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The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project
Authors:
Qiming Sun,
Matthew R Hermes,
Xiaojie Wu,
Huanchen Zhai,
Xing Zhang,
Abdelrahman M. Ahmed,
Juan José Aucar,
Oliver J. Backhouse,
Samragni Banerjee,
Peng Bao,
Nikolay A. Bogdanov,
Kyle Bystrom,
Frédéric Chapoton,
Ning-Yuan Chen,
Ivan Yu. Chernyshov,
Helen S. Clifford,
Sander Cohen-Janes,
Zhi-Hao Cui,
Yann D. Damour,
Nike Dattani,
Linus Bjarne Dittmer,
Sebastian Ehlert,
Janus Juul Eriksen,
Francesco A. Evangelista,
Simon A. Ewing
, et al. (78 additional authors not shown)
Abstract:
Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks.
Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks.
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Submitted 7 April, 2026; v1 submitted 14 March, 2026;
originally announced March 2026.
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Weighted Active Space Protocol for Multireference Machine-Learned Potentials
Authors:
Aniruddha Seal,
Simone Perego,
Matthew R. Hennefarth,
Umberto Raucci,
Luigi Bonati,
Andrew L. Ferguson,
Michele Parrinello,
Laura Gagliardi
Abstract:
Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application to train machine learning-based interatomic potentials (MLPs) for catalytic dynamics has been challenging due to the sensitivity of multireference calculations t…
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Multireference methods such as multiconfiguration pair-density functional theory (MC-PDFT) offer an effective means of capturing electronic correlation in systems with significant multiconfigurational character. However, their application to train machine learning-based interatomic potentials (MLPs) for catalytic dynamics has been challenging due to the sensitivity of multireference calculations to the underlying active space, which complicates achieving consistent energies and gradients across diverse nuclear configurations. To overcome this limitation, we introduce the Weighted Active-Space Protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrate the method on the TiC+-catalyzed C-H activation of methane, a reaction that poses challenges for Kohn-Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a new paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.
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Submitted 15 May, 2025;
originally announced May 2025.
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A Modular and Extensible CHARMM-Compatible Model for All-Atom Simulation of Polypeptoids
Authors:
Alex Berlaga,
Kaylyn Torkelson,
Aniruddha Seal,
Jim Pfaendtner,
Andrew L. Ferguson
Abstract:
Peptoids (N-substituted glycines) are a class of sequence-defined synthetic peptidomimetic polymers with applications including drug delivery, catalysis, and biomimicry. Classical molecular simulations have been used to predict and understand the conformational dynamics of single peptoid chains and their self-assembly into diverse morphologies including sheets, tubes, spheres, and fibrils. The CGe…
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Peptoids (N-substituted glycines) are a class of sequence-defined synthetic peptidomimetic polymers with applications including drug delivery, catalysis, and biomimicry. Classical molecular simulations have been used to predict and understand the conformational dynamics of single peptoid chains and their self-assembly into diverse morphologies including sheets, tubes, spheres, and fibrils. The CGenFF-NTOID model based on the CHARMM General ForceField has demonstrated success in enabling accurate all-atom molecular modeling of the structure and thermodynamic behavior of peptoids. Extension of this force field to new peptoid side chain chemistries has historically required parameterization of new side chain bonded interactions against ab initio and/or experimental data. This fitting protocol improves the accuracy of the force field but is also burdensome and time consuming, and precludes modular extensibility of the model to arbitrary peptoid sequences. In this work, we develop and demonstrate a Modular Side Chain CGenFF-NTOID (MoSiC-CGenFF-NTOID) as an extension of CGenFF-NTOID employing a modular decomposition of the peptoid backbone and side chain parameterizations wherein arbitrary side chain chemistries within the large family of substituted methyl groups (i.e., -CH3, -CH2R, -CHRR' -CRR'R'') are directly ported from CGenFF without any additional reparameterization. We validate this approach against ab initio calculations and experimental data to to develop a MoSiC-CGenFF-NTOID model for all 20 natural amino acid side chains along with 13 commonly-used synthetic side chains, and present an extensible paradigm to efficiently determine whether a novel side chain can be directly incorporated into the model or whether refitting of the CGenFF parameters is warranted. We make the model freely available to the community along with a tool to perform automated initial structure generation.
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Submitted 9 September, 2024;
originally announced September 2024.
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Observation of multiple flat bands and van Hove singularities in the distorted kagome metal NdTi3Bi4
Authors:
Mazharul Islam Mondal,
Anup Pradhan Sakhya,
Milo Sprague,
Brenden R. Ortiz,
Matthew Matzelle,
Arun K Kumay,
Avike Seal,
Barun Ghosh,
Arun Bansil,
Madhab Neupane
Abstract:
Kagome materials have attracted enormous research interest recently owing to their diverse topological phases and manifestation of electronic correlation. Here, we present the electronic structure of a distorted ferromagnetic kagome metal, NdTi3Bi4, exhibiting a transition temperature of 9 K. Our investigation employs a combination of angle-resolved photoemission spectroscopy (ARPES) measurements…
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Kagome materials have attracted enormous research interest recently owing to their diverse topological phases and manifestation of electronic correlation. Here, we present the electronic structure of a distorted ferromagnetic kagome metal, NdTi3Bi4, exhibiting a transition temperature of 9 K. Our investigation employs a combination of angle-resolved photoemission spectroscopy (ARPES) measurements and density functional theory (DFT) calculations. We discover the presence of two flat bands which are found to originate from the kagome structure formed by Ti atoms with major contribution from Ti dxy and Ti dx2-y2 orbitals. We also observed multiple van Hove singularities (VHSs) in its electronic structure, with one VHS lying near the Fermi level. The ARPES data reveals the existence of Dirac cone at the K point, a finding which is corroborated by our DFT calculations. These findings present detailed electronic structure capable of hosting correlation-driven phenomenon in this novel ferromagnetic kagome metal.
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Submitted 20 December, 2025; v1 submitted 19 November, 2023;
originally announced November 2023.
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Incorporating Ion-Specific van der Waals and Soft Repulsive Interactions in the Poisson-Boltzmann Theory of Electrical Double Layers
Authors:
Aniruddha Seal,
Utkarsh Tiwari,
Ankur Gupta,
Ananth Govind Rajan
Abstract:
Electrical double layers (EDLs) arise when an electrolyte is in contact with a charged surface, and are encountered in several application areas including batteries, supercapacitors, electrocatalytic reactors, and colloids. In the modeling of EDLs, a prominent knowledge gap has been the exclusion of van der Waals (vdW) and soft repulsive interactions in modified Poisson-Boltzmann (PB) theories. Al…
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Electrical double layers (EDLs) arise when an electrolyte is in contact with a charged surface, and are encountered in several application areas including batteries, supercapacitors, electrocatalytic reactors, and colloids. In the modeling of EDLs, a prominent knowledge gap has been the exclusion of van der Waals (vdW) and soft repulsive interactions in modified Poisson-Boltzmann (PB) theories. Although more short-ranged as compared to electrostatic interactions, we show here that vdW interactions can play an important role in determining the structure of the EDL via the formation of a Stern layer and in modulating the differential capacitance of an electrode in solution. To this end, we incorporate ion-ion and wall-ion vdW attraction and soft repulsion via a 12-6 Lennard-Jones (LJ) potential, resulting in a modified PB-LJ approach. The wall-ion LJ interactions were found to have a significant effect on the electrical potential and concentration profiles, especially close to the wall. However, ion-ion LJ interactions do not affect the EDL structure at low bulk ion concentrations (< 1 M). We also derive dimensionless numbers to quantify the impact of ion-ion and wall-ion LJ interactions on the EDL. Furthermore, in the pursuit of capturing ion-specific effects, we apply our model by considering various combinations of ions. We observe how varying parameters such as the electrolyte concentration and electrode potential affect the structure of the EDL due to the competition between ion-specific LJ and electrostatic interactions. Lastly, we show that the inclusion of vdW and soft repulsion interactions as well as hydration effects lead to a better qualitative agreement of the PB models with experimental double-layer differential capacitance data. Overall, the modified PB-LJ approach presented herein will lead to more accurate theoretical descriptions of EDLs in various application areas.
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Submitted 15 February, 2023;
originally announced February 2023.