End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction.

Powder X-ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse-grained level. The more difficult and important task of fine-grained crystal structure prediction from PXRD remains unaddressed.

π-HuB: the proteomic navigator of the human body.

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies.

Entropy in catalyst dynamics under confinement.

Entropy during the dynamic structural evolution of catalysts has a non-trivial influence on chemical reactions. Confinement significantly affects the catalyst dynamics and thus impacts the reactivity. However, a full understanding has not been clearly established.

Van der Waals semiconductor InSe plastifies by martensitic transformation.

Inorganic semiconductor materials are crucial for modern technologies, but their brittleness and limited processability hinder the development of flexible, wearable, and miniaturized electronics. The recent discovery of room-temperature plasticity in some inorganic semiconductors offers a promising solution, but the deformation mechanisms remain controversial. Here, we investigate the deformation of indium selenide, a two-dimensional van der Waals semiconductor with substantial plasticity.

GenAI for Scientific Discovery in Electrochemical Energy Storage: State-of-the-Art and Perspectives from Nano- and Micro-Scale.

The transition to electric vehicles (EVs) and the increased reliance on renewable energy sources necessitate significant advancements in electrochemical energy storage systems. Fuel cells, lithium-ion batteries, and flow batteries play a key role in enhancing the efficiency and sustainability of energy usage in transportation and storage. Despite their potential, these technologies face limitations such as high costs, material scarcity, and efficiency challenges.

First-Principles Calculations and Machine Learning of Hydrogen Evolution Reaction Activity of Nonmetallic Doped β-MoC Support Pt Single-Atom Catalysts.

The most widely used catalyst for the hydrogen evolution reaction (HER) is Pt, but the high cost and low abundance of Pt need to be urgently addressed. Single-atom catalysts (SACs) have been an effective means of improving the utilization of Pt atoms. In this work, we used a nonmetal (NM = B, N, O, F, Si, P, S, Cl, As, Se, Br, Te, and I) doped β-MoC (100) C-termination surface as the support, with Pt atoms dispersed on the support surface to construct Pt@NM-MoC.

Dominant Charge Density Order in TaTe_{4}.

Electronic orders such as charge density wave (CDW) and superconductivity raise exotic physics and phenomena as evidenced in recently discovered kagome superconductors and transition metal chalcogenides. In most materials, CDW induces a weak, perturbative effect, manifested as shadow bands, minigaps, resistivity kinks, etc. Here we demonstrate a unique example-transition metal tetratellurides TaTe_{4}, in which the CDW order dominates the electronic structure and transport properties.

Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why.

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained (CG) conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build CG models in dynamical consistency (DC) with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term DC.

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy.

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain.

Structural basis of adenine nucleotides regulation and neurodegenerative pathology in ClC-3 exchanger.

The ClC-3 chloride/proton exchanger is both physiologically and pathologically critical, as it is potentiated by ATP to detect metabolic energy level and point mutations in ClC-3 lead to severe neurodegenerative diseases in human. However, why this exchanger is differentially modulated by ATP, ADP or AMP and how mutations caused gain-of-function remains largely unknow. Here we determine the high-resolution structures of dimeric wildtype ClC-3 in the apo state and in complex with ATP, ADP and AMP, and the disease-causing I607T mutant in the apo and ATP-bounded state by cryo-electron microscopy.

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.

Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning interatomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS.

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This study introduces a comprehensive asymptotic parameter database encompassing atomic multipole moments, polarizabilities, and dispersion coefficients.

Stochastic Resolution of Identity to CC2 for Large Systems: Excited State Properties.

We apply a stochastic resolution of identity approximation (sRI) to the CC2 method for the excitation energy calculations. A set of stochastic orbitals are employed to decouple the crucial 4-index electron repulsion integrals and optimize the contraction steps in CC2 response theory. The CC2 response for excitations builds upon sRI-CC2 ground-state calculations, which scales as (), where is a measure for the system size.

Spatial transition tensor of single cells.

Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space.

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre-melting of Nanoclusters.

Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts.

Ab initio generalized Langevin equation.

We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori-Zwanzig formalism, under the constraint of the fluctuation-dissipation theorem.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data.

Phase and Composition Engineering of Self-Intercalated 2D Metallic Tantalum Sulfide for Second-Harmonic Generation.

Self-intercalation in two-dimensional (2D) materials is significant, as it offers a versatile approach to modify material properties, enabling the creation of interesting functional materials, which is essential in advancing applications across various fields. Here, we define ic-2D materials as covalently bonded compounds that result from the self-intercalation of a metal into layered 2D compounds. However, precisely growing ic-2D materials with controllable phases and self-intercalation concentrations to fully exploit the applications in the ic-2D family remains a great challenge.

A Layer-by-Layer Self-Assembled Bio-Macromolecule Film for Stable Zinc Anode.

Side reactions on zinc metal (Zn) anodes are formidable issues that cause limited battery life of aqueous zinc-ion batteries (AZIBs). Here, a facile and controllable layer-by-layer (LbL) self-assembly technique is deployed to construct an ion-conductive and mechanically robust electrolyte/anode interface for stabilizing the Zn anode. The LbL film consists of two natural and biodegradable bio-macromolecules, chitosan (CS) and sodium alginate (SA).