Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine-learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal structures containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine learning potentials.© 2023. The Author(s).

The spontaneously formed passivation layer, the solid electrolyte interphase (SEI) between the electrode and electrolyte, is crucial to the performance and durability of Li ion batteries. However, the Li ion transport mechanism in the major inorganic components of the SEI (LiCO and LiF) is still unclear. Particularly, whether introducing an amorphous environment is beneficial for improving the Li ion diffusivity is under debate. Here, we investigate the Li ion diffusion mechanism in amorphous LiF and LiCO via machine-learning-potential-assisted molecular dynamics simulations. Our results show that the Li ion diffusivity in LiF at room temperature cannot be accurately captured by the Arrhenius extrapolation from the high temperature (>600 K) diffusivities (difference of ∼2 orders of magnitude). We reveal that the spontaneous formation of Li-F regular tetrahedrons at low temperatures (<500 K) leads to an extremely low Li ion diffusivity, suggesting that designing an amorphous bulk LiF-based SEI cannot help with the Li ion transport. We further show the critical role of LiCO in suppressing the Li-F regular tetrahedron formation when these two components of SEIs are mixed. Overall, our work provides atomic insights into the impact of the local environment on Li ion diffusion in the major SEI components and suggests that suppressing the formation of large-sized bulk-phase LiF might be critical to improve battery performance.

We develop a new deep potential-range correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of six nonenzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free-energy profiles generated from a target QM model. We perform these comparisons using the MNDO/d and DFTB2 semiempirical models because they differ in the way they treat orbital orthogonalization and electrostatics and produce free-energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free-energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure, so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce four different reactions and yielded good agreement with the free-energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free-energy surfaces and 1D free-energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs but was sped up almost 100-fold when using NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free-energy applications ranging from drug discovery to enzyme design.

Free energy profile (FE Profile) is an essential quantity for the estimation of reaction rate and the validation of reaction mechanism. For chemical reactions in condensed phase or enzymatic reactions, the computation of FE profile at the ab initio (ai) quantum mechanical/molecular mechanics (QM/MM) level is still far too expensive. Although semiempirical (SE) method can be hundreds or thousands of times faster than the ai methods, the accuracy of SE methods is often unsatisfactory due to the approximations that have been adopted in these methods. In this work, we propose a new method termed MBAR+wTP in which the ai QM/MM free energy profile is computed by a weighted thermodynamic perturbation (TP) correction to the SE profile generated by the multistate Bennett acceptance ratio (MBAR) analysis of the trajectories from umbrella samplings (US). The weight factors used in the TP calculations are a byproduct of the MBAR analysis in the postprocessing of the US trajectories, which are often discarded after the free energy calculations. The raw ai QM/MM free energy profile is then smoothed using Gaussian process regression in which the noise of each datum is set to be inversely proportional to the exponential of the reweighting entropy. The results show that this approach can enhance the efficiency of ai FE profile calculations by several orders of magnitude with only a slight loss of accuracy. This method can significantly enhance the applicability of ai QM/MM methods in the studies of chemical reactions in condensed phase and enzymatic reactions.

Nucleation, the starting point of first-order discontinuous phase transformations, has long been an important issue in condensed matter physics and materials science. It plays a key role in determining the microstructures and mechanical properties of crystalline materials. As nucleation occurs at the atomic length scale and the diffusional time scale and is a typical stochastic event, investigating such kind of multiple scale issues will be taken up an enormous challenge. Because of the limitations of present experimental methods, it is still very hard to observe the nucleation process in situ. With the development of computational materials science, a deeper understanding of nucleation process has been obtained with the numerical modeling of nucleation process on the atomic scale. In this paper, some recent developments in modeling and simulation of nucleation process during solidification on the atomic scale are reviewed. Firstly, the development of classical nucleation theory and the step nucleation theory are reviewed. Then the developments in modeling of nucleation process by using the phase field method, Monte-Carlo method, Molecular dynamics method and the phase field crystal model are discussed. After that, some recent progresses in modeling of nucleation process during solidification in our research group by using the phase field crystal model are demonstrated. Finally, the outlooks of the future study on the nucleation during solidification are also presented.

形核是一级不连续相变的起点,对后续材料组织的形成及最终产品的性能具有重要影响。形核过程、机制及其控制一直是材料科学和凝聚态物理等领域中最为活跃的研究课题之一。形核过程发生在原子空间尺度及扩散时间尺度,同时还具有随机性,因此充分认识形核过程将面临极大的挑战。受实验条件的限制,目前难以通过实验方法直接观测金属凝固中晶核的形成过程。近年来,随着计算材料科学的兴起,借助于数值模拟方法,凝固形核问题研究取得了很大的进展。本文首先回顾了形核理论的发展历程,然后对当前凝固形核模拟的研究进展进行述评,并介绍本课题组近年来基于晶体相场模型在形核研究方面的工作进展,最后对形核研究进行了展望。

The nucleation of crystals in liquids is one of nature's most ubiquitous phenomena, playing an important role in areas such as climate change and the production of drugs. As the early stages of nucleation involve exceedingly small time and length scales, atomistic computer simulations can provide unique insights into the microscopic aspects of crystallization. In this review, we take stock of the numerous molecular dynamics simulations that, in the past few decades, have unraveled crucial aspects of crystal nucleation in liquids. We put into context the theoretical framework of classical nucleation theory and the state-of-the-art computational methods by reviewing simulations of such processes as ice nucleation and the crystallization of molecules in solutions. We shall see that molecular dynamics simulations have provided key insights into diverse nucleation scenarios, ranging from colloidal particles to natural gas hydrates, and that, as a result, the general applicability of classical nucleation theory has been repeatedly called into question. We have attempted to identify the most pressing open questions in the field. We believe that, by improving (i) existing interatomic potentials and (ii) currently available enhanced sampling methods, the community can move toward accurate investigations of realistic systems of practical interest, thus bringing simulations a step closer to experiments.

Recent observations of prenucleation species and multi-stage crystal nucleation processes challenge the long-established view on the thermodynamics of crystal formation. Here, we review and generalize extensions to classical nucleation theory. Going beyond the conventional implementation as has been used for more than a century now, nucleation inhibitors, precursor clusters and non-classical nucleation processes are rationalized as well by analogous concepts based on competing interface and bulk energy terms. This is illustrated by recent examples of species formed prior to/instead of crystal nucleation and multi-step nucleation processes. Much of the discussed insights were obtained from molecular simulation using advanced sampling techniques, briefly summarized herein for both nucleation-controlled and diffusion-controlled aggregate formation. © 2015 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Crystallisation is at the heart of various scientific disciplines, but still the understanding of the molecular mechanisms underlying phase separation and the formation of the first solid particles in aqueous solution is rather limited. In this review, classical nucleation theory, as well as established concepts of spinodal decomposition and liquid-liquid demixing, is introduced together with a description of the recently proposed pre-nucleation cluster pathway. The features of pre-nucleation clusters are presented and discussed in relation to recent modifications of the classical and established models for phase separation, together with a review of experimental work and computer simulations on the characteristics of pre-nucleation clusters of calcium phosphate, calcium carbonate, iron(oxy)(hydr)oxide, silica, and also amino acids as an example of small organic molecules. The role of pre-nucleation clusters as solute precursors in the emergence of a new phase is summarized, and the link between the chemical speciation of homogeneous solutions and the process of phase separation via pre-nucleation clusters is highlighted.

We introduce a powerful method for exploring the properties of the multidimensional free energy surfaces (FESs) of complex many-body systems by means of coarse-grained non-Markovian dynamics in the space defined by a few collective coordinates. A characteristic feature of these dynamics is the presence of a history-dependent potential term that, in time, fills the minima in the FES, allowing the efficient exploration and accurate determination of the FES as a function of the collective coordinates. We demonstrate the usefulness of this approach in the case of the dissociation of a NaCl molecule in water and in the study of the conformational changes of a dialanine in solution.

The simulation of rare events is one of the key problems in atomistic simulations. Toward its solution, a plethora of methods have been proposed. Here we combine two such methods: metadynamics and integrated tempering sampling. In metadynamics, the fluctuations of a carefully chosen collective variable are amplified, while in integrated tempering sampling the system is pushed to visit an approximately uniform interval of energies and allows exploring a range of temperatures in a single run. We describe our approach and apply it to the two prototypical systems a S2 chemical reaction and to the freezing of silica. The combination of metadynamics and integrated tempering sampling leads to a powerful method. In particular in the case of silica we have measured more than 1 order of magnitude acceleration.

Boosting transitions of rare events is critical to simulations of chemical and biophysical dynamic systems in order to close the time scale gaps between theoretical modeling and experiments. We present a novel approach, called targeted adversarial learning optimized sampling (TALOS), to modify the potential energy surface in order to drive the system to a user-defined target distribution where the free-energy barrier is lowered. Combining statistical mechanics and generative learning, TALOS formulates a competing game between a sampling engine and a virtual discriminator, enables unsupervised construction of bias potentials, and seeks for an optimal transport plan that transforms the system into a target. Through multiple experiments, we show that on-the-fly training of TALOS benefits from the state-of-art optimization techniques in deep learning and thus is efficient, robust, and interpretable. TALOS is also closely connected to the actor-critic reinforcement learning and hence leads to a new way of flexibly manipulating the many-body Hamiltonian systems.

Silica is one of the most abundant minerals on Earth and is widely used in many fields. Investigating the crystallization of liquid silica by atomic simulations is of great importance to understand the crystallization mechanism; however, the high crystallization barrier and the tendency of silica to form glasses make such simulations very challenging. Here we have studied liquid silica crystallization to β-cristobalite with metadynamics, using X-ray diffraction (XRD) peak intensities as collective variables. The frequent transitions between solid and liquid of the biased runs demonstrate the highly successful use of the XRD peak intensities as collective variables, which leads to the convergence of the free-energy surface. By calculating the difference in free energy, we have estimated the melting temperature of β-cristobalite, which is in good agreement with the literature. The nucleation mechanism during the crystallization of liquid silica can be described by classical nucleation theory.Copyright © 2018 the Author(s). Published by PNAS.

Cadmium selenide (CdSe) is an inorganic semiconductor with unique optical and electronic properties that make it useful in various applications, including solar cells, light-emitting diodes, and biofluorescent tagging. In order to synthesize high-quality crystals and subsequently integrate them into devices, it is crucial to understand the atomic scale crystallization mechanism of CdSe. Unfortunately, such studies are still absent in the literature. To overcome this limitation, we employed an enhanced sampling-accelerated active learning approach to construct a deep neural potential with <em>ab initio</em> accuracy for studying the crystallization of CdSe. Our brute-force molecular dynamics simulations revealed that a spherical-like nucleus formed spontaneously and stochastically, resulting in a stacking disordered structure where the competition between hexagonal wurtzite and cubic zinc blende polymorphs is temperature-dependent. We found that pure hexagonal crystal can only be obtained approximately above 1430 K, which is 35 K below its melting temperature. Furthermore, we observed that the solidification dynamics of Cd and Se atoms were distinct due to their different diffusion coefficients. The solidification process was initiated by lower mobile Se atoms forming tetrahedral frameworks, followed by Cd atoms occupying these tetrahedral centers and settling down until the third-shell neighbor of Se atoms sited on their lattice positions. Therefore, the medium-range ordering of Se atoms governs the crystallization process of CdSe. Our findings indicate that understanding the complex dynamical process is the key to comprehending the crystallization mechanism of compounds like CdSe, and can shed lights in the synthesis of high-quality crystals.

Water is one of the most abundant molecules on Earth. However, this common and "simple" material has more than 18 different phases, which poses a great challenge to theoretically study the nature of water and ice. We designed two reaction coordinates that can distinguish between water and various ice states and used them to efficiently sample all possible states of the system in all-atom molecular dynamics simulation at ambient temperature and pressure. Various structural and thermodynamics properties, including the water-ice phase diagrams, can thus be calculated. We also present a simple model that successfully explains the thermodynamic stability of different ice states. Our work provides effective methods and data for theoretical studies of different phases of water and ice.

We introduce a method to obtain one-dimensional collective variables for studying rarely occurring transitions between two metastable states separated by a high free energy barrier. No previous information, not even approximated, on the path followed during the transition is needed. The only requirement is to know the fluctuations of the system while in the two metastable states. With this information in hand, we build the collective variable using a modified version of Fisher's linear discriminant analysis. The usefulness of this approach is tested on the metadynamics simulation of two representative systems. The first is the freezing of silver iodide into the superionic α-phase, and the second is the study of a classical Diels-Alder reaction. The collective variable works very well in these two diverse cases.

Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the nonlinearly separable data set composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing nonlinear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.

Predicting the temperature and extent of ice freezing in aqueous solutions is crucial for areas as diverse as cryobiology and materials design. It has long been recognized that the thermodynamics of liquid water controls the temperature and kinetics of ice crystallization. Parameterizations of the freezing temperatures in terms of the water activity of the solution have been successfully established, but the fundamental origin of the thermodynamic control of the non-equilibrium crystallization of ice has remained elusive. Here we use large-scale molecular simulations to elucidate the relationship between the structure, thermodynamics, and ice crystallization temperatures for solutions of mW water and a strongly hydrophilic solute that mimics LiCI ions. Fast cooling of solutions with up to 20 mol% ions results in the formation of nanosegregated glasses with domains of low-density amorphous ice and an ion-rich vitrified solution. Slow cooling of the mixtures results in nucleation and growth of ice within the domains of four-coordinated liquid water. The temperature of crystallization Tx coincides with the temperature of appearance of nanoscopic domains of four-coordinated liquid water in the mixture, T(L). We use the insight provided by the simulations to derive a thermodynamic expression for the crystallization temperature as a function of the water activity, T(X)(a(W)), analogous to the dependence of the melting temperature, T(m)(a(W)). The simple expression derived in this work provides a good account of the experimental freezing temperatures of water and the well-known steepest dependence of Tx on solute concentration compared to that of T(m).

When an ice crystal is born from liquid water, two key changes occur: () The molecules order and () the mobility of the molecules drops as they adopt their lattice positions. Most research on ice nucleation (and crystallization in general) has focused on understanding the former with less attention paid to the latter. However, supercooled water exhibits fascinating and complex dynamical behavior, most notably dynamical heterogeneity (DH), a phenomenon where spatially separated domains of relatively mobile and immobile particles coexist. Strikingly, the microscopic connection between the DH of water and the nucleation of ice has yet to be unraveled directly at the molecular level. Here we tackle this issue via computer simulations which reveal that () ice nucleation occurs in low-mobility regions of the liquid, () there is a dynamical incubation period in which the mobility of the molecules drops before any ice-like ordering, and () ice-like clusters cause arrested dynamics in surrounding water molecules. With this we establish a clear connection between dynamics and nucleation. We anticipate that our findings will pave the way for the examination of the role of dynamical heterogeneities in heterogeneous and solution-based nucleation.Copyright © 2019 the Author(s). Published by PNAS.

Ice formation is ubiquitous in nature, with important consequences in a variety of environments, including biological cells, soil, aircraft, transportation infrastructure, and atmospheric clouds. However, its intrinsic kinetics and microscopic mechanism are difficult to discern with current experiments. Molecular simulations of ice nucleation are also challenging, and direct rate calculations have only been performed for coarse-grained models of water. For molecular models, only indirect estimates have been obtained, e.g., by assuming the validity of classical nucleation theory. We use a path sampling approach to perform, to our knowledge, the first direct rate calculation of homogeneous nucleation of ice in a molecular model of water. We use TIP4P/Ice, the most accurate among existing molecular models for studying ice polymorphs. By using a novel topological approach to distinguish different polymorphs, we are able to identify a freezing mechanism that involves a competition between cubic and hexagonal ice in the early stages of nucleation. In this competition, the cubic polymorph takes over because the addition of new topological structural motifs consistent with cubic ice leads to the formation of more compact crystallites. This is not true for topological hexagonal motifs, which give rise to elongated crystallites that are not able to grow. This leads to transition states that are rich in cubic ice, and not the thermodynamically stable hexagonal polymorph. This mechanism provides a molecular explanation for the earlier experimental and computational observations of the preference for cubic ice in the literature.

Post-transition elements, together with zinc-group metals and their alloys belong to an emerging class of materials with fascinating characteristics originating from their simultaneous metallic and liquid natures. These metals and alloys are characterised by having low melting points (i.e. between room temperature and 300 °C), making their liquid state accessible to practical applications in various fields of physical chemistry and synthesis. These materials can offer extraordinary capabilities in the synthesis of new materials, catalysis and can also enable novel applications including microfluidics, flexible electronics and drug delivery. However, surprisingly liquid metals have been somewhat neglected by the wider research community. In this review, we provide a comprehensive overview of the fundamentals underlying liquid metal research, including liquid metal synthesis, surface functionalisation and liquid metal enabled chemistry. Furthermore, we discuss phenomena that warrant further investigations in relevant fields and outline how liquid metals can contribute to exciting future applications.

Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist. In order to simulate this complex system, we construct an ab initio quality interaction potential by training a neural network on a set of density functional theory calculations performed on configurations generated in multithermal-multibaric simulations. Here we show that the relative equilibrium between liquid gallium, α-Ga, β-Ga, and Ga-II is well described. The resulting phase diagram is in agreement with the experimental findings. The local structure of liquid gallium and its nucleation into α-Ga and β-Ga are studied. We find that the formation of metastable β-Ga is kinetically favored over the thermodinamically stable α-Ga. Finally, we provide insight into the experimental observations of extreme undercooling of liquid Ga.

Calcium carbonate (CaCO), the most abundant biogenic mineral on earth, plays a crucial role in various fields such as hydrosphere, biosphere, and climate regulation. Of the four polymorphs, calcite, aragonite, vaterite, and amorphous CaCO, vaterite is the most enigmatic one due to an ongoing debate regarding its structure that has persisted for nearly a century. In this work, based on systematic transmission electron microscopy characterizations, crystallographic analysis and machine learning aided molecular dynamics simulations with ab initio accuracy, we reveal that vaterite can be regarded as a polytypic structure. The basic phase has a monoclinic lattice possessing pseudohexagonal symmetry. Direct imaging and atomic-scale simulations provide evidence that a single grain of vaterite can contain three orientation variants. Additionally, we find that vaterite undergoes a second-order phase transition with a critical point of ~190 K. These atomic scale insights provide a comprehensive understanding of the structure of vaterite and offer advanced perspectives on the biomineralization process of calcium carbonate.© 2023. The Author(s).

Materials genome engineering (MGE) is a frontier technology in the field of material science and engineering, which is well capable to revolutionize the research and development (R&D) mode of new materials, greatly improve the R&D efficiency, shorten the R&D time, and reduce the cost. This paper reviews the progress of MGE in China from the aspects of the fundamental theory and methods, key technology and equipment, the R&D of new materials and related engineering application, talents training, formation and promotion of new concept of material genetic engineering. The paper also looks forward to the future development of MGE in China.

材料基因工程是材料领域的颠覆性前沿技术，将对材料研发模式产生革命性的变革，全面加速材料从设计到工程化应用的进程，大幅度提升新材料的研发效率，缩短研发周期，降低研发成本，促进工程化应用。本文从基础理论与方法、关键技术与装备、新材料研发与工程化应用、人才培养以及材料基因工程新理念的形成和推广等方面，综述了中国材料基因工程的研究进展，并提出了未来发展方向建议。