arXiv subjects

Ming Qin

Publications and source records attributed to Ming Qin.

11 recordsLinked to original sources

arXiv2025

Geometric Control of Pairing: Universal Scaling of Superconductivity at KTaO3 Interfaces

The superconducting transition temperature Tc at KTaO3-based oxide interfaces exhibits a dramatic dependence on crystallographic orientation, yet a unifying principle has remained elusive. Here, we discover a universal linear scaling between Tc and a single geometric parameter - the angle {\theta} between the (hkl) plane and the (100) plane - across ten different orientations of LaAlO3/KTaO3 interfaces. With the exception of (100), all orientations exhibit two dimensional superconductivity, with transition temperatures Tc ranging from ~ 0.12 K to 1.9 K. This linear {\theta}-Tc scaling is robust against variations in growth temperature, device geometry, and transport configuration. By establishing geometric orientation as a direct control knob for pairing strength, our results impose a critical benchmark for microscopic theories of superconductivity in KTaO3-based systems.

cond-mat.supr-con↗

arXiv2025

Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization

Protein language models have emerged as powerful tools for sequence generation, offering substantial advantages in functional optimization and denovo design. However, these models also present significant risks of generating harmful protein sequences, such as those that enhance viral transmissibility or evade immune responses. These concerns underscore critical biosafety and ethical challenges. To address these issues, we propose a Knowledge-guided Preference Optimization (KPO) framework that integrates prior knowledge via a Protein Safety Knowledge Graph. This framework utilizes an efficient graph pruning strategy to identify preferred sequences and employs reinforcement learning to minimize the risk of generating harmful proteins. Experimental results demonstrate that KPO effectively reduces the likelihood of producing hazardous sequences while maintaining high functionality, offering a robust safety assurance framework for applying generative models in biotechnology.

cs.AI↗

arXiv2025

Covalently Integrated CNT@rGO for Superior Conductivity and Cycling Stability in Lithium-Ion Batterie

The limitations of conventional conductive agents in lithium-ion batteries, such as carbon black and graphite flakes, have driven the search for high-performance alternatives. Carbon nanotubes (CNTs) and graphene offer exceptional conductivity and lower dosage requirements, but face challenges related to high costs and complex fabrication processes. Here, we report a simple and cost-effective one-step chemical vapor deposition (CVD) method for the ultra-high yield growth (7692.31%) of CNTs on a reduced graphene oxide (rGO) substrate, forming a three-dimensional CNT@rGO composite with covalent integration. When employed as a conductive agent for lithium iron phosphate (LiFePO4) cathodes, the CNT@rGO composites significantly enhance rate performance across 1-6C rates, and demonstrate exceptional cycling stability, achieving 96.32% capacity retention after 300 cycles at 1C. The synergistic structure facilitates multiple conductive pathways, minimizes catalyst residue (0.52%), and ensures uniform dispersion, providing an effective and cost-efficient solution for next-generation battery technology. This study lays the foundation for the large-scale application of high-performance carbon conductive agents in battery technology.

physics.chem-ph↗

arXiv2025

Universally enhanced superconductivity and coexisting ferroelectricity at oxide interfaces

The coexistence of superconductivity and ferroelectricity is rare due to their conflicting requirements: superconductivity relies on free charge carriers, whereas ferroelectricity typically occurs in insulating systems. At LaAlO3/KTaO3 interfaces, we demonstrate the coexistence of two-dimensional superconductivity and ferroelectricity, enabled by the unique properties of KTaO3 as a quantum paraelectric. Systematic gating and poling experiments reveal a universal enhancement of the superconducting transition temperature (Tc) by 0.2-0.6 K and bistable transport properties, including hysteresis, strongly suggesting the existence of switchable ferroelectric polarization in the interfacial conducting layer. Hysteresis loops indicate robust ferroelectricity below 50 K. The Tc enhancement is attributed to ferroelectric polarization-induced reduction in dielectric constant, which narrows the interfacial potential well, confining carriers closer to the interface. The bistability arises from switchable ferroelectric polarization, which modulates the potential well depending on polarization direction. These findings establish a straightforward mechanism coupling ferroelectricity and superconductivity, providing a promising platform for exploring their interplay.

cond-mat.supr-con↗

arXiv2024

Advancing biomolecular understanding and design following human instructions

Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology and enzyme engineering. Recent breakthroughs in artificial intelligence have revolutionized biomolecular research, achieving remarkable accuracy in biomolecular prediction and design. However, a critical gap remains between artificial intelligence's computational capabilities and researchers' intuitive goals, particularly in using natural language to bridge complex tasks with human intentions. Large language models have shown potential to interpret human intentions, yet their application to biomolecular research remains nascent due to challenges including specialized knowledge requirements, multimodal data integration, and semantic alignment between natural language and biomolecules. To address these limitations, we present InstructBioMol, a large language model designed to bridge natural language and biomolecules through a comprehensive any-to-any alignment of natural language, molecules and proteins. This model can integrate multimodal biomolecules as the input, and enable researchers to articulate design goals in natural language, providing biomolecular outputs that meet precise biological needs. Experimental results demonstrate that InstructBioMol can understand and design biomolecules following human instructions. In particular, it can generate drug molecules with a 10% improvement in binding affinity and design enzymes that achieve an enzyme-substrate pair prediction score of 70.4. This highlights its potential to transform real-world biomolecular research. The code is available at https://github.com/HICAI-ZJU/InstructBioMol.

cs.CL↗

arXiv2024

Scientific Large Language Models: A Survey on Biological & Chemical Domains

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this paper, we endeavor to methodically delineate the concept of "scientific language", whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs.

cs.CL↗

arXiv2023

InstructProtein: Aligning Human and Protein Language via Knowledge Instruction

Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation. To achieve this, we first pre-train an LLM on both protein and natural language corpora, enabling it to comprehend individual languages. Then supervised instruction tuning is employed to facilitate the alignment of these two distinct languages. Herein, we introduce a knowledge graph-based instruction generation framework to construct a high-quality instruction dataset, addressing annotation imbalance and instruction deficits in existing protein-text corpus. In particular, the instructions inherit the structural relations between proteins and function annotations in knowledge graphs, which empowers our model to engage in the causal modeling of protein functions, akin to the chain-of-thought processes in natural languages. Extensive experiments on bidirectional protein-text generation tasks show that InstructProtein outperforms state-of-the-art LLMs by large margins. Moreover, InstructProtein serves as a pioneering step towards text-based protein function prediction and sequence design, effectively bridging the gap between protein and human language understanding.

q-bio.BM↗

arXiv2022

Molecular Contrastive Learning with Chemical Element Knowledge Graph

Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs. Our codes and data are available at https://github.com/ZJU-Fangyin/KCL.

cs.LG↗

arXiv2018

Densely Connected High Order Residual Network for Single Frame Image Super Resolution

Deep convolutional neural networks (DCNN) have been widely adopted for research on super resolution recently, however previous work focused mainly on stacking as many layers as possible in their model, in this paper, we present a new perspective regarding to image restoration problems that we can construct the neural network model reflecting the physical significance of the image restoration process, that is, embedding the a priori knowledge of image restoration directly into the structure of our neural network model, we employed a symmetric non-linear colorspace, the sigmoidal transfer, to replace traditional transfers such as, sRGB, Rec.709, which are asymmetric non-linear colorspaces, we also propose a "reuse plus patch" method to deal with super resolution of different scaling factors, our proposed methods and model show generally superior performance over previous work even though our model was only roughly trained and could still be underfitting the training set.

cs.CV↗

arXiv2014

Catalog of narrow C IV absorption lines in BOSS (II): for quasars with zem > 2.4

As the second work in a series of papers aiming to detect absorption systems in the quasar spectra of the Baryon Oscillation Spectroscopic Survey, we continue the analysis of Paper I by expanding the quasar sample to those quasars with zem>2.4. This yields a sample of 21,963 appropriate quasars to search for narrow C IV absorptions with Wr>=0.2A for both lines. There are 9708 quasars with at least one appropriate absorption system imprinted on their spectra. From these spectra, we detect 13,919 narrow Civabsorption systems whose absorption redshifts cover a range of zabs=1.8784 - 4.3704. In this paper and Paper 1, we have selected 37,241 appropriate quasars with median SNR>= 4 and 1.54<= zem <=5.16 to visually analyze narrow C IV absorption doublets one by one. A total of 15,999 quasars are found to have at least one appropriate absorption system imprinted on their spectra. From these 15,999 quasar spectra, we have detected 23,336 appropriate C IV absorption systems with Wr>=0.2A whose absorption redshifts cover a range of zabs=1.4544 - 4.3704. The largest values of Wr are 3.19 A for the \lambda1548 absorption line and 2.93 A for the lambda1551 absorption line, respectively. We find that only a few absorbers show large values of Wr. About 1.1% absorbers of the total absorbers have Wr>=2.0 A.

astro-ph.GA↗

arXiv2013

Catalog of narrow $C~IV$ absorption lines in BOSS (I): for quasars with $z_{em} \leq 2.4$

We have assembled absorption systems by visually identifying $C~IVλ\lambda1548,1551$ absorption doublets in the quasar spectra of the Baryon Oscillation Spectroscopic Survey (BOSS) one by one. This paper is the first of the series work. In this paper, we concern quasars with relatively low redshifts and high signal-to-noise ratios for their spectra, and hence we limit our analysis on quasars with $z_{em}\le2.4$ and on the doublets with $W_r\lambda1548\ge0.2$ Å. Out of the more than 87,000 quasars in the Data Release 9, we limit our search to 10,121 quasars that have the appropriate redshifts and spectra with high enough signal-to-noise ratios to identify narrow C IV absorption lines. Among them, 5,442 quasars are detected to have at least one $C~IVλ\lambda1548,1551$ absorption doublet. We obtain a catalog containing 8,368 $C~IVλ\lambda1548,1551$ absorption systems, whose redshifts are within $z_{abs}=1.4544$ - $2.2805$. In this catalog, about $33.7\%$ absorbers have $0.2$ Å$\le W_r\lambda1548<0.5$ Å, about $45.9\%$ absorbers have $0.5$ Å$\le W_r\lambda1548<1.0$ Å, about $19.2\%$ absorbers have $1.0$ Å$\le W_r\lambda1548<2.0$ Å, and about $1.2\%$ absorbers have $W_r\lambda1548\ge2.0$ Å.

astro-ph.CO↗