– Fernando Rojas Íñiguez – Director Centro de Nanociencias y Nanotecnología, UNAM.

AI-driven Fully Quantum (Bio)Molecular Simulations: From Dream to Reality

– Alex Tkatchenko – University of Luxembourg

Abstract

The convergence between accurate quantum-mechanical (QM) models (and codes) with efficient machine learning (ML) methods seem to promise a paradigm shift in molecular simulations. Many challenging applications are now being tackled by increasingly powerful QM/ML methodologies. These include modeling covalent materials, molecules, molecular crystals, surfaces, and even whole proteins in explicit water (https://www.science.org/doi/abs/10.1126/sciadv.adn4397). In this talk, I attempt to provide a reality check on these recent advances and on the developments required to enable fully quantum dynamics of complex functional (bio)molecular systems. Multiple challenges are highlighted that should keep theorists in business for the foreseeable future:

(1) Ensuring the accuracy of high-level QM methods (https://doi.org/10.1038/s41467-021-24119-3; https://doi.org/10.1038/s41586-023-06587-3), 

(2) Describing intricate QM long-range interactions (https://doi.org/10.1126/sciadv.aax0024; https://doi.org/10.1126/science.aae0509; https://doi.org/10.1103/PhysRevLett.128.106101), 

(3) Treating quantum electrodynamic effects that become relevant for complex molecules (https://doi.org/10.1021/acs.jpclett.1c04222; https://doi.org/10.1103/PhysRevResearch.4.013011).

(4) Developing increasingly accurate, efficient, scalable, and transferable ML architectures for molecules and materials (https://doi.org/10.1038/s41467-022-31093-x; https://arxiv.org/abs/2209.14865; https://arxiv.org/abs/2209.03985).

(5) Accounting for the quantum nature of the nuclei and the influence of external environments (https://doi.org/10.1038/s41467-020-20212-1; https://doi.org/10.1038/s41467-022-28461-y).

I argue that only a conjunction of all these developments will enable the long-held dream of fully quantum (bio)molecular simulations.

Strategies to boost electrical conductivity in porous materials: a computational perspective

– Joaquin Calbo – Universitat de València

Abstract

Despite being traditionally considered as insulators, (metal)-organic frameworks are porous crystalline structures that have proved to be efficient for the transport of electrical charge upon suitable chemical design for next-generation applications.[1] Among the most promising strategies to infuse charge conductivity in porous crystals, we emphasize the π-π stacking of electroactive ligands, the incorporation of guest molecules within the pores, or the exploitation of mixed valency in chemically available metal redox pairs. These porous conductors are meant to unlock new avenues in the design of novel materials for efficient energy conversion and storage.[2]

Herein, we showcase the theoretical modelling of advanced porous materials that take advantage of each of these chemical strategies to boost charge conductivity. First, the charge transport of a series of purely organic porous frameworks based on H-bonding (HOFs) is described in terms of a through-space hopping of charge carriers between stacked electroactive TTF-based ligands in a zwitterionic form (Figure 1a).[3] Second, one of the first examples of perylene-based MOFs with semiconductivity is presented based on the partial oxidation of the ligand upon inclusion of I2 dopant, and promoted by a herringbone CH···π perylene-perylene stacking (Figure 1b).[4] Last, the record 3D conducting MOF based on Fe(II)/Fe(III) redox pair and tetrazole ligand Fe2(BDT)3 (Figure 1c)[5] is characterized theoretically, and compared with its polymorphs, to unveil the origin of its striking high charge transport.

Figure 1. Crystal structure of a) TTF-based MUV-20a, b) perylene-based Per-MOF, and c) tetrazole-based Fe2(BDT)3 frameworks.

References

(1) Calbo, J.; Golomb, M. J.; Walsh, A. J. Mater. Chem. A 2019, 7, 16571-16597.

(2) Xie, L. S.; Skorupskii, G.; Dincă, M. Chem. Rev. 2020, 120, 8536–8580.

(3) Vicent-Morales, M.; Esteve-Rochina, M.; Calbo, J.; Ortí, E.; Vitórica-Yrezábal, I. J.; Mínguez Espallargas, G. J. Am. Chem. Soc. 2022, 144, 9074–9082.

(4) Valente, G.; Esteve-Rochina, M.; Paracana, A.; Rodríguez-Diéguez, A.; Choquesillo-Lazarte, D.; Ortí, E.; Calbo, J.; Ilkaeva, M.; Mafra, L.; Hernández-Rodríguez, M. A.; Rocha, J.; Alves, H.; Souto, M. Mol. Syst. Des. Eng. 2022, 7, 1065-1072.

(5) Xie, L. S.; Sun, L.; Wan, R.; Park, S. S.; DeGayner, J. A.; Hendon, C. H.; Dinca, M. J. Am. Chem. Soc. 2018, 140, 7411-7414.

Elevating the Accuracy of Density Functional Theory for Condensed Phase Simulations through Machine Learning and Many-Body Techniquesvited

– Saswata Dasgupta – University of California, San Diego

Abstract

Accurately depicting the electronic structure and dynamics of condensed phases is crucial for understanding their phase behavior and reactivity. Among the quantum chemistry techniques, Density Functional Theory (DFT) is extensively employed to model the properties of condensed-phase systems, despite its high computational demands that hinder the use of more accurate modern density functionals. To address this, we introduce innovative data-driven many-body potential energy functions trained with arbitrary DFT functionals (MB-DFT), which preserve the accuracy of the parent functionals consistently from gas to condensed phases. Despite these advancements, no existing density functional has achieved the necessary accuracy to predict the properties of hydrated systems across their full phase diagram. To bridge this gap, we present the Density-Corrected SCAN (DC-SCAN) method that elevates the accuracy of the SCAN functional to the level of coupled-cluster theory, the gold standard for chemical accuracy. This method allows for accurate descriptions of aqueous systems from gas to condensed phases within our data-driven many-body DFT framework. However, like most polarizable force fields, these MB-DFT potentials cannot participate in chemical reactions. To model chemical reactions in the condensed phase, we have combined the accuracy of DC-SCAN with the capabilities of neural network potentials. This integration has resulted in the development of reactive machine-learned potentials capable of studying phenomena such as water autoionization and proton transport within confinement, facilitated by advanced enhanced-sampling techniques. Our findings provide not only an accurate estimation of the autoionization constant but also emphasize the significant role of the Grotthuss mechanism in acid/base equilibria.

Molecular Simulations of Extraterrestrial Organic Minerals

– Richard Remsing – Rutgers University

Abstract

Titan, Saturn’s largest moon, has a thick atmosphere rich in organic molecules and a cold surface temperature of 94 K. As a result, many of the organic molecules produced by photochemistry in the atmosphere condense onto the surface as organic liquids and solids. The resulting crystalline organic solids coat Titan’s surface and are referred to as cryominerals, in analogy to minerals on Earth. Cryominerals can play important roles in Titan’s surface geology, geochemistry, and even potential prebiotic chemistry. To fully understand chemical processes in these unique environments, a molecular-level picture is needed, which we are working towards using a combination of ab initio simulations and machine learning models. I will discuss our recent work modeling structure and dynamics in several Titan cryominerals, including the potential for these solids to exist in plastic crystal phases, in which molecules are translationally ordered but rotationally disordered. The disorder present in plastic crystals generally impacts important thermodynamic and mechanical properties relevant to surface processes, such as reducing elastic moduli. I will also discuss the implications of our work for understanding the properties of Titan’s surface and for understanding driving forces for prebiotic chemistry in Titan-like non-aqueous environments.

Invited 4

– Author – Adscription

Abstract

Here goes the abstract

Plenary 2

– Marivi Fernandez-Serra – Stony Brook University

Abstract

Here goes the abstract

Plenary 5

– Jorge Sofo – PennState University

Abstract

Here goes the abstract

Current vortices in aromatic carbon molecules

– Yenny Priscila Ortiz Acero – Universidad de Barcelona

Abstract

The local current flow through three small aromatic carbon molecules, namely, benzene, naphthalene, and anthracene, is studied. Applying density functional theory and the nonequilibrium Green’s function method for transport, we demonstrate that pronounced current vortices exist at certain electron energies for these molecules. The intensity of these circular currents, which appear not only at the antiresonances of the transmission but also in the vicinity of its maxima, can exceed the total current flowing through the molecular junction and generate considerable magnetic fields. The π electron system of the molecular junctions is emulated experimentally by a network of macroscopic microwave resonators. The local current flows in these experiments confirm the existence of current vortices as a robust property of ring structures. The circular currents can be understood in terms of a simple nearest-neighbor tight-binding Hückel model. Current vortices are caused by the interplay of the complex eigenstates of the open system which have energies close to the considered electron energy. Degeneracies, as observed in benzene and anthracene, can thus generate strong circular currents, but also nondegenerate systems like naphthalene exhibit current vortices. Small imperfections and perturbations can couple otherwise uncoupled states and induce circular currents.

Invited 10

– Andreas Goetz – University of California San Diego

Abstract

Here goes the abstract

Spin Topology in Insulating Materials

– Rafael Gonzalez-Hernandes – Universidad del Norte

Abstract

In this work, we study the role of electron spin in establishing topological invariants in insulating materials. We explore how the spin valence operator, induced by the spin operator, provides crucial information about spin Chern class phases. Gapped spin valence spectra denote constant spin Chern numbers, defining materials as spin Chern insulators [1]. Conversely, changes in spin Chern numbers occur at zero spin spectrum values, characterizing materials as spin Weyl topological insulators [2]. We also introduce the average spin Chern number [3] to classify non-trivial spin transport properties in 3D topological insulators, which closely correlates with the spin Hall conductivity within the
bandgap.

[1] E. Prodan, Phys. Rev. B 80, 125327 (2009), https://link.aps.org/doi/10.1103/PhysRevB.80.125327
[2] R. González-Hernández and B. Uribe, Phys. Rev. B 109, 045126 (2024),
https://link.aps.org/doi/10.1103/PhysRevB.109.045126.
[3] R. González-Hernández and B. Uribe, arxiv: 2404.08595 (2024).
https://doi.org/10.48550/arXiv.2404.08595.

Invited 12

– Author – Adscription

Abstract

Here goes the abstract

Experimental and Computational Studies of 2D Carbides and Carbonitrides (MXenes)

– Yury Gogotsi – Drexel University

Abstract

2D carbides, nitrides, and carbonitrides of early transition metals known as MXenes are among the few nanomaterials that have jumped into the limelight not only because of their exotic structure or attractive properties but also because of numerous practical applications [1]. The family of MXenes has  been expanding rapidly since the discovery of Ti3C2 at Drexel University in 2011. Close to 50 different stoichiometric MXenes, dozens of solid solutions, high-entropy materials and MXenes with various surface terminations have been reported. The structure and properties of numerous other MXenes have been predicted by DFT. We are working to synthesize some of the predicted MXene compositions. The availability of solid solutions on M and X sites, multi-element high-entropy MXenes, control of surface terminations, and the discovery of out-of-plane ordered double-M o-MXenes (e.g., Mo2TiC2), as well as in-plane ordered i-MAX phases and their i-MXenes offer the potential for producing an infinite number of new 2D materials. This presentation will describe the state of the art in the field of MXene synthesis [2,3]. Synthesis-structure-properties relations of MXenes will be described [4]. The versatile chemistry of the MXene family renders their properties tunable for a large variety of energy-related, electronic, optical, biomedical, and other applications. In particular, the applications of MXenes in electrochemical energy storage and harvesting, electrocatalytic water splitting and water purification/desalination are promising [1]. However, MXene antennas, sensors, actuators, as well as coatings for EMI shielding and thermal regulation are equally attractive. 

1. A. VahidMohammadi, J. Rosen, Y. Gogotsi, The World of Two-Dimensional Carbides and Nitrides (MXenes), Science, 372, eabf1581 (2021).
2. H. Ding, Y. et al, Chemical-scissor-mediated structural editing of layered transition metal carbides, Science, 379, 1130–1135 (2023).
3. H. Shao, S. Luo, A. Descamps-Mandine, K. Ge, Z. Lin, P.-L. Taberna, Y. Gogotsi, P. Simon, Synthesis of MAX phase nanofibers and nanoflakes and the resulting MXenes, Advanced Science, 10 (1) 2205509 (2023).
4. K. R. G. Lim, M. Shekhirev, B. C. Wyatt, B. Anasori, Y. Gogotsi, Z. W. Seh, Fundamentals of MXene synthesis, Nature Synthesis, 1 (8) 601-614 (2022).