@article{20704, abstract = {Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables, like atom types or spin states, remains underexplored. In this work, we introduce a discrete flow matching model, tailored for systems with discrete phase-space coordinates (e.g., the Ising model or a multicomponent system on a lattice). This approach enables a single model to sample free energy surfaces over a wide temperature range with minimal training overhead, and the model generation is scalable to larger lattice sizes than those in the training set. We demonstrate our approach on the 2D Ising model, showing efficient and reliable free energy sampling. These results highlight the potential of flow matching for low-cost, scalable free energy sampling in discrete systems and suggest promising extensions to alchemical degrees of freedom in crystalline materials. The codebase developed for this work is openly available at https://github.com/tuoping/alchemicalFES.}, author = {Tuo, Ping and Zeng, Zezhu and Chen, Jiale and Cheng, Bingqing}, issn = {1549-9626}, journal = {Journal of Chemical Theory and Computation}, number = {22}, pages = {11427--11435}, publisher = {American Chemical Society}, title = {{Scalable multitemperature free energy sampling of classical Ising spin states}}, doi = {10.1021/acs.jctc.5c01248}, volume = {21}, year = {2025}, } @article{20926, abstract = {Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.}, author = {Kim, Dongjin and Wang, Xiaoyu and Vargas, Santiago and Zhong, Peichen and King, Daniel S. and Inizan, Theo Jaffrelot and Cheng, Bingqing}, issn = {1549-9626}, journal = {Journal of Chemical Theory and Computation}, number = {24}, pages = {12709--12724}, publisher = {American Chemical Society}, title = {{A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials}}, doi = {10.1021/acs.jctc.5c01400}, volume = {21}, year = {2025}, } @article{18452, abstract = {Diffusion models have recently emerged as powerful tools for the generation of new molecular and material structures. The key insight is that the noise in these models is related to the response of the atoms to displacement, and the denoising step is thus analogous to the geometry relaxation of atomistic systems starting from a random structure. Building on this, we present a generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching this response is closely related to score matching in diffusion models. Another important aspect of state-of-the-art diffusion models is the incorporation of physical symmetries such as translation, rotation, and periodicity. RM employs a machine learning interatomic potential and random structure search as the denoising model, inherently respecting these symmetries and exploiting the locality of atomic interactions. RM handles both molecules and bulk materials under the same framework. Its efficiency and generalization are demonstrated on three systems: a small organic molecular data set, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration.}, author = {Cheng, Bingqing}, issn = {1549-9626}, journal = {Journal of Chemical Theory and Computation}, number = {20}, pages = {9259--9266}, publisher = {American Chemical Society}, title = {{Response matching for generating materials and molecules}}, doi = {10.1021/acs.jctc.4c00998}, volume = {20}, year = {2024}, } @article{9680, abstract = {Atomistic modeling of phase transitions, chemical reactions, or other rare events that involve overcoming high free energy barriers usually entails prohibitively long simulation times. Introducing a bias potential as a function of an appropriately chosen set of collective variables can significantly accelerate the exploration of phase space, albeit at the price of distorting the distribution of microstates. Efficient reweighting to recover the unbiased distribution can be nontrivial when employing adaptive sampling techniques such as metadynamics, variationally enhanced sampling, or parallel bias metadynamics, in which the system evolves in a quasi-equilibrium manner under a time-dependent bias. We introduce an iterative unbiasing scheme that makes efficient use of all the trajectory data and that does not require the distribution to be evaluated on a grid. The method can thus be used even when the bias has a high dimensionality. We benchmark this approach against some of the existing schemes on model systems with different complexity and dimensionality.}, author = {Giberti, F. and Cheng, Bingqing and Tribello, G. A. and Ceriotti, M.}, issn = {1549-9626}, journal = {Journal of Chemical Theory and Computation}, number = {1}, pages = {100--107}, publisher = {American Chemical Society}, title = {{Iterative unbiasing of quasi-equilibrium sampling}}, doi = {10.1021/acs.jctc.9b00907}, volume = {16}, year = {2019}, } @article{804, abstract = {Polysaccharides (carbohydrates) are key regulators of a large number of cell biological processes. However, precise biochemical or genetic manipulation of these often complex structures is laborious and hampers experimental structure–function studies. Molecular Dynamics (MD) simulations provide a valuable alternative tool to generate and test hypotheses on saccharide function. Yet, currently used MD force fields often overestimate the aggregation propensity of polysaccharides, affecting the usability of those simulations. Here we tested MARTINI, a popular coarse-grained (CG) force field for biological macromolecules, for its ability to accurately represent molecular forces between saccharides. To this end, we calculated a thermodynamic solution property, the second virial coefficient of the osmotic pressure (B22). Comparison with light scattering experiments revealed a nonphysical aggregation of a prototypical polysaccharide in MARTINI, pointing at an imbalance of the nonbonded solute–solute, solute–water, and water–water interactions. This finding also applies to smaller oligosaccharides which were all found to aggregate in simulations even at moderate concentrations, well below their solubility limit. Finally, we explored the influence of the Lennard-Jones (LJ) interaction between saccharide molecules and propose a simple scaling of the LJ interaction strength that makes MARTINI more reliable for the simulation of saccharides.}, author = {Schmalhorst, Philipp S and Deluweit, Felix and Scherrers, Roger and Heisenberg, Carl-Philipp J and Sikora, Mateusz K}, issn = {1549-9618}, journal = {Journal of Chemical Theory and Computation}, number = {10}, pages = {5039 -- 5053}, publisher = {American Chemical Society}, title = {{Overcoming the limitations of the MARTINI force field in simulations of polysaccharides}}, doi = {10.1021/acs.jctc.7b00374}, volume = {13}, year = {2017}, } @article{17967, abstract = {We report a systematic computational search of molecular frameworks for intrinsic rectification of electron transport. The screening of molecular rectifiers includes 52 molecules and conformers spanning over 9 series of structural motifs. N-Phenylbenzamide is found to be a promising framework with both suitable conductance and rectification properties. A targeted screening performed on 30 additional derivatives and conformers of N-phenylbenzamide yielded enhanced rectification based on asymmetric functionalization. We demonstrate that electron-donating substituent groups that maintain an asymmetric distribution of charge in the dominant transport channel (e.g., HOMO) enhance rectification by raising the channel closer to the Fermi level. These findings are particularly valuable for the design of molecular assemblies that could ensure directionality of electron transport in a wide range of applications, from molecular electronics to catalytic reactions.}, author = {Ding, Wendu and Koepf, Matthieu and Koenigsmann, Christopher and Batra, Arunabh and Venkataraman, Latha and Negre, Christian F. A. and Brudvig, Gary W. and Crabtree, Robert H. and Schmuttenmaer, Charles A. and Batista, Victor S.}, issn = {1549-9626}, journal = {Journal of Chemical Theory and Computation}, number = {12}, pages = {5888--5896}, publisher = {American Chemical Society}, title = {{Computational design of intrinsic molecular rectifiers based on asymmetric functionalization of N-Phenylbenzamide}}, doi = {10.1021/acs.jctc.5b00823}, volume = {11}, year = {2015}, }