sci↗

arXiv2021

A universal model for the formation energy prediction of inorganic compounds

Harnessing the recent advance in data science and materials science, it is feasible today to build predictive models for materials properties. In this study, we employ the data of high-throughput quantum mechanics calculations based on 170,714 inorganic crystalline compounds to train a machine learning model for formation energy prediction. Different from the previous work, our model reaches a fairly good predictive ability (R2=0.982 and MAE=0.07 eVatom-1, DenseNet model) and meanwhile can be universally applied to the large phase space of inorganic materials. The improvement comes from several effective structure-dependent descriptors that are proposed to take the information of electronegativity and structure into account. This model can provide a useful tool to search for new materials in a vast phase space in a fast and cost-effective manner.

cond-mat.mtrl-sci↗