31 research outputs found
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
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
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 (Si<sub>7</sub>H<sub>11</sub>OH) 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 (Si<sub>44</sub>H<sub>45</sub>OH). 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)
Nonadiabatic Dynamics Algorithms with Only Potential Energies and Gradients: Curvature-Driven Coherent Switching with Decay of Mixing and Curvature-Driven Trajectory Surface Hopping
Direct dynamics by mixed quantum–classical nonadiabatic methods is an important tool for understanding processes involving multiple electronic states. Very often, the computational bottleneck of such direct simulation comes from electronic structure theory. For example, at every time step of a trajectory, nonadiabatic dynamics requires potential energy surfaces, their gradients, and the matrix elements coupling the surfaces. The need for the couplings can be alleviated by employing the time derivatives of the wave functions, which can be evaluated from overlaps of electronic wave functions at successive timesteps. However, evaluation of overlap integrals is still expensive for large systems. In addition, for electronic structure methods for which the wave functions or the coupling matrix elements are not available, nonadiabatic dynamics algorithms become inapplicable. In this work, building on recent work by Baeck and An, we propose new nonadiabatic dynamics algorithms that only require adiabatic potential energies and their gradients. The new methods are named curvature- driven coherent switching with decay of mixing (κCSDM) and curvature-driven trajectory surface hopping (κTSH). We show how powerful these new methods are in terms of computer time and good agreement with methods employing nonadiabatic coupling vectors computed in conventional ways. The lowering of the computational cost will allow longer nonadiabatic trajectories and greater ensemble averaging to be affordable, and the ability to calculate the dynamics without electronic structure coupling matrix elements extends the dynamics capability to new classes of electronic structure methods
Dual-Functional Tamm–Dancoff Approximation with Self-Interaction-Free Orbitals: Vertical Excitation Energies and Potential Energy Surfaces near an Intersection Seam
Recently we have developed the dual-functional
Tamm–Dancoff
approximation (DF-TDA) method. DF-TDA is an alternative to linear-response
time-dependent density functional theory (LR-TDDFT) with the advantage
of providing a correct double-cone topology of S<sub>1</sub>/S<sub>0</sub> conical intersections. In the DF-TDA method, we employ different
functionals, which are denoted G and F, for orbital optimization and
Hamiltonian construction. We use the notation DF-TDA/G:F. In the current
work, we propose that G be the same as F except for having 100% Hartree–Fock
exchange. We use the notation F100 to denote functional F with this
modification. A motivation for this is that functionals with 100%
Hartree–Fock exchange are one-electron self-interaction-free.
Here we validate the use of F100/M06 to compute vertical excitation
energies and the global potential energy surface of ammonia near a
conical intersection to further validate the F100 method for photochemical
problems