S. Nagel, Srikanth Sastry, Z. Zeravcic, M. Muthukumar
{"title":"Memory formation.","authors":"S. Nagel, Srikanth Sastry, Z. Zeravcic, M. Muthukumar","doi":"10.1007/978-3-540-29807-6_4344","DOIUrl":"https://doi.org/10.1007/978-3-540-29807-6_4344","url":null,"abstract":"","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115276514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Pellegrini, Ruggero Lot, Yusuf Shaidu, E. Küçükbenli
{"title":"PANNA 2.0: Efficient neural network interatomic potentials and new architectures","authors":"F. Pellegrini, Ruggero Lot, Yusuf Shaidu, E. Küçükbenli","doi":"10.48550/arXiv.2305.11805","DOIUrl":"https://doi.org/10.48550/arXiv.2305.11805","url":null,"abstract":"We present the latest release of PANNA 2.0 (Properties from Artificial Neural Network Architectures), a code for the generation of neural network interatomic potentials based on local atomic descriptors and multilayer perceptrons. Built on a new back end, this new release of PANNA features improved tools for customizing and monitoring network training, better graphics processing unit support including a fast descriptor calculator, new plugins for external codes, and a new architecture for the inclusion of long-range electrostatic interactions through a variational charge equilibration scheme. We present an overview of the main features of the new code, and several benchmarks comparing the accuracy of PANNA models to the state of the art, on commonly used benchmarks as well as richer datasets.","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"159 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131213526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Stress and heat flux via automatic differentiation","authors":"Marcel F. Langer, J. Frank, Florian Knoop","doi":"10.48550/arXiv.2305.01401","DOIUrl":"https://doi.org/10.48550/arXiv.2305.01401","url":null,"abstract":"Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface. This potential determines many materials properties and simulation techniques usually require its gradients, in particular forces and stress for molecular dynamics, and heat flux for thermal transport properties. Recently developed potentials feature high body order and can include equivariant semi-local interactions through message-passing mechanisms. Due to their complex functional forms, they rely on automatic differentiation (AD), overcoming the need for manual implementations or finite-difference schemes to evaluate gradients. This study discusses how to use AD to efficiently obtain forces, stress, and heat flux for such potentials, and provides a model-independent implementation. The method is tested on the Lennard-Jones potential, and then applied to predict cohesive properties and thermal conductivity of tin selenide using an equivariant message-passing neural network potential.","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127284513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. I. Menéndez, Nicolás Montenegro-Pohlhammer, Ricardo Pino‐Rios, Rodrigo Urzúa-Leiva, Simone Morales-Lovera, Merlys Borges-Martínez, Kevin Granados-Tavera, R. López, G. Cárdenas-Jirón
{"title":"Near-infrared absorption of fused core-modified expanded porphyrins for dye-sensitized solar cells.","authors":"M. I. Menéndez, Nicolás Montenegro-Pohlhammer, Ricardo Pino‐Rios, Rodrigo Urzúa-Leiva, Simone Morales-Lovera, Merlys Borges-Martínez, Kevin Granados-Tavera, R. López, G. Cárdenas-Jirón","doi":"10.2139/ssrn.4347432","DOIUrl":"https://doi.org/10.2139/ssrn.4347432","url":null,"abstract":"Photophysical, photovoltaic, and charge transport properties of fused core-modified expanded porphyrins containing two pyrroles, one dithienothiophene (DTT) unit, and 1-4 thiophenes (1-4) were inspected by using density functional theory (DFT) and time-dependent DFT. Compounds 1-3 have been investigated experimentally before, but 4 is a theoretical proposal whose photophysical features match those extrapolated from 1 to 3. They exhibit absorption in the range of 700-970 nm for their Q bands and 500-645 nm for their Soret bands. The rise of thiophene rings placed in front of the DTT unit in the expanded porphyrin ring causes a bathochromic shift of the longest absorption wavelength, leading to near-infrared absorptions, which represent 49% of the solar energy. All the systems show a thermodynamically favorable process for the electron injection from the dye to TiO2 and adsorption on a finite TiO2 model. The electron regeneration of the dye is only thermodynamically feasible for the smallest expanded porphyrins 1 and 2 when I-/I3- electrolyte is used. The charge transport study shows that for voltages lower than 0.4 V, junctions featuring pentaphyrin 1 and octaphyrin 4 are more conductive than those containing hexaphyrin 2 or heptaphyrin 3. The results showed that the four fused core-modified expanded porphyrins investigated are potential dyes for applications in dye-sensitized solar cells, mainly pentaphyrin 1 and hexaphyrin 2. Moreover, increasing the number of thiophene rings in the macrocycle proved fruitful in favoring absorption in the near-infrared region, which is highly desired for dye-sensitized solar cells.","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"585 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132914579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhonglin Cao, Yuyang Wang, Cooper Lorsung, A. Farimani
{"title":"Neural Network Predicts Ion Concentration Profiles under Nanoconfinement","authors":"Zhonglin Cao, Yuyang Wang, Cooper Lorsung, A. Farimani","doi":"10.48550/arXiv.2304.04896","DOIUrl":"https://doi.org/10.48550/arXiv.2304.04896","url":null,"abstract":"Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electro-osmotic flow. Due to the non-negligible surface interaction and the effect of discrete solvent molecules, molecular dynamics (MD) simulation is often used as an essential tool to study the behavior of ions under nanoconfinement. Despite the accuracy of MD simulation in modeling nanoconfinement systems, it is computationally expensive. In this work, we propose neural network to predict ion concentration profiles in nanochannels with different configurations, including channel widths, ion molarity, and ion types. By modeling the ion concentration profile as a probability distribution, our neural network can serve as a much faster surrogate model for MD simulation with high accuracy. We further demonstrate the superior prediction accuracy of neural network over XGBoost. Finally, we demonstrated that neural network is flexible in predicting ion concentration profiles with different bin sizes. Overall, our deep learning model is a fast, flexible, and accurate surrogate model to predict ion concentration profiles in nanoconfinement.","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128426881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haichao Huang, Yanting Xie, Da Xiong, Ningjun Chen, Xiang Chu, Xinglin Jiang, Haitao Zhang, Weiqing Yang
{"title":"Vertical-MXene based micro-supercapacitors with thickness-independent capacitance.","authors":"Haichao Huang, Yanting Xie, Da Xiong, Ningjun Chen, Xiang Chu, Xinglin Jiang, Haitao Zhang, Weiqing Yang","doi":"10.2139/ssrn.4211509","DOIUrl":"https://doi.org/10.2139/ssrn.4211509","url":null,"abstract":"MXenes have shown great potential as an emerging two-dimensional (2D) material for micro-supercapacitors (MSCs) due to their high conductivity, rich surface chemistry, and high capacity. However, MXene sheets inherently tend to lay flat on the substrate during film formation to assemble into compact stacked structures, which hinders ion accessibility and prolongs ion transport paths, leading to highly dependent electrochemical properties on the thickness of the film. Here, we demonstrate a vertically aligned Ti3C2Tx MXene based micro-supercapacitor with an excellent electrochemical performance by a liquid nitrogen-assisted freeze-drying method. The vertical arrangement of the 2D MXene sheets allows for directional ion transport, enabling the vertical-MXene based MSCs to exhibit thickness-independent electrochemical properties even in thick films. In addition, the MSCs displayed a high areal capacitance of 87 mF cm-2 at 10 mV s-1 along with an excellent stability of ∼87.4% after 10 000 charge-discharge cycles. Furthermore, the vertical-MXene approach proposed here is scalable and can be extended to other systems involving directional transport.","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124277945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Insight into the photocatalytic and photothermal effect in plasmon-enhanced water oxidation property of AuTNP@MnOx core-shell nanoconstruct","authors":"Diptiranjan Paital, Tarun Bansal, Tannu Kaushik, Gayatri Joshi, S. Sett, Saumyakanti Khatua","doi":"10.1063/5.0101743","DOIUrl":"https://doi.org/10.1063/5.0101743","url":null,"abstract":"Development of robust and efficient photocatalytic constructs for boosting the water oxidation reaction (WOR) is needed for establishing a sunlight-driven renewable energy infrastructure. Here we synthesized plasmonic core-shell nanoconstructs consisting of triangular gold nanoprism (Au-TNP) core with mixed manganese oxide (MnOx) shell for photoelectrocatalytic WOR. These constructs show electrocatalytic WOR with low onset overpotential requirement of 270 mV at pH 10.5. Photoexcitation showed further enhancement of their catalytic activity resulting in ~15% decrease of the onset overpotential requirement along with the generation of photocurrent density of up to 300 µA/cm2. We showed that such light-driven enhancement of AuTNP@MnOx dyad's catalytic activity includes contributions from both photocatalytic (hot carriers driven) and photothermal effects with photothermal effect playing the major role for wavelength between 532 nm and 808 nm. The contribution from the photocatalytic effect is appreciable only for high-energy excitations near the interband region, while the photothermal effect largely dominates for lower energy excitations near the LSPR wavelengths of the dyad.","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130580894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lixue Cheng, Jiace Sun, J. E. Deustua, V. Bhethanabotla, Thomas F. Miller
{"title":"Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression","authors":"Lixue Cheng, Jiace Sun, J. E. Deustua, V. Bhethanabotla, Thomas F. Miller","doi":"10.48550/arXiv.2207.08317","DOIUrl":"https://doi.org/10.48550/arXiv.2207.08317","url":null,"abstract":"We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML(KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML(KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, and GDB-13-T) and open-shell (QMSpin) molecules.","PeriodicalId":446961,"journal":{"name":"The Journal of chemical physics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122572861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}