A Molecular View of Methane Activation on Ni(111) through Enhanced Sampling and Machine Learning.

Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules. It shows excellent transferability to long alkanes and closely describes the ab inito potential energy surface for polyenes. Simulation of liquid ethane also shows reasonable agreement with experimental reports. This is a promising initiative toward a universal ab initio quality force-field for hydrocarbons and organic molecules.

Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Altogether, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others.

While DFT is the computational method of choice for mechanistic insight in homogeneous catalysis, the recent rise of foundation-level machine learning interatomic potentials (MLIPs) invites reconsideration: are we approaching competition, or a deeper synergy? These pretrained, fast surrogates are able to map reaction space, sample conformers, and flag likely transition states, potentially displacing routine low-level DFT. Yet their reliability hinges on calibrated uncertainty, transferability across ligand and oxidation-state manifolds, and faithful treatment of long-range polarization, solvation, and open-shell or multireference character. We argue that the near future will likely be contested: MLIPs will handle everyday exploratory tasks, while DFT and higher-level methods will anchor electronic effects, validate high-stakes predictions, and resolve edge cases. If supported by FAIR catalysis datasets, standardized workflows, and robust error quantification, the two approaches will coevolve, enabling scalable, predictive discovery without sacrificing rigor or interpretability.

The integration ofmachine learning into reactive chemistry, materialsdiscovery, and drug design is transforming the development of novelmolecules and materials. Machine Learning Interatomic Potentials (MLIPs)predict potential energies and forces with quantum chemistry accuracy,surpassing traditional approaches. Incorporating force fitting inMLIP training enhances potential-energy surface predictions and improvesmodel transferability and reliability. This paper introduces and evaluatesthe integration of Hessian matrix training in MLIPs, which encodessecond-order information about the PES curvature. Our evaluation focuseson models trained only to equilibrium geometries and first-order saddlepoints (i.e., critical points on the potential surface), demonstratingtheir ability to extrapolate to nonequilibrium geometries. This integrationimproves extrapolation capabilities, allowing MLIPs to accuratelypredict energies, forces, and Hessian predictions for nonequilibriumgeometries. Hessian-trained MLIPs enhance reaction pathway modeling,transition state identification, and vibrational spectra predictions,benefiting molecular dynamics (MD) simulations and Nudged ElasticBand (NEB) calculations. By analyzing models trained with varyingcombinations of energy, force, and Hessian data on a small moleculereactive data set, we demonstrate that models including Hessian informationnot only extrapolate more accurately to unseen molecular systems,improving accuracy in reaction modeling and vibrational analysis,but also reduce the total amount of data required for effective training.However, the primary trade-off is increased computational expense,as Hessian training requires more resources than conventional energy-forcetraining. Our findings provide comprehensive insights into the advantagesand limitations of Hessian integration in MLIP training, allowingpractitioners in computational chemistry to make informed decisionsabout employing this method in accordance with their specific researchobjectives and computational constraints.

Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond those accessible to conventional first-principles methods.1 Specifically, for solid electrolyte materials, MLIPs have bridged the gap between ionic conductivities simulated by ab initio molecular dynamics (AIMD) at room temperature and experimental measurements.2 MLIPs also extend the accuracy of AIMD to systems too computationally demanding for AIMD alone, including grain boundaries, amorphous structures, interfaces, and structures with varying short-range order.3 Recently, the emergence of universal MLIPs has opened opportunities for discovering new materials at unprecedented scales.4 In this talk, I will introduce MatPES, a fundamental dataset constructed to enhance the stability and accuracy of universal MLIPs in capturing both thermodynamic and kinetic properties.5 With benchmark results, I will also attempt to answer the question posed in the title of my talk. Unke, O. T. et al. Machine Learning Force Fields. Chem. Rev. 121, 10142–10186 (2021).Qi, J. et al. Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors. Mater. Today Phys. 21, 100463 (2021).Lee, T. et al. Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3. Nat. Commun. 14, 1940 (2023).Jacobs, R. et al. A practical guide to machine learning interatomic potentials – Status and future. Curr. Opin. Solid State Mater. Sci. 35, 101214 (2025).Kaplan, A. D. et al. A Foundational Potential Energy Surface Dataset for Materials. Preprint at https://doi.org/10.48550/arXiv.2503.04070 (2025).

The accurate description of phonon dispersion of two-dimensional (2D) materials demonstrates significance in many research fields of condensed matter physics. In this paper, we systematically calculate the phonon spectra and transport properties of six representative 2D materials (encompassing single-element and binary compounds with flat, buckled, and puckered backbone geometries) by means of density functional theory (DFT) and two machine learning interatomic potentials [MLIPs, on-the-fly machine learning potential (FMLP), and moment tensor potential (MTP)]. The results show that the acoustic out-of-plane flexural (ZA) dispersion of the 2D materials are always and easily exhibiting non-quadratic dispersion phenomena near the center of the Brillouin zone by using the pure DFT calculation method. This phenomenon contradicts physics and reflects intuitively from the non-zero group velocity at Γ point. However, no matter which MLIP (FMLP/MTP) the calculation is based on, it could solve such behavior perfectly, where the ZA mode conforms to the quadratic dispersion relationship in the long-wavelength limit. Our results further demonstrate that compared to the pure DFT calculation, the FMLP and MTP method could quickly and relatively accurately obtain the lattice thermal conductivities of graphene, silicene, phosphorene, SiC, MoS2, and GeS. The findings presented in this work provide a solution about the pseudophysical phenomenon of ZA dispersions in 2D materials with the pure DFT calculation, which will greatly facilitate research areas such as phonon thermal transport, flexural mechanics, and electron–acoustic coupling.

Dopants can tune the performance of MoS2 in various applications, but the use of molecular dynamics simulations for doped MoS2 materials discovery is limited by the lack of multidopant interatomic potentials. Universal machine learning interatomic potentials (MLIPs) could be a solution, but the accuracy of these potentials must first be evaluated. Here, we evaluate the accuracy of a recently developed MLIP, META's Universal Model for Atoms, for 25 different MoS2 dopants spanning metals, nonmetals, and transition metals in Mo-substitution, S-substitution, and intercalated positions by benchmarking the MLIP-predicted formation energy and the dopant-induced structural change against density functional theory (DFT) calculations. The computational framework for MLIP validation and simulations is described in detail, and the source code is made available online. The MLIP is then demonstrated by performing heating-cooling simulations of MoS2 supercells with all 25 dopants. These simulations capture complex phenomena including dopant clustering, MoS2 layer fracturing, interlayer diffusion, and chemical compound formation at orders-of-magnitude reduced computational cost compared to DFT. This work provides a computational workflow for the application-oriented design of doped-MoS2, enabling high-throughput screening of dopant candidates and optimization of compositions for targeted tribological, electronic, and optoelectronic performance.

In nuclear and/or solar applications that involve molten salts, impurities frequently enter the salt as either fission products or via corrosion. Impurities can interact and make complexes, but the impact of such complexation on the properties of the salts and corrosion rates has not been understood. Common impurities in molten salts, such as FLiBe, include Cr, Ni, and Fe. Here, we investigate the complexation of Cr and Ni in FLiBe using molecular dynamics based on a machine learning interatomic potential (MLIP) fitted using the atomic cluster expansion (ACE) method. The MLIP allows us to overcome the challenges of simultaneously needing accurate energetics and long time scale to study complexation. We demonstrate that impurity behavior is more difficult to capture than that of concentrated elements with MLIPs due to less sampling in training data, but that this can be overcome by using active learning strategies to obtain a robust fit. Our findings suggest that there is a weak but potentially significant binding free energy between CrF2 and NiF2 in eutectic FLiBe of -0.112eV. Under certain conditions this binding creates a significant short-range order between the species and lowers the redox potential of NiF2 in the presence of CrF2 in FLiBe, making Ni dissolution more favorable in the presence of Cr as compared to its dissolution in pure FLiBe. However, we find little impact of this complexation on the diffusivity of Ni and Cr. Overall, the methodology presented here suggests an approach to modeling complexation with MLIPs and suggests that interactions between dissolved cations could be playing a significant role in some salt thermophysical properties.

Training machine learning interatomic potentials (MLIPs) on total energies of molecular clusters using differential or transfer learning is becoming a popular route to extend the accuracy of correlated wave-function theory to condensed phases. A key challenge, however, lies in validation, as reference observables in finite-temperature ensembles are not available at the reference level. Here, we construct synthetic reference data from pretrained MLIPs and evaluate the generalizability of cluster-trained models on ice-Ih, considering scenarios where both energies and forces and where only energies are available for training. We study the accuracy and data-efficiency of differential, single-fidelity transfer, and multi-fidelity transfer learning against ground-truth thermodynamic observables. We find that transferring accuracy from clusters to bulk requires regularization, which is best achieved through multi-fidelity transfer learning when training on both energies and forces. By contrast, training only on energies introduces artefacts: stable trajectories and low energy errors conceal large force errors, leading to inaccurate microscopic observables. More broadly, we show that accurate reproduction of microscopic structure correlates strongly with low force errors but only weakly with energy errors, whereas global properties such as energies and densities correlate with low energy errors. This
highlights the need to incorporate forces during training or to apply careful validation before production. Our results highlight the promise and pitfalls of cluster-trained MLIPs for condensed phases and provide guidelines for developing - and critically, validating - robust and data-efficient MLIPs.

Low-dimensional hybrid organic-inorganic perovskites (HOIPs) are promising electronically active materials for light absorption and emission. The design space of HOIPs is extremely large, as a variety of organic cations can be combined with different inorganic frameworks. This not only allows for tunable electronic and mechanical properties but also necessitates the development of new tools for in silico high throughput analysis of candidate materials. In this work, we present an accurate, efficient, and widely applicable machine learning interatomic potential (MLIP) trained on 86 diverse experimentally reported HOIP materials. This MLIP was tested on 73 experimentally reported perovskite compositions and achieves a high accuracy, relative to density functional theory (DFT). We also introduce a novel random structure search algorithm designed for the crystal structure prediction of 2D HOIPs. The combination of MLIP and the structure search algorithm reliably recovers the crystal structure of 14 known 2D perovskites by specifying only the organic molecule and inorganic cation/halide. Performing this crystal structure search with ab initio methods would be computationally prohibitive but is relatively inexpensive with the MLIP. Finally, the developed procedure is used to predict the structure of a totally new HOIP with cation (cis-1,3-cyclohexanediamine). Subsequently, the new compound was synthesized and characterized, which matches the predicted structure, confirming the accuracy of our method. This capability will enable the efficient and accurate screening of thousands of combinations of organic cations and inorganic layers for further investigation.

External field‐enhanced electrocatalysis is a novel and promising approach for boosting the efficiency of electrocatalytic reactions, potentially achieving significant enhancement without altering the composition and structure of electrocatalysts. In addition, the scaling relations of electrocatalysis typically lead to similar variations of initial‐state and transition‐state (TS) energy, which minimally impacts the reaction energy barrier. A sophisticated design of the external field effects shall break these scaling relations. This review provides a comprehensive overview of current research on the effect of mechanical, electric, and magnetic fields on electrocatalysis. It meticulously details the mechanisms underlying activity enhancement based on external field regulations, spanning from the synthesis of electrocatalytic materials to their behavior during the reaction process and modulation of the electrolyte environment. Additionally, the applications of emerging machine learning (ML) technologies in electrocatalysis design, including machine learning interatomic potentials (MLIPs) to simulate large‐scale and dynamic chemical reaction processes, data‐driven design and optimization of electrocatalysis performance, are briefly reviewed. In addition, the significant potential of ML technologies in conjunction with external field regulation, envisioning them as effective tools for optimizing or reverse designing electrocatalysis, considering both thermodynamic and kinetic factors as well as the dynamic effect of electrocatalyst surfaces under extreme external fields, is highlighted.

This work develops a hybrid machine learning/molecular mechanics (ML/MM) interface integrated into the AMBER molecular simulation package. The resulting platform is highly versatile, accommodating several advanced machine learning interatomic potential (MLIP) models while providing stable simulation capabilities and supporting high-performance computations. Building upon this robust foundation, we developed new computational protocols to enable pathway-based and end point-based free energy calculation methods utilizing ML/MM hybrid potential. In particular, we proposed an ML/MM-compatible thermodynamic integration (TI) framework that adequately addressed the challenge of applying MLIPs in TI calculations due to its indivisible nature of energy and force. Our results demonstrated that the hydration free energies calculated using this framework achieved an accuracy of 1.0 kcal/mol, outperforming the traditional approaches. Moreover, ML/MM enables more precise sampling of conformational ensembles for improved end point-based free energy calculations. Overall, our efficient, stable, and highly compatible interface not only broadens the application of MLIPs in multiscale simulations but also enhances the accuracy of free energy calculations from multiple aspects. By introducing a novel ML/MM-compatible thermodynamic integration framework, we offered a novel foundation for combining advanced multiscale simulation methodologies with highly accurate free energy calculation techniques, thereby opening new avenues and providing a robust theoretical framework for future developments in this field.

Detailed knowledge of the structure of bio molecules with atomic resolution is essential for the understanding of their function. Moreover the identification and quantification of their dynamic processes are important, as they are the origin of molecular functionalities. Different spectroscopic methods like X-ray crystallography (limited to structure elucidation, no dynamics), mass spectroscopy, electron spin resonance (EPR) and nuclear magnetic resonance (NMR) spectroscopy can deliver this detailed structural information. Especially NMR spectroscopy has found its application in the identification of dynamic processes. This thesis was focused on the characterization of dynamic processes, the structure elucidation of natural products available in nanograms and the synthesis of lanthanide chelating tags for the study of protein-ligand and protein-protein interactions in solution. The thesis is divided into three chapters addressing a specific task of the mentioned topic. A: The influences of different donors and linkers on lanthanide chelating tags were investigated. A 4S-Tetramethylcyclen was used as enantiomerically pure core molecule. The side chains were based on enantiopure lactic acid derivatives. The carboxylic acids of lactic acid were transformed into different functional groups like thiols, nitrogens, and carbonyl. These different donor groups are expected to change the magnetic susceptibility tensor of the lanthanide cation in complex with the synthesized tags. Special protection protocols for the introduction of heteroatoms are presented. On this basis also two new linkers were introduced. These linkers bind in a selective way to cysteins on a protein surface as thioethers. The alpha-bromoketones are very selective for the coupling to cysteins, nevertheless they have a tendency to hydrolyse under Lewis acidic conditions. Vinylsulfones require harsher tagging conditions but they are much more stable against hydrolysis. All new tags were attached to proteins and tested as PCS reagents in protein NMR spectroscopy. B: The effects of para-substituents on the rotation barrier of 2,2«-propyl and 2,2«-butyl-bridged biphenyls were studied by dynamic NMR spectroscopy and dynamic HPLC measurements. Gibbs free activation energies of the rotation about the central biphenyl bond were estimated by variable temperature 1H-NMR experiments for the propyl bridged biphenyl. The resulting data were correlated to Hammett-parameters ?P as a measure of electron donor and acceptor strength. It was demonstrated that the electronic effects influence the activation barrier significantly, whereas sterics had only minor influence. Rate constants were calculated from line shape analysis and analysed by Eyring plots to calculate the entropic and enthalpic contributions. Thermodynamic data for the butyl-bridged biphenyls were directly obtained from dynamic HPLC chromatograms. DFT calculations delivered different transition states for the two series of biphenyls. The calculation of the activation parameters showed a similar trend and therefore the model is validated. The differences in the enthalpic and entropic contributions between HPLC, NMR and DFT calculations are method dependent, which was proven by changes of the solvent in NMR experiments that led to alteration of these contributions. C: The identification and total synthesis of a novel methylated lipid antigen (mLPA) was performed. The presented mLPA shows potent inhibition properties against human leukaemia. A combination of extraction protocols, activation essays and HPLC-MS measurements were used to identify the different antigen molecules from cell extracts. Three different candidates were identified. MS-MS experiments delivered structural insights, which were further confirmed by NMR spectroscopy and allowed the characterization of two of these structures. Total syntheses of the identified structures were performed in 6 linear steps. The high biological activity of the synthesized structures corroborated the identity of the active molecule.

Using machine learning (ML) to construct interatomic interactions and thus potential energy surface (PES) has become a common strategy for materials design and simulations. However, those current models of machine-learning interatomic potential (MLIP) consider no relevant physical constraints or global scaling and thus may owe intrinsic out-of-domain difficulty which underlies the challenges of model generalizability and physical scalability. Here, by incorporating the global universal scaling law, we develop an ultrasmall parameterized MLIP with superlinear expressive capability, named SUS2-MLIP. Due to the global scaling derived from the universal equation of state (UEOS), SUS2-MLIP not only has significantly reduced parameters by decoupling the element space from coordinate space but also naturally outcomes the out-of-domain difficulty and endows the model with inherent generalizability and scalability even with relatively small training dataset. The non-linearity-embedding transformation in radial function endows the model with superlinear expressive capability. SUS2-MLIP outperforms the state-of-the-art MLIP models with its exceptional computational efficiency, especially for multiple-element materials and physical scalability in property prediction. This work not only presents a highly efficient universal MLIP model but also sheds light on incorporating physical constraints into AI-aided materials simulation.

Interpolating Moving Ridge Regression (IMRR): A machine learning algorithm to predict energy gradients for ab initio molecular dynamics simulations