IF 1.6 4区化学

Chimia Pub Date : 2026-05-27 DOI: 10.2533/chimia.2026.298

Antonia S Kuhn, Igor Gordiy, Felix Pultar, Sereina Riniker

引用次数: 0

Cooperativity in Enzyme-Substrate Complex Formation in Nitrogenase-like Dark Operative Protochlorophyllide Oxidoreductase (DPOR). 类氮酶暗操作原叶绿内酯氧化还原酶（DPOR）酶-底物复合物形成的协同性。

IF 1.4 4区化学

Chimia Pub Date : 2026-05-27 DOI: 10.2533/chimia.2026.327

Giada Bedendi, Plinio Maroni, Ross D Milton

引用次数: 0

Physics-Informed and Equivariant Machine Learning for Molecular Dipole Moment Prediction. 分子偶极矩预测的物理信息和等变机器学习。

IF 1.6 4区化学

Chimia Pub Date : 2026-05-27 DOI: 10.2533/chimia.2026.314

Ke Chen, Sandra Luber

{"title":"Physics-Informed and Equivariant Machine Learning for Molecular Dipole Moment Prediction.","authors":"Ke Chen, Sandra Luber","doi":"10.2533/chimia.2026.314","DOIUrl":"https://doi.org/10.2533/chimia.2026.314","url":null,"abstract":"Accurate prediction of molecular electric dipole moments is crucial for determining molecular properties and understanding molecular reactivity. Recent developments in equivariant machine learning enable direct prediction of electric dipole moments as vector quantities. This raises the question as to whether it is still necessary to consider physics-informed charge-based principles in the model to achieve satisfactory performance. In this work, we systematically compare direct equivariant electric dipole prediction using MACE with charge-based approaches using adapted MACE which incorporates a variant charge equilibrium equation (QEq) into the MACE framework. Across diverse datasets including QM7b, QM9, as well as subsets of the SPICE and SN2 data sets, we demonstrate that both direct equivariant electric dipole prediction and physics-informed QEq approaches show good performance for short-to-medium range interaction datasets. The charge-based QEq model shows slightly better performance than direct prediction beyond small training data sizes. Notably, the charge-based QEq model outperforms direct electric dipole prediction for the systems with long range interactions. Our results highlight the importance of incorporating physics into the model for improved model interpretability and transferability.","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 5","pages":"314-318"},"PeriodicalIF":1.6,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148136498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Editorial. 社论。

IF 1.6 4区化学

Chimia Pub Date : 2026-05-27 DOI: 10.2533/chimia.2026.289

Stefan Vučković

引用次数: 0

From Experimental Planning to Autonomous Discovery: The Changing Role of Design of Experiments in Nanotechnology. 从实验计划到自主发现：纳米技术实验设计的角色变化。

IF 1.6 4区化学

Chimia Pub Date : 2026-05-27 DOI: 10.2533/chimia.2026.305

Miroslava Nedyalkova, Vasil Simeonov, Marco Lattuada

引用次数: 0

Delta-Augmented Subsystem Density Functional Theory: A Study Across Diverse Systems. 增量子系统密度泛函理论：跨不同系统的研究。

IF 1.6 4区化学

Chimia Pub Date : 2026-05-27 DOI: 10.2533/chimia.2026.319

Michela Pauletti, Marcella Iannuzzi, Vladimir V Rybkin

{"title":"Delta-Augmented Subsystem Density Functional Theory: A Study Across Diverse Systems.","authors":"Michela Pauletti, Marcella Iannuzzi, Vladimir V Rybkin","doi":"10.2533/chimia.2026.319","DOIUrl":"https://doi.org/10.2533/chimia.2026.319","url":null,"abstract":"In this study, we expand upon and benchmark the Kim-Gordon method (KG), a subsystem density functional theory (DFT) approach appended with a machine-learned correction to compensate for errors in the kinetic energy term and thereby match Kohn-Sham (KS) DFT accuracy. This correction is obtained through 'delta- learning' based on KS-DFT data. The method promises sampling of configurations for condensed molecular systems at the Kohn-Sham DFT level of accuracy at a fraction of the computational cost. Despite encouraging results for liquid water, it was not obvious whether the scheme had more general appeal. In this work, we show that the approach allows for a broad range of applications. In particular, we successfully apply it to complex molecular liquids, such as bulk ammonia and methanol. As a bonus, the correction trained on the bulk KS data is applicable to clusters, illustrating its transferability. By focusing on 'delta-learning'-predicting small corrections rather than full Kohn-Sham (KS) energies and forces-we significantly reduce the required training data. This approach, especially when combined with linear-scaling self-consistent field (LS-SCF) techniques, establishes the method as a highly efficient computational tool for molecular dynamics.","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 5","pages":"319-326"},"PeriodicalIF":1.6,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148136495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics. 高精度分子模拟与机器学习势和量子动力学的半经典近似。

IF 1.6 4区化学

Chimia Pub Date : 2026-05-27 DOI: 10.2533/chimia.2026.292

Valerii Andreichev, Jindra Dušek, Markus Meuwly, Jeremy O Richardson

引用次数: 0

Bending the Macrocycle: A Deep Dive into Curved Porphyrinoids. 弯曲大循环：对弯曲卟啉的深入研究。

IF 1.6 4区化学

Chimia Pub Date : 2026-04-29 DOI: 10.2533/chimia.2026.219

Owen Kanisius, Michel Rickhaus

引用次数: 0

Using Iron amides as Basic Reagents for Fluoroarene Metalation. 铁酰胺作为氟丙烷金属化碱性试剂的研究。