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arXiv 2024年

作者： Wang, Dedi Tiwary, Pratyush Biophysics Program and Institute for Physical Science and Technology University of Maryland College Park20742 United States Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College Park20742 United States University of Maryland Institute for Health Computing Bethesda20852 United States

The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional collective variable (CV) space and then partitioning it into bins. The efficacy of WE simulations heavily depends on the selection of CVs and binning schemes. The recently proposed State Predictive Information Bottleneck (SPIB) method has emerged as a promising tool for automatically constructing CVs from data and guiding enhanced sampling through an iterative manner. In this work, we advance this data-driven pipeline by incorporating prior expert knowledge. Our hybrid approach combines SPIB-learned CVs to enhance sampling in explored regions with expert-based CVs to guide exploration in regions of interest, synergizing the strengths of both methods. Through benchmarking on alanine dipeptide and chignoin systems, we demonstrate that our hybrid approach effectively guides WE simulations to sample states of interest, and reduces run-to-run variances. Moreover, our integration of the SPIB model also enhances the analysis and interpretation of WE simulation data by effectively identifying metastable states and pathways, and offering direct visualization of dynamics. © 2024, CC BY.

关键词： Deep learning

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Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena

arXiv

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arXiv 2024年

作者： Tiwary, Pratyush Herron, Lukas John, Richard Lee, Suemin Sanwal, Disha Wang, Ruiyu Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College Park20742 United States University of Maryland Institute for Health Computing Bethesda20852 United States Biophysics Program Institute for Physical Science and Technology University of Maryland College Park20742 United States Department of Physics Institute for Physical Science and Technology University of Maryland College Park20742 United States

The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress in sampling molecular structures across chemical species, developing force fields, and speeding up simulations. This Perspective offers a structured overview, beginning with the fundamental theoretical concepts in both Generative AI and computational chemistry. It then covers widely used Generative AI methods, including autoencoders, generative adversarial networks, reinforcement learning, flow models and language models, and highlights their selected applications in diverse areas including force field development, and protein/RNA structure prediction. A key focus is on the challenges these methods face before they become truly predictive, particularly in predicting emergent chemical phenomena. We believe that the ultimate goal of a simulation method or theory is to predict phenomena not seen before, and that Generative AI should be subject to these same standards before it is deemed useful for chemistry. We suggest that to overcome these challenges, future AI models need to integrate core chemical principles, especially from statistical mechanics. © 2024, CC BY.

关键词： Generative adversarial networks

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Exploring kinase DFG loop conformational stability with AlphaFold2-RAVE

arXiv

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arXiv 2023年

作者： Vani, Bodhi P. Aranganathan, Akashnathan Tiwary, Pratyush Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Biophysics Program Institute for Physical Science and Technology University of Maryland College Park20742 United States Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College Park20742 United States

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular cancers. The ubiquitousness and structural similarities of kinases makes specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved DFG motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence, and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learnt order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations. © 2023, CC BY.

关键词： Enzymes

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A survey of probabilistic generative frameworks for molecular simulations

arXiv

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arXiv 2024年

作者： John, Richard Herron, Lukas Tiwary, Pratyush Department of Physics Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Biophysics Program Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States University of Maryland Institute for Health Computing Bethesda20852 United States Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. In this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: Neural Spline Flows, Conditional Flow Matching, and Denoising Diffusion Probabilistic Models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) Neural Spline Flows do best at capturing mode asymmetry present in low-dimensional data, (ii) Conditional Flow Matching outperforms other models for high-dimensional data with low complexity, and (iii) Denoising Diffusion Probabilistic Models appears the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks. © 2024, CC BY.

关键词： Molecular docking

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Self-energy corrections to zone-edge acoustic phonons in monolayer and bilayer WS2

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physical Review B 2024年第12期109卷 L121202-L121202页

作者： Qixing Wang Yu Li Huang Qi Zhang Jiayu Ma Xin Luo Yun-Mei Li Department of Physics College of Physical Science and Technology Xiamen University Xiamen 361005 China Joint School of National University of Singapore and Tianjin University International Joint Institute of Tianjin University Fuzhou 350207 China School of Physics and Key Laboratory of MEMS of the Ministry of Education Southeast University Nanjing 211189 China State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices Centre for Physical Mechanics and Biophysics School of Physics Sun Yat-sen University Guangzhou 510275 China

The self-energy corrections to phonons have mostly been observed for modes with zone-center (q=0) wave vectors. Here, we investigated the self-energy corrections to phonons in ionic-liquid-gated semiconductors, i.e., monolayer and bilayer WS2, experimentally. Apart from the previously observed A1g(Γ) mode renormalization by Raman spectroscopy, we additionally discovered that the zone-edge longitudinal acoustic (LA(M)) phonon also softened under finite electron doping. This observed renormalization effect arises from the LA(M) phonon-assisted electron scattering between the K and Q valleys in the conduction bands, indicating the nonadiabatic self-energy corrections dominate, different from the A1g(Γ) mode. The self-energy corrections of the two modes both depend on layer configurations. Our findings suggest that WS2 and other similar two-dimensional materials are intriguing playgrounds to study the electron-phonon interactions and valley-related physics.

关键词： Acoustic phonons Electron-phonon coupling Layered crystals Semiconductors Wide band gap systems Density functional theory Raman spectroscopy

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Thermal energy dependent transient permittivity of epsilon-near-zero material

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science China(Physics,Mechanics & Astronomy) 2022年第8期65卷 96-101页

作者： Heng Wang Lixun Sun Kang Du Wending Zhang SooJin Chua Guixin Li Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an710129China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore LEES Program Singapore-MIT Alliance for Research&Technology(SMART)Singapore138602Singapore

Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of conduction electrons. The optical nonlinearity of ENZ materials has been explained by using the overall-effective-mass and the overall-scattering-time of electrons in the extended Drude model. However, their response to optical excitation is yet the last building block to complete the theory. In this paper, the concept of thermal energy is theoretically proposed to account for the total energy of conduction electrons exceeding their thermal equilibrium value. The time-varying thermal energy is adopted to describe the transient optical response of indium-tin-oxide(ITO), a typical ENZ material. A spectrally-resolved femtosecond pump-probe experiment was conducted to verify our theory. By correlating the thermal energy with the pumping density, both the giant change and the transient response of the permittivity of ITO can be predicted. The results in this work provide a new methodology to describe the transient permittivities of ENZ materials, which will benefit the design of ENZ-based nonlinear photonic devices.

关键词： epsilon-near-zero optical nonlinearity transient response thermal energy hot electron indium tin oxide

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Chromatograms and Mass Spectra of High-Mannose and Paucimannose N-Glycans for Rapid Isomeric Identifications

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Journal of Proteome Research 2024年第3期23卷 939-955页

作者： Liew, Chia Yen Chen, Jien-Lian Lin, Yen-Ting Luo, Hong-Sheng Hung, An-Ti Magoling, Bryan John Abel Nguan, Hock-Seng Lai, Charles Pin-Kuang Ni, Chi-Kung Institute of Atomic and Molecular Sciences Academia Sinica Taipei 106216 Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei 106216 Taiwan Molecular Science and Technology Taiwan International Graduate Program Academia Sinica Taipei 106216 Taiwan Department of Chemistry National Taiwan Normal University Taipei 116059 Taiwan Department of Chemistry National Tsing Hua University Hsinchu 300044 Taiwan Institute of Biochemical Sciences College of Life Science National Taiwan University Taipei 106216 Taiwan Chemical Biology and Molecular Biophysics Program Taiwan International Graduate Program Academia Sinica Taipei 115201 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei 106216 Taiwan

N-Linked glycosylation is one of the most essential post-translational modifications of proteins. However, N-glycan structural determination remains challenging because of the small differences in structures between isomers. In this study, we constructed a database containing collision-induced dissociation MSⁿ mass spectra and chromatograms of high-performance liquid chromatography for the rapid identification of high-mannose and paucimannose N-glycan isomers. These N-glycans include isomers by breaking of arbitrary numbers of glycosidic bonds at arbitrary positions of canonical Man₉GlcNAc₂ N-glycans. In addition, some GlcMan_nGlcNAc₂ N-glycan isomers were included in the database. This database is particularly useful for the identification of the N-glycans not in conventional N-glycan standards. This study demonstrated the application of the database to structural assignment for high-mannose N-glycans extracted from bovine whey proteins, soybean proteins, human mammary epithelial cells, and human breast carcinoma cells. We found many N-glycans that are not expected to be generated by conventional biosynthetic pathways of multicellular eukaryotes. © 2024 The Authors. Published by American Chemical Society.

关键词： bovine whey protein chromatogram CID spectra high-mannose N-glycans human breast carcinoma cell human mammary epithelial cell LODES/MSn logically derived sequence tandem mass spectrometry soybean protein structure

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Negative PO3-implanted bionic ion transport channels for promoting Li-S battery

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science China Chemistry 2025年

作者： Wenhuan Huang Shun Wang Xuehan Hou Yanan Zhang Xingxing Zhang Yaoxiao Zhao Xilang Jin Huabin Zhang Fei Wang Jian Zhang Key Laboratory of Chemical Additives for China National Light Industry College of Chemistry and Chemical EngineeringShaanxi University of Science and Technology School of Materials and Chemical Engineering Xi'an Technological University Chemistry Program Physical Science and Engineering Division King Abdullah University of Science and Technology State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences

Lithium-sulfur （Li-S） battery,as a promising next-generation high-energy-density battery,suffers from lithium polysulfides（LPSs） shuttle and Li dendrites *** crystalline framework-based membranes as separator can effectively absorb and restrain LPSs but without precise structural design principal exploration and mechanism ***,bio-inspired 1D oriented lithium-ion transport channels with molecular negatively charged PO3（-1） have been developed in hybrid tetrazole frameworks for promoting the transmission of Li+*** theoretical calculations and in situ spectroscopy demonstrated the higher binding energy and inhibited diffusion of LPSs in *** assembled PP@PO3Janus separator in Li-S coin cell delivered a high initial capacity of 1410.9 mAh g-1at 0.1 C and a low attenuation rate of 0.038%per cycle over 500 cycles at 5 ***,the high capacities of 862,542 and 409 mAh g-1based on high-sulfur-loading cathodes of 2.6,3.8 and 5.0 mg cm-2at0.5 C were achieved,***,the punch battery of PO3@PP separator with S-cathode of 1.2 mg cm-2has been developed for demonstrating its potentials commercial application,which displays impressive capacity of 873.4 mAh g-1with the retention of～78.9%over 50 cycles.

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Concentrated ternary ether electrolyte allows for stable cycling of a lithium metal battery with commercial mass loading high-nickel NMC and thin anodes

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Carbon Energy 2023年第3期5卷 2-18页

作者： Jun Yang Xing Li Ke Qu Yixian Wang Kangqi Shen Changhuan Jiang Bo Yu Pan Luo Zhuangzhi Li Mingyang Chen Bingshu Guo Mingshan Wang Junchen Chen Zhiyuan Ma Yun Huang Zhenzhong Yang Pengcheng Liu Rong Huang Xiaodi Ren David Mitlin School of New Energy and Materials Southwest Petroleum UniversityChengduChina Energy Storage Research Institute Southwest Petroleum UniversityChengduChina Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina China Key Laboratory of Polar Materials and Devices(MOE) Department of ElectronicsEast China Normal UniversityShanghaiChina Materials Science Program and Texas Materials Institute The University of Texas at AustinAustinTexasUSA Beijing Computational Science Research Center BeijingChina School of Materials Science and Engineering Center for Green Innovation University of Science and Technology BeijingBeijingChina Department of Materials Science and Engineering Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefeiAnhuiChina

A new concentrated ternary salt ether-based electrolyte enables stable cycling of lithium metal battery(LMB)cells with high-mass-loading(13.8 mg cm^(−2),2.5 mAh cm^(−2))NMC622(LiNi_(0.6)Co_(0.2)Mn_(0.2)O_(2))cathodes and 50μm Li ***“CETHER-3,”this electrolyte is based on LiTFSI,LiDFOB,and LiBF4 with 5 vol%fluorinated ethylene carbonate in 1,***-cial carbonate and state-of-the-art binary salt ether electrolytes were also tested as *** CETHER-3,the electrochemical performance of the full-cell battery is among the most favorably reported in terms of high-voltage cycling *** example,LiNi_(x)Mn_(y)Co_(1-x-y)O_(2)(NMC)-Li metal cells retain 80%capacity at 430 cycles with a 4.4 V cut-off and 83%capacity at 100 cycles with a 4.5 V cut-off(charge at C/5,discharge at C/2).According to simulation by density functional theory and molecular dynamics,this favorable performance is an outcome of enhanced coordination between Li^(+)and the solvent/salt *** advanced microscopy(high-resolution transmission electron microscopy,scanning electron microscopy)and surface science(X-ray photoelectron spectroscopy,time-of-fight secondary ion mass spectroscopy,Fourier-transform infrared spectroscopy,Raman spectroscopy),it is demonstrated that a thinner and more stable cathode electrolyte interphase(CEI)and solid electrolyte interphase(SEI)are *** CEI is rich in lithium sulfide(Li_(2)SO_(3)),while the SEI is rich in Li_(3)N and *** cycling,the CEI/SEI suppresses both the deleterious transformation of the cathode R-3m layered near-surface structure into disordered rock salt and the growth of lithium metal dendrites.

关键词： concentrated electrolyte density functional theory ether electrolyte high‐nickel cathode high‐voltage battery molecular dynamics

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Bifunctional Supercapacitor and Photocatalytic Properties of Cuboid Ni-TMA MOF Synthesized Using a Facile Hydrothermal Approach

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Journal of Electronic Materials 2024年第1期53卷 16-29页

作者： Narwade, Vijaykiran N. Rahane, Ganesh K. Bogle, Kashinath A. Tsai, Meng-Lin Rondiya, Sachin R. Shirsat, Mahendra D. RUSA Centre for Advanced Sensor Technology Dr. Babasaheb Ambedkar Marathwada University Maharashtra Aurangabad431 004 India Department of Materials Engineering Indian Institute of Science Bangalore560 012 India School of Physical Sciences Swami Ramanand Teerth Marathwada University Maharashtra Nanded431 606 India Department of Materials Science and Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Nuclear Physics and Biophysics Faculty of Mathematics Physics and Informatics Comenius University Bratislava842 48 Slovakia

Metal–organic frameworks (MOFs) with a substantial surface area and configurable porosity have gained the interest of researchers in energy storage devices and environmental management. Our investigation efficaciously employs a cuboid-shaped nickel and trimesic acid metal–organic framework (Ni-TMA MOF) as a bifunctional high-performance supercapacitor electrode as well as an efficient photocatalyst. The Ni-cuboid-like MOF's structure provides more electroactive sites along with a shorter pathway for electron transfer and electrolyte diffusion, resulting in a high specific capacitance of 236 F g−1 at 1 A g−1. The fabricated electrode exhibits particularly high capacitance retention, with over 98% retention even after 1000 cycles. The photocatalytic activity of the Ni-TMA MOF for the degradation of rhodamine 6G dye was investigated, which revealed that 90% of the dye was degraded in nearly 40 min, which follows the modified Freundlich process. The development of multifunctional materials to address emerging problems is of paramount importance, and this work ensures this possibility, with results showing improved energy storage and photocatalytic capability. Graphical Abstract: [Figure not available: see fulltext.] © 2023, The Minerals, Metals & Materials Society.

关键词： Supercapacitor

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