xxMD: Benchmarking Neural Force Fields Using Extended Dynamics beyond Equilibrium

Neural force fields (NFFs) have gained prominence in computational chemistry as surrogate models, superseding quantum-chemistry calculations in ab initio molecular dynamics. The prevalent benchmark for NFFs has been the MD17 dataset and its subsequent extension. These datasets predominantly comprise geometries from the equilibrium region of the ground electronic state potential energy surface, sampling from direct adiabatic dynamics. However, many chemical reactions entail significant molecular deformations, notably bond breaking. We demonstrate the constrained distribution of internal coordinates and energies in the MD17 datasets, underscoring their inadequacy for representing systems undergoing chemical reactions. Addressing this sampling limitation, we introduce the xxMD (Extended Excited-state Molecular Dynamics) dataset, derived from non-adiabatic dynamics. This dataset encompasses energies and forces ascertained from both multireference wave function theory and density functional theory. Furthermore, its nuclear configuration spaces authentically depict chemical reactions, making xxMD a more chemically relevant dataset. Our re-assessment of equivariant models on the xxMD datasets reveals notably higher mean absolute errors than those reported for MD17 and its variants. This observation underscores the challenges faced in crafting a generalizable NFF model with extrapolation capability. Our proposed xxMD-CASSCF and xxMD-DFT datasets are available at \url{https://github.com/zpengmei/xxMD}.Comment: 19 pages, many figures. Data available at \url{https://github.com/zpengmei/xxMD

Dissociation Time, Quantum Yield, and Dynamic Reaction Pathways in the Thermolysis of <i>trans</i>-3,4-Dimethyl-1,2-dioxetane

The thermolysis of trans-3,4-dimethyl-1,2-dioxetane is studied by trajectory surface hopping. The significant difference between long and short dissociation times is rationalized by frustrated dissociations and the time spent in triplet states. If the C–C bond breaks through an excited state channel, then the trajectory passes over a ridge of the potential energy surface of that state. The calculated triplet quantum yields match the experimental results. The dissociation half-times and quantum yields follow the same ascending order as per the product states, justifying the conjecture that the longer dissociation time leads to a higher quantum yield, proposed in the context of the methylation effect. The populations of the molecular Coulomb Hamiltonian and diagonal states reach equilibrium, but the triplet populations with different Sz components fluctuate indefinitely. Certain initial velocities, leading the trajectories to given product states, can be identified as the most characteristic features for sorting trajectories according to their product states

Anthrax Lethal Toxin Suppresses Murine Cardiomyocyte Contractile Function and Intracellular Ca2+ Handling via a NADPH Oxidase-Dependent Mechanism

OBJECTIVES: Anthrax infection is associated with devastating cardiovascular sequelae, suggesting unfavorable cardiovascular effects of toxins originated from Bacillus anthracis namely lethal and edema toxins. This study was designed to examine the direct effect of lethal toxins on cardiomyocyte contractile and intracellular Ca(2+) properties. METHODS: Murine cardiomyocyte contractile function and intracellular Ca(2+) handling were evaluated including peak shortening (PS), maximal velocity of shortening/ relengthening (± dL/dt), time-to-PS (TPS), time-to-90% relengthening (TR(90)), intracellular Ca(2+) rise measured as fura-2 fluorescent intensity (ΔFFI), and intracellular Ca(2+) decay rate. Stress signaling and Ca(2+) regulatory proteins were assessed using Western blot analysis. RESULTS: In vitro exposure to a lethal toxin (0.05-50 nM) elicited a concentration-dependent depression on cardiomyocyte contractile and intracellular Ca(2+) properties (PS, ± dL/dt, ΔFFI), along with prolonged duration of contraction and intracellular Ca(2+) decay, the effects of which were nullified by the NADPH oxidase inhibitor apocynin. The lethal toxin significantly enhanced superoxide production and cell death, which were reversed by apocynin. In vivo lethal toxin exposure exerted similar time-dependent cardiomyocyte mechanical and intracellular Ca(2+) responses. Stress signaling cascades including MEK1/2, p38, ERK and JNK were unaffected by in vitro lethal toxins whereas they were significantly altered by in vivo lethal toxins. Ca(2+) regulatory proteins SERCA2a and phospholamban were also differentially regulated by in vitro and in vivo lethal toxins. Autophagy was drastically triggered although ER stress was minimally affected following lethal toxin exposure. CONCLUSIONS: Our findings indicate that lethal toxins directly compromised murine cardiomyocyte contractile function and intracellular Ca(2+) through a NADPH oxidase-dependent mechanism

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations

xxMD

&lt;p&gt;xxMD datasets involves geometries and gradients for several molecules from nonadiabatic dynamics. These geometries are useful in benchmarking machine learning force field for photochemical reactions.&nbsp;&lt;/p&gt

Beyond MD17: The reactive xxMD dataset

System specific neural force fields (NFFs) have gained popularity in computational chemistry. One of the most popular datasets as a bencharmk to develop NFF models is the MD17 dataset and its subsequent extension. These datasets comprise geometries from the equilibrium region of the ground electronic state potential energy surface, sampled from direct adiabatic dynamics. However, many chemical reactions involve significant molecular geometrical deformations, for example, bond breaking. Therefore, MD17 is inadequate to represent a chemical reaction. To address this limitation in MD17, we introduce a new dataset, called Extended Excited-state Molecular Dynamics (xxMD) dataset. The xxMD dataset involves geometries sampled from direct nonadiabatic dynamics, and the energies are computed at both multireference wavefunction theory and density functional theory. We show that the xxMD dataset involves diverse geometries which represent chemical reactions. Assessment of NFF models on xxMD dataset reveals significantly higher predictive errors than those reported for MD17 and its variants. This work underscores the challenges faced in crafting a generalizable NFF model with extrapolation capability

First-Principles Study of Nonradiative Recombination in Silicon Nanocrystals: The Role of Surface Silanol

The photophysical properties of emissive silicon nanomaterials depend strongly on the chemical composition and structure of their surfaces, and the development of a causal, microscopic understanding of this relationship is highly desirable. One surface-dependent property of interest is the propensity for nonradiative recombination (NRR). In this work, we apply ab initio theoretical methods to investigate the mechanism of NRR in a silicon nanocrystal with a single surface silanol group. Ab initio multiple spawning simulations of an electronically excited cluster model (Si₇H₁₁OH) indicate ultrafast nonradiative decay to the electronic ground state. A multireference electronic structure study demonstrates that this nonradiative decay occurs near conical intersections between the ground and first excited electronic states of the cluster. These intersections are accessed after stretching of the bond between the silanol silicon atom and an adjacent silicon atom. The presence of this intersection in a true nanomaterial is confirmed by optimization of a similar conical intersection in a silicon nanocrystal (oblate, major diameter 1.4 nm, minor diameter 1.0 nm) with a silanol group on the surface (Si₄₄H₄₅OH). This intersection was identified using a graphics processing unit accelerated implementation of the configuration interaction singles natural orbital complete active space configuration interaction method. All intersections identified in this work are predicted to be at least 4.3 eV above the ground state minimum energy. This confirms the widely held view that silanol groups do not introduce efficient pathways for nonradiative recombination of excitons created upon absorption of visible light. That such an assignment is made entirely from first-principles underscores the value of conical intersection optimization as a tool for elucidating semiconductor photophysics

Do Excited Silicon–Oxygen Double Bonds Emit Light?

Oxidation of the surface of well-passivated silicon nanocrystals introduces defects which dramatically affect the optical properties of the material. One such defect is the silicon–oxygen double bond, which has been implicated as the source of the unusual particle-size-independent S-band photoluminescence of oxidized silicon nanocrystals. Herein, we investigate the photodynamics of this defect by application of a first-principles nonadiabatic molecular dynamics approach to a cluster model containing a silicon–oxygen double bond. Upon excitation, pyramidalization occurs about the double-bonded silicon atom, leading to a conical intersection between the ground and first excited state. This conical intersection facilitates nonradiative decay, resulting in the internal conversion of 7% of the excited population in the first picosecond after excitation. Extrapolation to longer times suggests that nonradiative decay via conical intersection proceeds faster than the microsecond photoluminescence lifetime of silicon nanocrystals and thus that silicon–oxygen double bonds are unlikely to be responsible for the experimentally observed emission

Nonradiative Recombination via Conical Intersections Arising at Defects on the Oxidized Silicon Surface

Nonradiative recombination of excitations in semiconductors limits the performance of photovoltaics, light-emitting diodes, photocatalysts, and other devices. Herein we investigate the role that two known defects on the oxidized surface of silicon play in nonradiative recombination in silicon nanocrystals. We apply ab initio multiple spawning and multireference electronic structure methods to model the nonradiative processes which follow excitation of two cluster models of silicon epoxide defects that differ in the oxidation state of their respective silicon atoms. We find conical intersections in both clusters, and these intersections are found to be accessible at energies corresponding to visible wavelengths. In both cases, photochemical opening of the epoxide ring precedes nonradiative decay. These results support the hypothesis that conical intersections associated with specific defect structures on the oxidized surface of silicon nanocrystals facilitate nonradiative recombination. Discussion regarding how this hypothesis can be tested experimentally is presented

Parametrically Managed Activation Function for a Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential

Machine-learned representations of potential energy surfaces generated in the output layer of a feedforward neural network are becoming increasingly popular. One difficulty with neural-network output is that it is often unreliable in regions where training data is missing or sparse. Human-designed potentials often build in proper extrapolation behavior by choice of functional form. Because machine learning is very efficient, it is desirable to learn how to add human intelligence to machine-learned potentials in a convenient way. One example is the well understood feature of interaction potentials that they vanish when subsystems are too far separated to interact. In this article, we present a way to add a new kind of activation function to a neural network to enforce low-dimensional constraints. In particular the activation function depends parametrically on all the input variables. We illustrate the use of this step by showing how it can force an interaction potential to go to zero at large subsystem separations with either inputting a specific functional form for the potential or adding data to the training set in the asymptotic region of geometries where the subsystems are separated. In the process of illustrating this, we present an improved set of potential energy surfaces for the 14 lowest 3A´ states of O3. The method is more general than this example, and it may be used to add other low-dimensional knowledge or lower-level knowledge to machine-learned potentials. In addition to the O3 example, we present a greater-generality method called parametrically managed diabatization by deep neural network (PM-DDNN) that is an improvement on our previously presented permutationally restrained diabatization by deep neural network (PR-DDNN)