Jinjin Li

Bio: Jinjin Li is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Battery (electricity) & Quantum dot. The author has an hindex of 17, co-authored 134 publications receiving 1242 citations. Previous affiliations of Jinjin Li include University of California, Santa Barbara & University of Illinois at Urbana–Champaign.

Surface-Electronic-State-Modulated, Single-Crystalline (001) TiO2 Nanosheets for Sensitive Electrochemical Sensing of Heavy-Metal Ions.

Abstract: Intrinsically low conductivity and poor reactivity restrict many semiconductors from electrochemical detection. Usually, metal- and carbon-based modifications of semiconductors are necessary, making them complex, expensive, and unstable. Here, for the first time, we present a surface-electronic-state-modulation-based concept applied to semiconductors. This concept enables pure semiconductors to be directly available for ultrasensitive electrochemical detection of heavy-metal ions without any modifications. As an example, a defective single-crystalline (001) TiO2 nanosheet exhibits high electrochemical performance toward Hg(II), including a sensitivity of 270.83 μA μM–1 cm–2 and a detection limit of 0.017 μM, which is lower than the safety standard (0.03 μM) of drinking water established by the World Health Organization (WHO). It has been confirmed that the surface oxygen vacancy adsorbs an O2 molecule while the Ti3+ donates an electron, forming the O2•– species that facilitate adsorption of Hg(II) and ser...

Ab initio molecular crystal structures, spectra, and phase diagrams.

Abstract: Conspectus Molecular crystals are chemists' solids in the sense that their structures and properties can be understood in terms of those of the constituent molecules merely perturbed by a crystalline environment. They form a large and important class of solids including ices of atmospheric species, drugs, explosives, and even some organic optoelectronic materials and supramolecular assemblies. Recently, surprisingly simple yet extremely efficient, versatile, easily implemented, and systematically accurate electronic structure methods for molecular crystals have been developed. The methods, collectively referred to as the embedded-fragment scheme, divide a crystal into monomers and overlapping dimers and apply modern molecular electronic structure methods and software to these fragments of the crystal that are embedded in a self-consistently determined crystalline electrostatic field. They enable facile applications of accurate but otherwise prohibitively expensive ab initio molecular orbital theories such as Moller-Plesset perturbation and coupled-cluster theories to a broad range of properties of solids such as internal energies, enthalpies, structures, equation of state, phonon dispersion curves and density of states, infrared and Raman spectra (including band intensities and sometimes anharmonic effects), inelastic neutron scattering spectra, heat capacities, Gibbs energies, and phase diagrams, while accounting for many-body electrostatic (namely, induction or polarization) effects as well as two-body exchange and dispersion interactions from first principles. They can fundamentally alter the role of computing in the studies of molecular crystals in the same way ab initio molecular orbital theories have transformed research practices in gas-phase physical chemistry and synthetic chemistry in the last half century. In this Account, after a brief summary of formalisms and algorithms, we discuss applications of these methods performed in our group as compelling illustrations of their unprecedented power in addressing some of the outstanding problems of solid-state chemistry, high-pressure chemistry, or geochemistry. They are the structure and spectra of ice Ih, in particular, the origin of two peaks in the hydrogen-bond-stretching region of its inelastic neutron scattering spectra, a solid-solid phase transition from CO2-I to elusive, metastable CO2-III, pressure tuning of Fermi resonance in solid CO2, and the structure and spectra of solid formic acid, all at the level of second-order Moller-Plesset perturbation theory or higher.

Three-dimensional graphene-based nanocomposites for high energy density Li-ion batteries

Abstract: High energy density Li-ion batteries have attracted broad attention due to their great significance for various applications ranging from portable electronics to electric vehicles. However, emerging applications require batteries with greater than currently available energy densities, which have motivated numerous research efforts such as investigations on high energy density active materials, and engineered electrode structures that maximize the capacity. Three-dimensional (3D) graphene provides promising pathways for developing high energy density electrodes including cathodes and anodes, because of its potential for providing a conductive 3D network, improving Li+ ion and electron transfer, as well as accommodating the structure and volume change during cycling. In this review, recent research efforts particularly focused on 3D graphene-based nanocomposite electrodes which exhibit high energy density, high capacity, and good rate performance have been summarized comprehensively. The current challenges for high energy density Li-ion batteries have been discussed, while the potential research perspectives have been presented as well, which we hope would inspire high-performance battery investigations.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Progress in optics

Abstract: Have you ever felt that the very title, Progress in Optics, conjured an image in your mind? Don’t you see a row of handsomely printed books, bearing the editorial stamp of one of the most brilliant members of the optics community, and chronicling the field of optics since the invention of the laser? If so, you are certain to move the bookend to make room for Volume 16, the latest of this series. It contains seven chapters on widely diverging topics, written by well-known authorities in their fields. These are: 1) Laser Selective Photophysics and Photochemistry by V. S. Letokhov, 2) Recent Advances in Phase Profiles (sic) Generation by J. J. Clair and C. I. Abitbol, 3 ) Computer-Generated Holograms: Techniques and Applications by W.-H. Lee, 4) Speckle Interferometry by A. E. Ennos, 5 ) Deformation Invariant, Space-Variant Optical Pattern Recognition by D. Casasent and D. Psaltis, 6) Light Emission from High-Current Surface-Spark Discharges by R. E. Beverly, and 7) Semiclassical Radiation Theory within a QuantumMechanical Framework by I. R. Senitzkt. The breadth of topic matter spanned by these chapters makes it impossible, for this reviewer at least, to pass judgement on the comprehensiveness, relevance, and completeness of every chapter. With an editorial board as prominent as that of Progress in Optics, however, it seems hardly likely that such comments should be necessary. It should certainly be possible to take the authority of each author as credible. The only remaining judgment to be made on these chapters is their readability. In short, what are they like to read? The first sentence of the first chapter greets the eye with an obvious typographical error: “The creation of coherent laser light source, that have tunable radiation, opened the . . . .” Two pages later we find: “When two types of atoms or molecules of different isotopic composition ( A and B ) have even one spectral line that does not overlap with others, it is pos-

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

Abstract: Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein-ligand binding, protein-protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields.

Quantum-Chemical Insights from Deep Tensor Neural Networks

Abstract: Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

Abstract: The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost.