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Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations
Authors:
Hyeonbin Moon,
Donghyuk Cho,
Jecheon Yu,
Jeong Whan Yoon,
Seunghwa Ryu
Abstract:
Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial. This study proposes a physics-informed framework for discovering yield functions from full-field displacement data and reaction force data,…
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Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial. This study proposes a physics-informed framework for discovering yield functions from full-field displacement data and reaction force data, without stress observations, plastic strain measurements, direct yield surface data, or a prescribed parametric yield function. The framework identifies the yield function as a mechanically constrained constitutive component inside elastoplastic stress integration, rather than through direct stress-space supervision. The yield function is represented by a convex neural network that enforces convexity and positive homogeneity of degree one while imposing the assumed tension-compression symmetry, and this neural yield function is trained with a differentiable stress update and a physics-informed force equilibrium loss across multiple loading cases. The proposed framework is validated using finite element (FE) benchmark studies with von Mises, Hill 1948, and Yld2000-2d yield functions, assessing yield contour agreement, displacement-noise sensitivity, identifiability through plastically active stress states, epistemic uncertainty, and polynomial-surrogate deployment. This study provides a mechanics-constrained pathway for discovering anisotropic yield functions from displacement and force data while keeping the identified component within the structure of elastoplastic stress integration.
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Submitted 12 June, 2026;
originally announced June 2026.
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SG-NNP: Species-separated Gaussian Neural Network Potential with Linear Elemental Scaling and Optimized Dimensions for Multi-component Materials
Authors:
Ji Wei Yoon,
Bangjian Zhou,
J Senthilnath
Abstract:
Accurate simulations of materials at long-time and large-length scales have increasingly been enabled by Machine-learned Interatomic Potentials (MLIPs). There have been increasing interest on improving the robustness of such models. To this end, we engineer a novel set of Gaussian-type descriptors that scale linearly with the number of atoms, reduce informational degeneracy for multi-component ato…
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Accurate simulations of materials at long-time and large-length scales have increasingly been enabled by Machine-learned Interatomic Potentials (MLIPs). There have been increasing interest on improving the robustness of such models. To this end, we engineer a novel set of Gaussian-type descriptors that scale linearly with the number of atoms, reduce informational degeneracy for multi-component atomic environments and apply them in Species-separated Gaussian Neural Network Potentials (SG-NNPs). The robustness of our method was tested by analyzing the impact of various design choices and hyperparameters on Molybdenum (Mo) SG-NNP performance during training and inference/simulation. With less dimensions, SG-NNPs are shown to have superior atomic forces and total energy predictions than other traditional and ML descriptor-based interatomic potentials on diverse set of materials - Ni, Cu, Li, Mo, Si, Ge, NiMo, Li3N and NbMoTaW. From the obtained results we can observe that the proposed method improves the performance of atomic descriptors of complex environments with multiple species.
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Submitted 9 July, 2024;
originally announced July 2024.
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Generating Nanoporous Graphene from Point and Stone-Wales Defects: A Study with Dimensionally Restricted Molecular Dynamics (DR-MD)
Authors:
Ji Wei Yoon
Abstract:
Defects in graphene are both a boon and a bane for applications - they can induce uncontrollable effects but can also provide novel ways to manipulate the properties of pristine graphene. Nanoporous Graphene, which contains nanoscopic holes, has found impactful applications in sustainability domains, e.g. gas separation, water filtration membranes and battery technologies. For this report, we inve…
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Defects in graphene are both a boon and a bane for applications - they can induce uncontrollable effects but can also provide novel ways to manipulate the properties of pristine graphene. Nanoporous Graphene, which contains nanoscopic holes, has found impactful applications in sustainability domains, e.g. gas separation, water filtration membranes and battery technologies. For this report, we investigate pore formation in graphene with no defect, one and two mono-vacancies, and two di-vacancies using bespoke Dimensionally Restricted Molecular Dynamics (DR-MD) designed for the purpose. We show DR-MD to be superior to free-standing or substrate suspended configurations for simulating stable defected structures. Applying DR-MD, stable pore configurations are identified, and their formation mechanisms elucidated. We also investigated formation mechanisms due to two Stone-Wales 55-77 defects, and the formation energies of their linearly extended structures, along the zigzag and armchair directions, and when they are placed in different relative orientations. This study offers a way to identify stable porous defect structures in graphene and insights into atomistic pore formation mechanisms for an environmentally important material.
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Submitted 6 October, 2023; v1 submitted 26 August, 2023;
originally announced August 2023.
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Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers
Authors:
Ji Wei Yoon,
Adithya Kumar,
Pawan Kumar,
Kedar Hippalgaonkar,
J Senthilnath,
Vijila Chellappan
Abstract:
The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurement…
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The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity > 25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.
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Submitted 27 April, 2024; v1 submitted 8 August, 2023;
originally announced August 2023.
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Microscopic Statistical Characterisation of the Congested Traffic Flow and Some Salient Empirical Features
Authors:
Bo Yang,
Ji Wei Yoon,
Christopher Monterola
Abstract:
We present large scale and detailed analysis of the microscopic empirical data of the traffic flow, focusing on the non-linear interactions between the vehicles when the traffic is congested. By implementing a "renormalisation" procedure that averages over relatively unimportant factors, we extract the effective dependence of the acceleration on the vehicle headway, velocity and relative velocity,…
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We present large scale and detailed analysis of the microscopic empirical data of the traffic flow, focusing on the non-linear interactions between the vehicles when the traffic is congested. By implementing a "renormalisation" procedure that averages over relatively unimportant factors, we extract the effective dependence of the acceleration on the vehicle headway, velocity and relative velocity, that characterises not just a few vehicles but the traffic system as a whole. Several interesting features of the detailed vehicle-to-vehicle interactions are revealed, including the stochastic distribution of human responses, relative importance of non-linear terms in different density regimes, symmetric response to the relative velocity, and the insensitivity of the acceleration to the velocity within a certain headway and velocity range. The latter leads to a multitude of steady-states without a fundamental diagram, showing strong evidence of the "synchronised phase", substantiated with additional microscopic details. We discuss the richness of the available data for the understanding of the human driving behaviours, and its usefulness in construction and calibration of both deterministic and stochastic microscopic traffic models.
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Submitted 14 March, 2016;
originally announced March 2016.
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Theoretical Modeling of ME effect at Low frequency and Resonance Frequency for Magnetoelectric Laminates with Anisotropic Piezoelectric Properties
Authors:
Deepak Rajaram Patil,
Yisheng Chai,
Rahul C. Kambale,
Byung-Gu Jeon,
Jungho Ryu,
Woon-Ha Yoon,
Dong-Soo Park,
Dae-Yong Jeong,
Sang-Goo Lee,
Jeongho Lee,
Joong-Hee Nam,
Jeong-Ho Cho,
Byung-Ik Kim,
Kee Hoon Kim
Abstract:
A new theory is developed for the magnetoelectric (ME) coupling in a symmetric 2-2 ME laminate having a representative piezoelectric crystal (PMN-PT) particularly with anisotropic piezoelectric properties. Considering the average field method, the theoretical expressions for the transverse ME voltage coefficients at low and resonance frequencies were derived. The theory takes into account the anis…
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A new theory is developed for the magnetoelectric (ME) coupling in a symmetric 2-2 ME laminate having a representative piezoelectric crystal (PMN-PT) particularly with anisotropic piezoelectric properties. Considering the average field method, the theoretical expressions for the transverse ME voltage coefficients at low and resonance frequencies were derived. The theory takes into account the anisotropic properties of the piezoelectric materials providing two different expressions of transverse ME voltage coefficients for different in-plane magnetic fields both at low and resonance frequencies. The numerical simulations show multiple resonance frequencies and phase differences between transverse ME voltage coefficients showing good agreement with the experimental results. Our theory should be generally applicable to other ME laminates with any piezoelectric with anisotropic piezoelectric coefficients.
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Submitted 9 December, 2012; v1 submitted 29 November, 2012;
originally announced November 2012.
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Structure and Magnetism of the mono-layer hydrate Na0.3NiO2 0.7H2O
Authors:
S. Park,
W. -S. Yoon,
T. Vogt
Abstract:
The mono-layer hydrate (Ni-MLH) Na0.3NiO2 0.7H2O was synthesized with an average Ni valence close to one in the bi-layer hydrate (Ni-BLH) Na0.3NiO2 1.3H2O. A weak unsaturated ferromagnetism and divergence of the magnetic susceptibilities of field and zero-field cooled samples of both Ni-MLH and Ni-BLH are observed. However, as a result of the increased 3-dimensional electronic and magnetic chara…
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The mono-layer hydrate (Ni-MLH) Na0.3NiO2 0.7H2O was synthesized with an average Ni valence close to one in the bi-layer hydrate (Ni-BLH) Na0.3NiO2 1.3H2O. A weak unsaturated ferromagnetism and divergence of the magnetic susceptibilities of field and zero-field cooled samples of both Ni-MLH and Ni-BLH are observed. However, as a result of the increased 3-dimensional electronic and magnetic character present in Ni-MLH a frequency dependence of the AC magnetic susceptibility indicative of the spin glass-like behavior as seen in Ni-BLH was no longer observed in Ni-MLH.
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Submitted 20 January, 2007;
originally announced January 2007.
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Synthesis and characterization of Na03RhO206H2O - a semiconductor with a weak ferromagnetic component
Authors:
S. Park,
K. Kang,
W. Si,
W. -S. Yoon,
Y. Lee,
A. R. Moodenbaugh,
L. H. Lewis,
T. Vogt
Abstract:
We have prepared the oxyhydrate Na03RhO206H2O by extracting Na+ cations from NaRhO2 and intercalating water molecules using an aqueous solution of Na2S2O8. Synchrotron X-ray powder diffraction, thermogravimetric analysis (TGA), and energy-dispersive x-ray analysis (EDX) reveal that a non-stoichiometric Na03(H2O)06 network separates layers of edge-sharing RhO6 octahedra containing Rh3+(4d6, S=0)…
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We have prepared the oxyhydrate Na03RhO206H2O by extracting Na+ cations from NaRhO2 and intercalating water molecules using an aqueous solution of Na2S2O8. Synchrotron X-ray powder diffraction, thermogravimetric analysis (TGA), and energy-dispersive x-ray analysis (EDX) reveal that a non-stoichiometric Na03(H2O)06 network separates layers of edge-sharing RhO6 octahedra containing Rh3+(4d6, S=0) and Rh4+ (4d5, S=1/2). The resistivities of NaRhO2 and Na03RhO206H2O (T < 300) reveal insulating and semi-conducting behavior with activation gaps of 134 meV and 7.8 meV, respectively. Both Na03RhO206H2O and NaRhO2 show paramagnetism at room temperature, however, the sodium-deficient sample exhibits simultaneously a weak but experimentally reproducible ferromagnetic component. Both samples exhibit a temperature-independent Pauli paramagnetism, for NaRhO2 at T > 50 K and for Na03RhO206H2O at T > 25 K. The relative magnitudes of the temperature-independent magnetic susceptibilities, that of the oxide sample being half that of the oxyhydrate, is consistent with a higher density of thermally accessible electron states at the Fermi level in the hydrated sample. At low temperatures the magnetic moments rise sharply, providing evidence of localized and weakl -ordered electronic spins.
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Submitted 31 March, 2005;
originally announced April 2005.