Abstract Electric vehicles (EVs) continue to increase their share of the automotive market. To spur this growth, original equipment manufacturers (OEMs) and battery cell manufacturers have invested in atomistic modeling approaches based on fundamental science. This effort has been successful in improving vehicle performance through modifications to battery cell chemistry. However, it is our view that atomistic modeling can go a step further to affect vehicle battery design. In this perspective, we demonstrate a multi-scale modeling approach to link atomic-scale phenomena with full cell predictions relevant for battery design engineers. Recent multi-scale modeling approaches undertaken at General Motors are discussed. We show that variation in the predicted diffusivity of lithium ions in the electrolyte leads to variation in final cell temperatures of 3°C in small format cells, and that reversible volume change for common cathode materials can be as large as 10% for the full state of lithiation window. Additionally, machine learning (ML) will be presented as another growing area in the literature to drive cell-level battery design. Linking ML approaches with datasets from atomistic modeling represents a key direction of growth for vehicle design.

The Modern Physics course discusses basic concepts such as relativity, quantum, and atomic structure, which form the basis of today's technology. One of the important early topics, namely the atomic model, is often difficult for students to understand because it is abstract and not immediately apparent. The Contextual Teaching and Learning (CTL) approach provides a solution by linking academic material to real phenomena that are close to everyday life. One example is the color of the flame on a propane gas stove, which reflects electron transitions and the light spectrum. By utilizing this phenomenon as a learning context, students can understand the concept of the atomic model in a more concrete, meaningful, and applicable way. The purpose of this study was to measure students' interest in learning after the application of Contextual Teaching and Learning (CTL) in the Atomic Model Material by raising the phenomenon of LPG gas stoves. The research method used was descriptive, and the data analysis used was quantitative descriptive analysis of learning interest after learning. The subjects of this study were 15 fifth-semester students in the 2025/2026 academic year from the Physics Education Study Program at PGRI Silampari University. Data collection techniques used questionnaires, and the data collection instrument was a questionnaire on learning interest. The research results were obtained after applying CTL to atomic model material by raising the phenomenon of LPG gas stoves. The average student learning interest questionnaire score reached 73.5, with a classical percentage score of 93.3%, so it can be concluded that the application of the CTL model to atomic model material by raising the phenomenon of LPG gas stoves made student learning interest very good.

Enzymes find broad use as biocatalysts in industry and medicine owing to their exquisite selectivity, efficiency and mild reaction conditions. Custom-designed enzymes can produce tailor-made biocatalysts with potential applications that extend beyond natural reactions. However, current design methods require testing a large number of designs and mostly produce de novo enzymes with low catalytic activities1-3. As a result, they require costly experimental optimization and high-throughput screening to be industrially viable4,5. Here we present rotamer inverted fragment finder-diffusion (Riff-Diff), a hybrid machine learning and atomistic modelling strategy for scaffolding catalytic arrays in de novo proteins. We highlight the general applicability of Riff-Diff by designing enzymes for two mechanistically distinct chemical transformations, the retro-aldol reaction and the Morita-Baylis-Hillman reaction. We show that in both cases, it is possible to generate catalysts that exhibit activities rivalling those optimized by in vitro evolution, along with exquisite stereoselectivity. High-resolution structures of six of the designs revealed near-atomic active site design precision. The design strategy can, in principle, be applied to any catalytically competent amino acid array. These findings lay the basis for practical applicability of de novo protein catalysts in synthesis and describe fundamental principles of protein design and enzyme catalysis.

Plant cryptochromes (CRYs) are blue-light photoreceptors regulating physiological processes via oligomerization-dependent interaction with effectors. However, the structural basis for photoactivated CRY-effector assembly remains elusive. Here, we report the crystal structure of an active maize CRY1c photolyase homology region in complex with GLOSSY2 (ZmGL2), a BAHD acyltransferase family protein that could form an enzyme complex with ECERIFERUM6 (ZmCER6) and direct very-long-chain fatty acid elongation in cuticular wax biosynthesis. Light-activated CRY1c forms a homotetrameric scaffold. Each protomer binds one ZmGL2 molecule via conformational changes, forming a 4:4 hetero-octameric photosignaling complex. Structural alignment shows 78% overlap between the GL2-binding interfaces in the ZmCRY1c-ZmGL2 and ZmCER6-ZmGL2 complexes. Biochemically, CRY1c dose-dependently inhibits ZmCER6-ZmGL2 enzyme activity, unveiling a light-dependent regulatory switch modulating very-long-chain fatty acid elongation efficiency. Our work establishes the atomic model for light-activated CRY-effector assembly and uncovers spatial competition between photoreceptor and metabolic enzyme complexes as a photoregulatory paradigm in wax biosynthesis.

Nanoscale systems often contain weakly coupled components, as exemplified by layered materials. Time-domain atomistic modeling of excited state processes in such systems with nonadiabatic (NA) molecular dynamics (MD) runs into severe challenges due to the divergence of the NA coupling. At the same time, standard NAMD methods work well within each component. We develop an efficient ab initio NAMD methodology using a mixed diabatic-adiabatic representation (dNAMD), implement decoherence-induced surface hopping (DISH) within the dNAMD framework, and demonstrate its utility with long-range charge transfer in 2D perovskites taking place on nano- to microsecond time scales. The dNAMD method bypasses the trivial state crossing issue of traditional NAMD by using a diabatization technique to derive diabatic electronic coupling integrals between weakly coupled components, while employing adiabatic representation within each component. We demonstrate the approach by application to 2D perovskites, which are promising materials for optoelectronic applications, but show limited efficiencies because of the insulating nature of organic spacer cations and slow interlayer charge transport. The interlayer charge transfer time scales predicted by DISH-dNAMD are consistent with experimental data and Marcus rate constants. The simulations show that phenethylammonium spacers enhance inorganic lattice rigidity via strong hydrogen bonding and π-π stacking interactions, and reduce electron-vibrational coupling while increasing interlayer spacing and charge localization. These effects significantly reduce the electronic couplings, yielding charge transfer rates that are 1-2 orders of magnitude lower than those for the more structurally flexible butylammonium spacers. The DISH-dNAMD simulations highlight the critical role of the spacer rigidity in the interlayer charge transport of 2D perovskites. The developed dNAMD framework provides an efficient and versatile tool for simulating and elucidating excited state dynamics in weakly coupled nanoscale and condensed phase systems at the atomistic level and in the time domain as it occurs in nature and experiments, advancing the design of next-generation optoelectronic devices.

Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps.

Chemical imaging of lignocellulosic biomass: Mapping plant chemistry.

Atomistic modeling of the channeling process with and without account for ionizing collisions: A comparative study

Kinetic and atomistic modelling of biochar-mediated bioreactor systems for wastewater treatment: Deciphering the mechanistic insights, re-application and impact on environment-economics-society nexus

The object of the study is the process of pyrolysis of high-density polyethylene in the air environment. The aim of the work is to establish a qualitative and quantitative distribution of valuable and harmful gaseous products and to determine rational temperature regimes that ensure the maximum yield of valuable components and minimize the harmful impact on the environment. The study was carried out using reactive molecular dynamics (MD) modeling methods using the ReaxFF force field and specialized Materials Studio software for building and preparing an atomistic model and LAMMPS for productive modeling. The modeling methodology included the creation of a model of a HDPE polymer matrix with added air, geometric optimization, energy minimization and equilibration. Further modeling of HDPE pyrolysis was carried out in LAMMPS in a wide temperature range from 450°C to 2000°C. Based on the results of MD modeling, time dependences of the yield of key gases were constructed: H2, CO, NH3, CHN and others. Among the main components with the highest total yield, H2 (from 200 units for 450°C up to 600 for 2000°C), CHN (from 0 units for low temperatures up to 60 for 2000°C) and CO (from 30 up to 90 units depending on the temperature) were identified. Analysis of the MD modeling results showed that at 800°C there is a steady release of such gases as: H2, NH3, CHN and CO. At the same time, nitrogen-containing products are present in low concentrations (HN3 – near 0 units, H4N2 – 14, C2H7N – 10, HCN – 6), but at elevated temperatures their content increases noticeably. The temperature of 800°C is considered optimal for HDPE pyrolysis, as it ensures minimal formation of toxic molecules compared to higher temperatures, provides a higher reaction rate than at 450°C and 600°C, and reduces the energy consumption required to sustain the process

Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics?

We present GridFF, an efficient method for simulating molecules on rigid substrates, derived from techniques used in protein-ligand docking in biochemistry. By projecting molecule-substrate interactions onto precomputed spatial grids with tricubic B-spline interpolation, GridFF reduces the computational cost by orders of magnitude compared to traditional pairwise atomistic models, without compromising the accuracy of forces or trajectories. The CPU implementation of GridFF in the open-source FireCore package provides a 100-1000× speedup over all-atom simulations using LAMMPS, while the GPU implementation - running thousands of system replicas in parallel - samples millions of configurations per second, enabling an exhaustive exploration of the configuration space of small flexible molecules on surfaces within minutes. Furthermore, as demonstrated in our previous application of a similar technique to high-resolution scanning probe microscopy, GridFF can be extended beyond empirical pairwise potentials to those derived from ab initio electron densities. Altogether, this unlocks accurate high-throughput modeling of molecular self-assembly, adsorption, and scanning probe manipulation in surface science.

The scattering polarization of the infrared (IR) triplet of neutral oxygen (O i ) near 777,nm provides a powerful diagnostic of solar atmospheric conditions. However, interpreting such polarization requires a rigorous treatment of isotropic depolarizing collisions between O i atoms and neutral hydrogen. We aim to investigate the combined effects of collisional and magnetic depolarization in shaping the alignment of O i levels (and thus the polarization of the O i IR triplet). We compute, for the first time, a comprehensive set of collisional depolarization and polarization transfer rates for the relevant O i energy levels. These rates are incorporated into a multilevel atomic model, and the statistical equilibrium equations (SEE) are solved to quantify the impact of collisions and magnetic fields on atomic alignment. Our calculations indicate that elastic collisions with neutral hydrogen, together with the Hanle effect of turbulent magnetic fields stronger than about 20 G, efficiently suppress the bulk of the atomic alignment in deep photospheric conditions where hydrogen densities exceed n_ H !∼! 10^ cm^-3. In the chromosphere, however, the lower hydrogen density weakens collisional depolarization, allowing polarization to persist. Our results are consistent with a chromospheric origin for the linear polarization signals of the O i IR triplet. Future studies should combine accurate non-LTE radiative transfer with reliable collisional rates in order to achieve fully consistent modeling.

Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been applied. However, these ML models generally do not directly incorporate the experimental measurements, such as X-ray crystallographic diffraction data. To this end, we explore an approach that more tightly couples these traditional crystallographic and recent ML-based methods by training a hybrid 3D vision transformer and convolutional network on inputs from both domains. We make use of two distinct input constructs: Patterson maps, which are directly obtainable from crystallographic data, and `partial structure' template maps derived from predicted structures deposited in the AlphaFold Protein Structure Database with subsequently omitted residues. With these, we predict electron-density maps that are then post-processed into atomic models through standard crystallographic refinement processes. Introducing an initial data set of small protein fragments taken from Protein Data Bank entries and placing them in hypothetical crystal settings, we demonstrate that our method is effective at both improving the phases of the crystallographic structure factors and completing the regions missing from partial structure templates, as well as improving the agreement of the electron-density maps with the ground-truth atomic structures.

Abstract Cucurbit[7]uril molecules form non-covalent host – guest complexes with small molecular dyes. In addition, cucurbit[7]uril can also bind gold nanoparticles on gold surfaces with a 0.9 nm gap, creating plasmonic nanocavities for the dyes, with extreme confinement of the electromagnetic field. For methylene blue in such cavities, single molecule strong coupling was inferred from a complete disappearance of a characteristic shoulder in its spectrum, attributed to dimer removal. Yet, the shoulder’s origin remains debated. Using atomistic simulations, we show that it arises from both dimerization and vibronic progression. While cucurbit[7]uril binding removes the dimer contribution, vibronic progression persists. As this conflicts with previous reports, we also measured the spectra. In line with our computations, the shoulder remains visible when cucurbit[7]uril binds methylene blue. These results clarify the spectral features and pave the way for atomistic models of single-molecule strong coupling in nanoparticle-on-mirror cavities.

The electronic properties of ultra-wide band gap semiconductors, notably β-Ga2O3, have been intensively investigated for applications such as high-power electronics [1] and solar-blind photodetectors. Until recently, atomistic modelling has been mostly focused on pure β-Ga2O3, e.g., to determine its band offset and dopability[2]. Despite this effort, mechanisms for effective p-type doping of pure β-Ga2O3 remain elusive. Alloying β-Ga2O3 with Al2O3 or In2O3 provides an alternative and promising strategy for tuning mechanical and electronic properties, including lattice mismatch, band offsets in heterostructure and defect formation energies. To this end, atomistic modelling from first principles offers valuable insights that complement experimental measurements using, e.g., capacitance–voltage (C-V) characterisation or X-ray Photoelectron Spectroscopy (XPS).In the first part of this talk, first principles modelling using density functional theory (DFT) is briefly reviewed along with the main results for pure β-Ga2O3. In the second part, recent investigations of Ga2O3-based alloys are presented [3]. These investigations include the assessment of the critical thickness for epitaxially grown in the (100B), (010), (001B), and (-201) directions and the formation energies of substitutional donors (Si, Sn, C, Ge, Ta, Zr, Hf) and acceptors (Mg, Zn, Cu). Accurate band offsets from first principles calculations are also combined with Technology Computer-Aided Design (TCAD) modelling, showing good agreement with experimental results in . Our findings underscore the importance of growth orientation, strain, and substitutional impurities investigation for Ga2O3-based alloys for precise control of band offset and defect formation.[1] M. J. Tadjer, “Toward gallium oxide power electronics,” Science, vol. 378, no. 6621, pp. 724–725, 2022[2] M. D. McCluskey, “Point defects in Ga2O3,” Journal of Applied Physics, vol. 127, p. 101101, 03 2020[3] M. A. Fadla, M. Gruning, and L. Stella, “Effective band structure and crack formation analysis in pseudomorphic ¨ epitaxial growth of (InxGa1−x)2O3 alloys: A first-principles study,” ACS omega, vol. 9, no. 13, pp. 15320–15327, 2024.

A priori and reliable simulations can enable timely and cost-efficient design and discovery of materials for energy. Therefore, ‘Compute first’ is an optimal approach to initialize modern day R&D processes. In energy storage, beyond lithium-ion (BLI) research has the potential to revolutionize consumer electronics including portable and stationary power, transportation, and grid energy storage sectors. Multi-valent (Mg, Ca, Zn) energy storage or economically viable monovalent (Na, K) batteries, high-density metal-air, metal-sulfur batteries, or grid-storage systems are considered in the beyond lithium-ion research and development. All these R&D efforts require significant fundamental knowledge via a priori computations for materials discovery, property prediction, and optimization. Atomistic modeling when coupled with reliable Artificial Intelligence (AI) approaches can provide accurate insights to accelerate discovery of optimal electrolytes, electrodes, and membranes for BLI systems to reduce the cost. Thus, coupled with AI and multi-scale simulations techniques, atomistic modeling can address prediction of molecular level properties of materials (redox potentials, solvation, spectroscopic, diffusion, and reactivity) to down-select optimal materials or material combinations. In this presentation, I will describe some of our recent published efforts in active learning coupled with first principles simulations to down select/optimize desired materials for energy storage. This concept can be utilized for design of experiments using autonomous experimentation.

Solvent morphological- and dipol-driven collective behavior of acetonitrile (AN) and dimethoxyethane (DME) molecules in the solvation structure are studied with a first principle modelling approach. The study extends the previous findings on acetonitrile molecule’s stability that differs depending on the position (free or bound in the solvation sheath, with or without anion species around), originated from the electrostatic degradation. As the previous study suggests, for a successful application of conventional electrolyte system into Mg-ion battery system, solvation structure tailoring is one of the key solutions. Hereby, DME is proposed as a co-solvent due to its complementary chemical and physical properties with AN. Atomistic modeling is performed on Mg(TFSI)₂ electrolyte solvation structures with varied AN/DME ratios to assess solvation behavior.A systematic modeling approach, incorporating molecular relaxation criteria, explores over 40 solvation structures. Results indicate that a 3:1 AN:DME ratio enhances solvation stability and promotes effective dissociation of TFSI⁻ from Mg²⁺. Charge analysis on each ligand around Mg2+ cation is conducted from an intermolecular dipol perspective. The efficacy of this finding is tested experimentally, linking the anion dissociation capability of the co-solvent system to the cycling performance of Mg-ion batteries, which confirms improved cycling performance with the 3:1 AN:DME electrolyte. The result is attributed to the direct effect of optimal electrolyte kinetics among the target systems, which in turn enables the consistent initiation of Mg plating/stripping processes.To further confirm the electrostatic stability of solvent species, previously established DFT methodology is used. Relaxed solvation structures are directly integrated into the workflow of Mg-based alloying anode interface model. These findings highlight the potential of co-solvent engineering to optimize Mg-ion battery performance.

Multivariate Metal-Organic Frameworks (MTV-MOFs) have recently garnered attention as ultrafast lithium ion (Li⁺) conductors, showing potential as all-solid-state battery electrolytes due to their mechanical stability, cost-effective processing, and conductive properties. However, their application in full cell or cyclic cell batteries is limited by issues with unstable or conductive solid electrolyte interphases (SEI). In this study, we demonstrate that UiO-66 functionalized with ether-based linker groups can immobilize anions, enhancing the Li⁺ transference number and achieving improved conductivity in cyclic cells, reaching up to 0.23 mS/cm at room temperature. Despite these advances, the molecular mechanisms behind Li⁺ transport in MTV-MOF electrolytes remain poorly understood. Using a combination of atomistic simulations, quantum mechanics, and molecular modeling, we identify three distinct ion-hopping mechanisms—linker-linker hopping, linker-counterion hopping, and counterion-counterion hopping—that drive Li⁺ conduction within the MOF. These mechanisms are influenced by counterion distribution, linker binding strength, and entropic factors such as linker variability. To explore how different ether-based linker topologies affect Li⁺ binding, classical molecular dynamics (MD) simulations were conducted with various MOFs at a fixed ratio of LiTFSI salt, matching experimental conditions. MD simulations revealed a reduced affinity of Li⁺ for the TFSI⁻ anion, favoring interactions with linker groups, while density functional theory (DFT) calculations indicated that specific linker groups exhibit higher Li⁺ binding affinity, providing additional hopping sites. The role of ion channels in Li⁺ transport was further examined in UiO-66, which possesses octahedral and tetrahedral pores critical to conductivity. Our analysis showed that low-conductivity MOFs rely predominantly on counterion-counterion hopping, while high-conductivity MOFs exhibit all three hopping mechanisms. Enhanced sampling techniques such as metadynamics revealed that linker groups offering hopping sites create local minima on the free energy landscape. Our computational findings align with experimental data, highlighting the importance of structure-property relationships in achieving ultrafast Li⁺ conductivity. Figure 1

The atomic structure of the electrode surface in electrochemical environments has been widely investigated using in situ structural techniques including in-situ Scanning Tunneling Microscopy (STM) and surface X-ray scattering (SXS). The two methods are complementary in such that STM probes local structure directly whereas SXS gives access to the extended surface structure through probing of the reciprocal space. However, recent advances in SXS have facilitated insight into the charge distribution, the structure of the electrolyte at the interface and time resolved structural development.Advances in these directions offer possibilities in elucidating atomic scale models of the electrochemical interface and thus will help to establish structure-stability-reactivity relationships and to understand growth kinetics and electrochemical phase formation.Examples of how the use of surface x-ray scattering techniques can help to characterize electrochemical interfaces in-situ in order to link, structure, reactivity and stability will be presented. [1-3][1] Y. Grunder et al., Charge Reorganization at the Adsorbate Covered Electrode Surface Probed through in Situ Resonant X-ray Diffraction Combined with ab Initio Modeling; Phys. Chem. C 2022, 126, 9, 4612–4619[2] Y. Soldo-Olivier et al., Unraveling the Charge Distribution at the Metal-Electrolyte Interface Coupling in Situ Surface Resonant X-Ray Diffraction with Ab Initio Calculations, ACS Catal. 2022, 12, 4, 2375–2380[3] Y. Grunder et al., Dynamics of potential-induced structural changes at the Ag(111)/alkaline interface, Electrochem. Sci. Adv. 2025, 5, e2400009.