Accurate Ab Initio Research Articles

Accurate Ab Initio Investigation of Electronic and Radiative Properties, and Cross Sections for Charged Diatomic Systems FrLi+ and FrNa.

This paper presents an extensive ab initio investigation of the electronic properties and elastic collisions of charged diatomic systems FrLi+ and FrNa+. We employ an accurate ab initio approach using nonempirical pseudopotentials for Li+, Na+, and Fr+ cores. We calculate the potential energy curves of the ground state and low-lying excited states of 2Σ+, 2Π, and 2Δ symmetries, identifying and interpreting avoided crossings between higher 2Σ+ and 2Π states. The spectroscopic parameters, transition dipole moments, and vibrational energies associated with 1-32Σ+ states are calculated, along with the radiative lifetimes of the vibrational states trapped in the 22Σ+ state. Using accurate potential energy data, we evaluate partial and total cross sections across a wide range of energies. At low energies (<1 mK), the elastic cross section follows the Wigner law threshold comportment, while at high energies, it scales as E-1/3. To the best of our knowledge, no theoretical or experimental data have been collected on these charged diatomic systems until now. Hence, we proceeded to analyze our findings by comparing them with data acquired from comparable systems. The information pertaining to electronic structures, spectroscopic parameters, transition properties, and collision data provided in this work is expected to serve as a valuable guideline for future theoretical and experimental research on each considered system.

Modelling electrified microporous carbon/electrolyte electrochemical interface and unraveling charge storage mechanism by machine learning accelerated molecular dynamics

Ion storage in electric double layer (EDL) of microporous carbon (< 1 nm) has been demonstrated to be a partially desolvated structure, leading to a capacitance increase. Due to the relevance of the interface structure to capacitance behavior and charge storage mechanism, it is critical to give a deep insight into the micropore/electrolyte interface at the molecular scale. Ab initio molecular dynamics (AIMD) simulation can describe the electronic structure and dynamic properties of the EDL. However, the complex EDL is hard to be well equilibrated at the limited time scale of the AIMD simulation. Here, we have performed machine learning force field accelerated molecular dynamics (MLMD) to construct the EDL of microporous carbon at different electrode potentials with a much lower cost while keeping ab initio accuracy. Based on the MLMD with a longer time scale, we have clarified the microscopic information of the interface, such as the distributions of ions and water molecules. By simulating the Na+ intercalation process, the critical role of the electrode potential on ion storage in pore is clarified. In addition, according to the MLMD simulation on micropore/electrolyte EDLs with different pore sizes, the pore size of ∼0.65 nm is appropriate for desolvation effect due to the match between the pore size and the solvated ion size. These theoretical results provide a way for studying micropore/electrolyte EDL by MLMD and improving the electrochemical performance of microporous carbon electrodes.

Machine learning potential for Ab Initio phase transitions of zirconia

Zirconia has been extensively used in aerospace, military, biomedical and industrial fields due to its unusual combination of high mechanical, electrical and thermal properties. However, the fundamental and critical phase transition process of zirconia has not been well studied because of its difficult first-order phase transition with formidable energy barrier. Here, we generated a machine learning interatomic potential with ab initio accuracy to discover the mechanism behind all kinds of phase transition of zirconia at ambient pressure. The machine learning potential precisely characterized atomic interactions among all zirconia allotropes and liquid zirconia in a wide temperature range. We realized the challenging reversible first-order monoclinic-tetragonal and cubic-liquid phase transition processes with enhanced sampling techniques. From the thermodynamic information, we gave a better understanding of the thermal hysteresis phenomenon in martensitic monoclinic-tetragonal transition. The phase diagram of zirconia from our machine learning potential based molecular dynamics simulations corresponded well with experimental results.

Crystal structure prediction at finite temperatures

Crystal structure prediction is a central problem of crystallography and materials science, which until mid-2000s was considered intractable. Several methods, based on either energy landscape exploration or, more commonly, global optimization, largely solved this problem and enabled fully non-empirical computational materials discovery. A major shortcoming is that, to avoid expensive calculations of the entropy, crystal structure prediction was done at zero Kelvin, reducing to the search for the global minimum of the enthalpy rather than the free energy. As a consequence, high-temperature phases (especially those which are not quenchable to zero temperature) could be missed. Here we develop an accurate and affordable solution, enabling crystal structure prediction at finite temperatures. Structure relaxation and fully anharmonic free energy calculations are done by molecular dynamics with a forcefield (which can be anything from a parametric forcefield for simpler cases to a trained on-the-fly machine learning interatomic potential), the errors of which are corrected using thermodynamic perturbation theory to yield accurate results with full ab initio accuracy. We illustrate this method by applications to metals (probing the P–T phase diagram of Al and Fe), a refractory covalent solid (WB), an Earth-forming silicate MgSiO3 (at pressures and temperatures of the Earth’s lower mantle), and ceramic oxide HfO2.

Machine learning and DFT-based combined framework for predicting transmission spectra of quantum-confined bio-molecular nanotube.

The Adenine-based nanotube is theoretically designed, and its transmission spectra are investigated. The quantum-confined Adenine nanotube shows electronic transmission of the carrier at minimum stress. In this paper, the prediction of transmission spectra of the quantum-confined bio-molecular nanotube is investigated and deeply studied. Molecular level structure prediction and their electronic characterization can be possible with ab initio accuracy using a machine learning algorithmic approach. At the molecular level, it is difficult to predict quantum transmission spectra as these results are always hampered by the carrier backscattering effect. However, mostly these predictive models are available for intrinsic semi-conducting materials and other inorganic structures. Machine learning algorithms are designed to predict the electronic properties of the nano-scale structure. This task is even more difficult when quantum-confined molecular arrangements are considered, whose transmission spectra are sensitive to the confinements applied. This paper presents an effective machine learning algorithms framework for predicting transmission spectra of quantum-confined nanotubes from their geometries. In this paper, we consider regression machine learning algorithms to find maximum accuracy with varying configurations and geometries to excerpt their atoms' local environment information. The Hamiltonian components are then used to enable the utilization of the information to predict the electronic structure at any arbitrary sampling point or k-point. The theoretical basics introduced in this process help to capture and incorporate minor changes in quantum confinements into transmission spectra and provide the framework algorithm with more accuracy. This paper shows the ability to predict the accurate algorithmic models of the Adenine nanotube. In this framework, we have considered a tiny data set to achieve a rapid and reliable method for electronic structure determination and also propose the best algorithm for predictive model analysis.

Collisional excitation of PO+ by para-H2: potential energy surface, scattering calculations, and astrophysical applications

ABSTRACT We report the derivation of rate coefficients for the rotational (de-)excitation of PO+ induced by collisions with H2. The calculations were performed on a 4D potential energy surface, obtained on top of highly accurate ab initio energy points. Preliminary tests pointed out the low influence of the coupling between j = 0 and the higher rotational levels of H2 on the cross-sections values, thus allowing to neglect the rotational structure of H2. On this basis, state-to-state collisional rate coefficients were derived for temperatures ranging from 5 to 200 K. Radiative transfer calculations have been used to model the recent observation of PO+ in the G+0.693–0.027 molecular cloud, in order to evaluate the possible impact of non-LTE models on the determination of its physical conditions. The derived column density was found to be approximately ∼ 3.7 × 1011 cm−2, which is 60% (a factor of ∼ 1.7) smaller than the previously LTE-derived value. Extensive simulations show that PO+ low-j rotational lines exhibit maser behaviour at densities between 104 and 106 cm−3, thus highlighting the importance of a proper treatment of the molecular collisions to accurately model PO+ emissions in the interstellar medium.

Fully First-Principles Surface Spectroscopy with Machine Learning.

Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). As in other vibrational spectroscopies, we must turn to atomistic simulations to extract all of the information encoded in the VSFG spectra. The high computational cost associated with existing methods means that they have limitations in representing systems with complex electronic structure or in achieving statistical convergence. In this work, we combine high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to overcome these constraints. We show that it is possible to model VSFG signals with fully ab initio accuracy using machine learning and illustrate the versatility of our approach on the water/air interface. Our strategy allows us to identify the main sources of theoretical inaccuracy and establish a clear pathway toward the modeling of surface-sensitive spectroscopy of complex interfaces.

ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys.

Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on adhoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with abinitio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1-xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials.

AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics

Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy.

Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven abinitio simulations.

Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is also poorly understood because both experimental and conventional abinitio methods are restricted to studying refractory covalent materials. The use of machine learning interatomic potentials is a revolutionary trend that gives a unique opportunity for high-temperature study of materials with abinitio accuracy. We develop a deep machine learning potential (DP) for accurate atomistic simulations of the solid and liquid phases of BP as well as their transformations near the melting line. Our DP provides quantitative agreement with experimental and abinitio molecular dynamics data for structural and dynamic properties. DP-based simulations reveal that at ambient pressure, a tetrahedrally bonded cubic BP crystal melts into an open structure consisting of two interpenetrating sub-networks of boron and phosphorous with different structures. Structure transformations of BP melt under compressing are reflected by the evolution of low-pressure tetrahedral coordination to high-pressure octahedral coordination. The main contributions to structural changes at low pressures are made by the evolution of medium-range order in the B-subnetwork and, at high pressures, by the change of short-range order in the P-subnetwork. Such transformations exhibit an anomalous behavior of structural characteristics in the range of 12-15GPa. DP-based simulations reveal that the Tm(P) curve develops a maximum at P ≈ 13 GPa, whereas experimental studies provide two separate branches of the melting curve, which demonstrate the opposite behavior. Analysis of the results obtained raises open issues in developing machine learning potentials for covalent materials and stimulates further experimental and theoretical studies of melting behavior in BP.

Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates

Despite the successes of machine learning methods in physical sciences, the prediction of the Hamiltonian, and thus the electronic properties, is still unsatisfactory. Based on graph neural network (NN) architecture, we present an extendable NN model to determine the Hamiltonian from ab initio data, with only local atomic structures as inputs. The rotational equivariance of the Hamiltonian is achieved by our complete local coordinates (LCs). The LC information, encoded using a convolutional NN and designed to preserve Hermitian symmetry, is used to map hopping parameters onto local structures. We demonstrate the performance of our model using graphene and SiGe random alloys as examples. We show that our NN model, although trained using small-size systems, can predict the Hamiltonian, as well as electronic properties such as band structures and densities of states for large-size systems within the ab initio accuracy, justifying its extensibility. In combination with the high efficiency of our model, which takes only seconds to get the Hamiltonian of a 1728-atom system, the present work provides a general framework to predict electronic properties efficiently and accurately, which provides new insights into computational physics and will accelerate the research for large-scale materials.

Anisotropic Collective Variables with Machine Learning Potential for Ab Initio Crystallization of Complex Ceramics.

Enhanced sampling molecular dynamics (MD) simulations have been extensively used in the phase transition study of simple crystalline materials, such as aluminum, silica, and ice. However, MD simulation of the crystallization process for complex crystalline materials still faces a formidable challenge due to their multicomponent induced multiphase problem. Here, we realize the ab initio accuracy MD crystallization simulations of complex ceramics by using anisotropic collective variables (CVs) and machine learning (ML) potential. The anisotropic X-ray diffraction intensity CVs provide precise identification of complex crystal structures with detailed crystallography information, while the ML potential makes it feasible to further perform enhanced sampling simulations with ab initio accuracy. We verify the universality and accuracy of this method through complex ceramics with three kinds of representative structures, i.e., Ti3SiC2 for the MAX structure, zircon for the mineral structure, and lead zirconate titanate for the perovskite structure. It demonstrates exceptional efficiency and ab initio quality in achieving crystallization and generating free energy surfaces of all these ceramics, facilitating the analysis and design of complex crystalline materials.

RLEKF: An Optimizer for Deep Potential with Ab Initio Accuracy

It is imperative to accelerate the training of neural network force field such as Deep Potential, which usually requires thousands of images based on first-principles calculation and a couple of days to generate an accurate potential energy surface. To this end, we propose a novel optimizer named reorganized layer extended Kalman filtering (RLEKF), an optimized version of global extended Kalman filtering (GEKF) with a strategy of splitting big and gathering small layers to overcome the O(N^2) computational cost of GEKF. This strategy provides an approximation of the dense weights error covariance matrix with a sparse diagonal block matrix for GEKF. We implement both RLEKF and the baseline Adam in our alphaDynamics package and numerical experiments are performed on 13 unbiased datasets. Overall, RLEKF converges faster with slightly better accuracy. For example, a test on a typical system, bulk copper, shows that RLEKF converges faster by both the number of training epochs (x11.67) and wall-clock time (x1.19). Besides, we theoretically prove that the updates of weights converge and thus are against the gradient exploding problem. Experimental results verify that RLEKF is not sensitive to the initialization of weights. The RLEKF sheds light on other AI-for-science applications where training a large neural network (with tons of thousands parameters) is a bottleneck.

Measurement and calculation of CO (7-0) overtone line intensities.

Intensities of 14 lines in the sixth overtone (7-0) band of carbon monoxide (12C16O) are measured in the visible range between 14300 and 14500 cm-1 using a frequency-stabilized cavity ring-down spectrometer. This is the first observation of such a high and weak overtone spectrum of the CO molecule. A theoretical model is constructed and tested based on the use of a high accuracy abinitio dipole moment curve and a semi-empirical potential energy curve. Accurate studies of high overtone transitions provide a challenge to both experiment and theory as the lines are very weak: below 2 × 10-29cmmolecule-1 at 296K. Agreement between theory and experiment within the experimental uncertainty of a few percent is obtained. However, this agreement is only achieved after issues with the stability of the Davidson correction to the multi-reference configuration interaction calculations are addressed.

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.

Predicting the properties of NiO with density functional theory: Impact of exchange and correlation approximations and validation of the r2SCAN functional

Transition metal oxide materials are of great utility, with a diversity of topical applications ranging from catalysis to electronic devices. Because of their widespread importance in materials science, there is increasing interest in developing computational tools capable of reliable prediction of transition metal oxide phase behavior and properties. The workhorse of materials theory is density functional theory (DFT). Accordingly, we have investigated the impact of various correlation and exchange approximations on their ability to predict the properties of NiO using DFT. We have chosen NiO as a particularly challenging representative of transition metal oxides in general. In so doing, we have provided validation for the use of the r2SCAN density functional for predicting the materials properties of oxides. r2SCAN yields accurate structural properties of NiO and a local spin moment that notably persists under pressure, consistent with experiment. The outcome of our study is a pragmatic scheme for providing electronic structure data to enable the parameterization of interatomic potentials using state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies. The latter is essential to allow large scale molecular dynamics simulations of bulk and surface materials phase behavior and properties with ab initio accuracy.

The kinetics of the ice-water interface from abinitio machine learning simulations.

Molecular simulations employing empirical force fields have provided valuable knowledge about the ice growth process in the past decade. The development of novel computational techniques allows us to study this process, which requires long simulations of relatively large systems, with abinitio accuracy. In this work, we use a neural-network potential for water trained on the revised Perdew-Burke-Ernzerhof functional to describe the kinetics of the ice-water interface. We study both ice melting and growth processes. Our results for the ice growth rate are in reasonable agreement with previous experiments and simulations. We find that the kinetics of ice melting presents a different behavior (monotonic) than that of ice growth (non-monotonic). In particular, a maximum ice growth rate of 6.5 Å/ns is found at 14K of supercooling. The effect of the surface structure is explored by investigating the basal and primary and secondary prismatic facets. We use the Wilson-Frenkel relation to explain these results in terms of the mobility of molecules and the thermodynamic driving force. Moreover, we study the effect of pressure by complementing the standard isobar with simulations at a negative pressure (-1000bar) and at a high pressure (2000bar). We find that prismatic facets grow faster than the basal one and that pressure does not play an important role when the speed of the interface is considered as a function of the difference between the melting temperature and the actual one, i.e., to the degree of either supercooling or overheating.

General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian

The combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure, which can naturally preserve the Euclidean symmetry even in the presence of spin–orbit coupling. Our DeepH-E3 method enables efficient electronic structure calculation at ab initio accuracy by learning from DFT data of small-sized structures, making the routine study of large-scale supercells (>104 atoms) feasible. The method can reach sub-meV prediction accuracy at high training efficiency, showing state-of-the-art performance in our experiments. The work is not only of general significance to deep-learning method development but also creates opportunities for materials research, such as building a Moiré-twisted material database.

Energy response and spatial alignment of the perturbed electron gas.

We study the linear energy response of the uniform electron gas to an external harmonic perturbation with a focus on resolving different contributions to the total energy. This has been achieved by carrying out highly accurate abinitio path integral Monte Carlo (PIMC) calculations for a variety of densities and temperatures. We report a number of physical insights into effects such as screening and the relative importance of kinetic and potential energies for different wave numbers. A particularly interesting finding is obtained from the observed non-monotonic behavior of the induced change in the interaction energy, which becomes negative for intermediate wave numbers. This effect is strongly dependent on the coupling strength and constitutes further direct evidence for the spatial alignment of electrons introduced in earlier works [T. Dornheim et al., Commun. Phys. 5, 304 (2022)]. The observed quadratic dependence on the perturbation amplitude in the limit of weak perturbations and the quartic dependence of perturbation amplitude corrections are consistent with linear and nonlinear versions of the density stiffness theorem. All PIMC simulation results are freely available online and can be used to benchmark new methods or as input for other calculations.

Machine Learning Interatomic Potentials and Long-Range Physics.

Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs. In light of this, we present a Perspective focused on key methodologies and models being used where the presence of nonlocal physics and chemistry are crucial for describing system properties. The strategies covered include MLIPs augmented with dispersion corrections, electrostatics calculated with charges predicted from atomic environment descriptors, the use of self-consistency and message passing iterations to propagated nonlocal system information, and charges obtained via equilibration schemes. We aim to provide a pointed discussion to support the development of machine learning-based interatomic potentials for systems where contributions from only nearsighted terms are deficient.