The Journal of Physical Chemistry A最新文献_第4页

Basis Set Convergence and Empirical Approaches for Obtaining Accurate Diagonal Born-Oppenheimer Corrections from an Extensive Database of 200 Structurally Diverse Hydrocarbons. 基集收敛和经验方法获得准确对角Born-Oppenheimer修正从一个广泛的数据库的200结构多样的碳氢化合物。

IF 2.7 2区化学

The Journal of Physical Chemistry A Pub Date : 2025-06-26 Epub Date: 2025-06-12 DOI: 10.1021/acs.jpca.5c02680

Amir Karton

{"title":"Basis Set Convergence and Empirical Approaches for Obtaining Accurate Diagonal Born-Oppenheimer Corrections from an Extensive Database of 200 Structurally Diverse Hydrocarbons.","authors":"Amir Karton","doi":"10.1021/acs.jpca.5c02680","DOIUrl":"10.1021/acs.jpca.5c02680","url":null,"abstract":"The Born-Oppenheimer (BO) approximation is fundamental to computational chemistry because it drastically simplifies the time-independent Schrödinger equation, making calculations for molecular systems computationally feasible. Accurate determination of the diagonal Born-Oppenheimer correction (DBOC) is essential for achieving benchmark accuracy in high-level thermochemical applications. Here, we establish the DBOC200HC database, consisting of 200 structurally diverse hydrocarbons with up to 18 carbon atoms (e.g., triamantane (C18H24)), including aliphatic, aromatic, antiaromatic, cyclic, noncyclic, and caged systems. Reference DBOCs are determined near the coupled-cluster singles and doubles complete basis set limit (CCSD/CBS) using additivity schemes based on HF/cc-pVQZ and CCSD/cc-pVnZ (n = D, T) calculations. Given the computational expense associated with CCSD/CBS calculations for large hydrocarbons, it is important to develop reliable yet computationally economical approximations. Several such approaches are assessed using the DBOC200HC database. While scaled Hartree-Fock methods offer limited improvement, methods incorporating first-order Møller-Plesset perturbation theory (MP1) perform significantly better. Specifically, calculating the DBOC at the MP1/cc-pVDZ level of theory and scaling the MP1 correlation component (ΔEDBOCMP1 = EDBOCMP1 - EDBOCHF) by an empirical factor of 1.5447 yields the best balance between accuracy (RMSD = 0.026 kJ/mol) and computational cost (practically the same cost as HF/cc-pVDZ). This exceptionally low RMSD suggests that highly accurate DBOCs for use in high-level thermochemical protocols can be obtained via the scaled MP1 approach, without resorting to computationally more demanding levels of theory such as MP2 or CCSD. To validate our results, we further test the empirical methods optimized over the DBOC200HC database on an independent database of 12 larger hydrocarbons, including systems like dodecahedrane (CH)20.","PeriodicalId":59,"journal":{"name":"The Journal of Physical Chemistry A","volume":" ","pages":"5692-5699"},"PeriodicalIF":2.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geometric Guidance Integrated with Directed Electrostatics Strategy within a Graph Neural Network Approach for Nanocluster Structure Prediction. 几何制导与定向静电策略相结合的图神经网络纳米团簇结构预测。

IF 2.7 2区化学

The Journal of Physical Chemistry A Pub Date : 2025-06-26 Epub Date: 2025-06-13 DOI: 10.1021/acs.jpca.5c02284

Sridatri Nandy, K V Jovan Jose

{"title":"Geometric Guidance Integrated with Directed Electrostatics Strategy within a Graph Neural Network Approach for Nanocluster Structure Prediction.","authors":"Sridatri Nandy, K V Jovan Jose","doi":"10.1021/acs.jpca.5c02284","DOIUrl":"10.1021/acs.jpca.5c02284","url":null,"abstract":"We introduce the Geometric-DESIGNN method, which integrates Geometric Guidance with Directed Electrostatics Strategy within a Graph Neural Network framework to predict the stable configuration of nanoclusters on their potential energy surfaces. This approach merges the geometric and electronic strategies using graph neural network-based models to predict structures of large atomic clusters with specific size and point-group symmetries. This approach aids in constructing atomic metal cluster structures by predicting building frames through a geometric approach and locating the minima in the molecular electrostatic potential (MESP) landscape. By following alternate geometric and DESIGNN building strategies for each shell of parent clusters, we efficiently achieve close-packed daughter structures along their evolutionary paths. The geometric-DESIGNN approach is validated on the prototype Mgn clusters, by building structures for sizes up to n < 561. Furthermore, constraining the point-group symmetry of the parent clusters, we identify new symmetric isomers of medium to large Mgn clusters with n < 150. This methodology is also employed to construct stable Mgn nanoclusters with n = 332, 338, and 561. Benchmarking results show that the geometric-DESIGNN approach is an efficient tool for accelerated prediction of the nanocluster structure.","PeriodicalId":59,"journal":{"name":"The Journal of Physical Chemistry A","volume":" ","pages":"5671-5682"},"PeriodicalIF":2.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.7 2区化学