Article
Chemistry, Physical
Qiong Gao, Jingdong Ai, Shixiang Tang, Minhuan Li, Yanshuang Chen, Jiping Huang, Hua Tong, Lei Xu, Limei Xu, Hajime Tanaka, Peng Tan
Summary: Experimental evidence shows that fast crystallization occurs in charged colloidal systems under deep supercooling, due to a coupled mechanism involving discrete advancement of the crystal growth front and defect repair inside the recently formed solid phase.
Article
Plant Sciences
Alois Bilavcik, Milos Faltus, Jiri Zamecnik
Summary: The study evaluated the viability of dormant pear buds of various varieties after cryopreservation, indicating that the two-step cryopreservation method is suitable for safe storage of dormant buds in most tested pear varieties.
Article
Materials Science, Multidisciplinary
Vaidehi Menon, Sambit Das, Vikram Gavini, Liang Qi
Summary: Understanding solute segregation thermodynamics is crucial for investigating grain boundary properties. The spectral approach and thermodynamic integration methods can be used to predict solute segregation behavior at grain boundaries and compare with experimental observations, thus aiding in alloy design and performance control.
Article
Mechanics
Weiming Ji, Mao See Wu
Summary: The research reveals that as the crack length increases, the Young's modulus and yield stress of Cantor alloys decrease, while the work of fracture increases. The formation of nanosized cavities in front of the crack tip and the subsequent crack propagation through cavity coalescence results in ductile fracture of Cantor alloys at cryogenic temperatures.
ENGINEERING FRACTURE MECHANICS
(2021)
Article
Thermodynamics
Sungjoon Byun, Haijun Jeong, Dong Rip Kim, Kwan-Soo Lee
Summary: The study focused on the impact of frost and fog during the vaporization of liquefied natural gas, specifically examining their influence on ambient air vaporizers. Experimental results indicated that fog generation was primarily influenced by the frost surface temperature. Frost and fog modeling could be utilized for predicting the timing of fog formation.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2021)
Article
Materials Science, Multidisciplinary
Lei Xi, Xinqi Zheng, Yawei Gao, Jiawang Xu, Chaofan Liu, Dingsong Wang, Juping Xu, Wen Yin, Shuxian Yang, Baojie Jin, Mengyuan Zhu, Weifeng Xu, Jianxin Shen, Jingyan Zhang, He Huang, Yanfei Wu, Fei Gu, Huiyu Shi, Yixuan Tao, Shouguo Wang, Baogen Shen
Summary: A series of polycrystalline Er1-xYxCr2Si2 (0 <= x <= 0.8) samples with giant low-field magnetocaloric effect were successfully synthesized. The sample with x = 0.1 shows the best performance, with the working temperature down to 2 K. The maximum values of magnetic entropy change ((-Delta S-M)(max)) and adiabatic temperature change ((Delta T-ad)(max)) are 19.2 J kg(-1) K-1 and 4.3 K respectively. The value of (-Delta S-M)(max) is the largest ever reported for intermetallic MCE materials below 20 K.
SCIENCE CHINA-MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Chaolin You, Wenbin Wu, Wangsheng Yuan, Peng Han, Qianyu Zhang, Xi Chen, Xinhai Yuan, Lili Liu, Jilei Ye, Lijun Fu, Yuping Wu
Summary: This study demonstrates a low-cost brine refrigerant electrolyte that enables high ionic conductivity and stable operation of an aqueous energy storage device at low temperatures. The investigation reveals the effect of different cations on reducing the freezing point of aqueous electrolytes and provides a rational design strategy for green, inexpensive, and safe low-temperature aqueous electrolytes for energy storage devices.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Construction & Building Technology
Yuanqi Cai, Yue Zhang, Yang Liu, Jinguang Li
Summary: This paper proposes a predictive method for the mechanical properties of concrete at ultra-low temperatures based on the randomness of concrete meso-structure and the influence of damage at the interface layer. By statistically analyzing the numerical simulation test results, the macroscopic mechanical properties of concrete at ultra-low temperature can be predicted.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Construction & Building Technology
Sungjoon Byun, Haijun Jeong, Seounghwan Hyeon, Kwan-Soo Lee
Summary: Quantitative analysis of frosting characteristics was conducted at ultra-low temperatures under forced convection conditions, proposing correlations for frost thickness and density in relation to Reynolds and Fourier number, absolute humidity, and non-dimensional temperature. A parametric study was performed to identify the effect of frosting parameters on frost layer growth. The correlations proposed in this study can help identify frosting behavior on devices operating under ultra-low temperatures and forced convection conditions.
ENERGY AND BUILDINGS
(2021)
Editorial Material
Chemistry, Physical
Nghi P. Nguyen, Gary F. Moore
Summary: Artificial leaves have the potential to convert sunlight into fuels for a sustainable energy future, but economic viability is a current challenge. Decentralized hydrogen production using thermally integrated architectures operating under sub-freezing temperatures shows promise for addressing this issue.
Article
Chemistry, Multidisciplinary
Samantha L. Miller, Nancy E. Levinger
Summary: The research shows that urea may cause an increase in reverse micelle size and hydrogen exchange in confined AOT reverse micelles, particularly in very small diameter spaces.
Review
Chemistry, Multidisciplinary
Francois Nkinahamira, Ruijie Yang, Rongshu Zhu, Jingwen Zhang, Zhaoyong Ren, Senlin Sun, Haifeng Xiong, Zhiyuan Zeng
Summary: This review discusses the design of efficient catalysts for activating and converting methane at low temperatures. It summarizes the applications of noble metals and transition metal-based catalysts in activating methane at temperatures ranging from 50 to 500 degrees C. It also briefly discusses the partial oxidation of methane at relatively low temperatures and presents challenges and perspectives for designing and synthesizing highly efficient catalysts at low temperatures.
Article
Chemistry, Physical
Carter T. Butts, Rachel W. Martin
Summary: The hydroxyl radical, as the primary reactive oxygen species produced by water radiolysis, plays a significant role in causing radiation damage to living organisms. The study estimates the diffusion coefficient of the hydroxyl radical in aqueous solution through a hierarchical Bayesian model based on atomistic molecular dynamics trajectories. This research is crucial for understanding the adaptive capabilities of organisms in high-radiation environments.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Review
Thermodynamics
Sungjoon Byun, Haijun Jeong, Hobin Son, Dong Rip Kim, Kwan-Soo Lee
Summary: This paper investigates the frosting phenomenon, mechanism, and properties at low temperatures. The temperature terminologies for frosting were classified, revealing condensation-freezing and desublimation as the dominant phase change mechanisms at general-low and ultra-low temperatures, respectively. The growth mechanism of frost at ultra-low temperatures involves the formation of ice nuclei, growth of ice particles, and deposition of a frost layer. The morphology of frost at ultra-low temperatures differs from that at general-low temperatures, presenting wisteria or shrub-shaped frost.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Chemistry, Physical
Richard B. Garza, Jiyoung Lee, Mai H. Nguyen, Andrew Garmon, Danny Perez, Meng Li, Judith C. Yang, Graeme Henkelman, Wissam A. Saidi
Summary: In this work, atomistic simulations were used to investigate the effect of vacancy diffusion on surface segregation during annealing of CuNi bimetallic alloys. The multi-timescale methods employed allowed for the observation of rare stochastic events that are not typically seen with standard molecular dynamics (MD), thus bridging the gap between computational and experimental timescales. These findings have implications for the experimental design of CuNi alloy surfaces with controlled segregation.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Jingang Lan, Venkat Kapil, Piero Gasparotto, Michele Ceriotti, Marcella Iannuzzi, Vladimir V. Rybkin
Summary: The nature of the bulk hydrated electron has been a challenge for both experiment and theory. Here the authors use a machine-learning model trained on MP2 data to achieve an accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.
NATURE COMMUNICATIONS
(2021)
Article
Materials Science, Multidisciplinary
Arpan Kundu, Marco Govoni, Han Yang, Michele Ceriotti, Francois Gygi, Giulia Galli
Summary: The study investigates the impact of quantum vibronic coupling on the electronic properties of carbon allotropes, utilizing path integral first principles molecular dynamics combined with a colored noise thermostat. By avoiding common approximations and only adding a moderate computational cost to FPMD simulations, the approach is suitable for large supercells needed for describing amorphous solids. The research predicts the effect of electron-phonon coupling on the fundamental gap of amorphous carbon and reveals a larger zero-phonon renormalization of the band gap in diamond than previously reported.
PHYSICAL REVIEW MATERIALS
(2021)
Article
Chemistry, Physical
Jigyasa Nigam, Michael J. Willatt, Michele Ceriotti
Summary: This article discusses a family of structural descriptors that generalize atom-centered density correlation features to multiple centers and shows how they can be applied to learning matrix elements of the single-particle Hamiltonian. These features are fully equivariant in terms of translations, rotations, and permutations of indices associated with atoms.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Raimon Fabregat, Alberto Fabrizio, Edgar A. Engel, Benjamin Meyer, Veronika Juraskova, Michele Ceriotti, Clemence Corminboeuf
Summary: The application of machine learning to theoretical chemistry has enabled the combination of quantum chemical energetics accuracy with thorough sampling of fluctuations. By utilizing local kernel regression and neural networks, accurate potential energy surfaces were generated efficiently to sample the conformational landscape of polypeptides.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Physical
Jigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux, Michele Ceriotti
Summary: Data-driven schemes provide a concise and effective description of the arrangement of atomic constituents in molecular and crystal structures. Atom-centered density correlations (ACDC) are used as a basis to expand targets in a body-ordered and symmetry-adapted manner. The ACDC framework can be extended to include multi-centered information and provide a linear basis for regressing symmetric functions of atomic coordinates, making it suitable for both atom-centered and message-passing machine-learning schemes.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Editorial Material
Chemistry, Physical
Sergey N. N. Pozdnyakov, Michael J. J. Willatt, Albert P. P. Bartok, Christoph Ortner, Gabor Csanyi, Michele Ceriotti
Summary: This paper points out the degenerate pairs of configurations issue in all low-body-order atom-density correlation representations of molecular structures and discovers the quasi-constant smooth overlap of atomic position and atom-centered symmetry function fingerprint manifolds closely related to this problem. They demonstrate that the rigid singular configurations can only occur in finite, discrete sets and propose methods to optimize model parameters and the training set to mitigate their impact on machine learning models.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Manuel Cordova, Edgar A. Engel, Artur Stefaniuk, Federico Paruzzo, Albert Hofstetter, Michele Ceriotti, Lyndon Emsley
Summary: Nuclear magnetic resonance (NMR) chemical shifts provide information about the atomic environments in solid materials, but accurately predicting these shifts is computationally expensive. ShiftML is a machine-learning model that can rapidly predict chemical shifts with the same accuracy as density functional theory (DFT) calculations, reducing the computational cost by over four orders of magnitude.
JOURNAL OF PHYSICAL CHEMISTRY C
(2022)
Article
Chemistry, Physical
Lorenzo Gigli, Max Veit, Michele Kotiuga, Giovanni Pizzi, Nicola Marzari, Michele Ceriotti
Summary: The study develops an integrated machine learning model that accurately describes the structural, energetic, and functional properties of ferroelectric materials, and allows for the investigation of the microscopic mechanism of ferroelectric transition without the need for a coarse-grained description. The main driver of the ferroelectric transition in barium titanate is found to be the order-disorder transition of the off-centered states.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Chemistry, Physical
Filippo Bigi, Kevin K. Huguenin-Dumittan, Michele Ceriotti, David E. Manolopoulos
Summary: Machine learning frameworks based on correlations of interatomic positions rely on a discretized description of atomic density, and this study investigates a basis obtained from solving the Laplacian eigenvalue problem within a sphere around the atom of interest. The results show that this basis has controllable smoothness and performs better than some widely used basis sets, comparable to data-driven bases.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Andrea Grisafi, Alan M. Lewis, Mariana Rossi, Michele Ceriotti
Summary: The electron density of molecules or materials is an important target quantity for machine learning models. This study proposes a model that uses multicentered atomic basis to represent the scalar field, allowing transferable and linear-scaling predictions. The model overcomes the challenges posed by the non-orthogonality of the basis by using a gradient-based approach and optimizing the feature space. The results show that this method can accurately predict electron density with a comparatively small training set and achieve a good balance between accuracy and computational efficiency.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Multidisciplinary
Rose K. Cersonsky, Maria Pakhnova, Edgar A. Engel, Michele Ceriotti
Summary: We have automated the construction of an extensive library of molecular building blocks using machine learning techniques. By predicting the stability of crystal structures based on the inherent structure and energy correlations, as well as the contribution of chemical groups to the lattice energy, this library provides a comprehensive database for guiding the design of molecular materials.
Editorial Material
Chemistry, Physical
Michele Ceriotti, Lasse Jensen, David E. Manolopoulos, Todd Martinez, David R. Reichman, Francesco Sciortino, C. David Sherrill, Qiang Shi, Carlos Vega, Lai-Sheng Wang, Emily A. Weiss, Xiaoyang Zhu, Jenny Stein, Tianquan Lian
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Materials Science, Multidisciplinary
Michele Ceriotti
Summary: Machine learning-based interatomic potentials have become an essential tool in atomic-scale modeling of materials, enabling accurate predictions of properties and extending simulation capabilities. These models have greatly facilitated the calculation of thermodynamics and functional properties, and are bringing us closer to predictive simulations of existing and novel materials.
Article
Computer Science, Artificial Intelligence
Sergey N. Pozdnyakov, Michele Ceriotti
Summary: Graph neural networks (GNN) are popular in machine learning and have been successful in predicting properties of molecules and materials. However, first-order GNNs are known to be incomplete, leading to the design of more complex schemes. The construction of graph representations for molecules adds a geometric dimension, with the most common approach being to consider atoms as vertices and connect them with bonds. This approach, known as distance graph NNs (dGNN), has shown excellent resolving power in chemical ML. However, the authors present a counterexample that proves dGNNs are not complete even for fully-connected graphs induced by 3D atom clouds.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2022)
Article
Materials Science, Multidisciplinary
Chiheb Ben Mahmoud, Federico Grasselli, Michele Ceriotti
Summary: The article discusses the prediction of electronic free energy at arbitrary electron temperature using machine learning, avoiding the need to train temperature-dependent potentials, providing a new method. By combining physical considerations with machine-learning predictions, it offers a blueprint for the development of atomistic modeling.