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Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been proven to have a stronger representation ability than DT, as it…

Machine Learning · Computer Science 2025-02-04 Shen-Huan Lyu , Yi-Xiao He , Yanyan Wang , Zhihao Qu , Bin Tang , Baoliu Ye

Via hydrodynamics preserving molecular dynamics simulations we study growth phenomena in a phase separating symmetric binary mixture model. We quench high-temperature homogeneous configurations to state points inside the miscibility gap,…

Soft Condensed Matter · Physics 2021-07-23 Koyel Das , Subir K. Das

Accurately modeling crack propagation is critical for predicting failure in engineering materials and structures, where small cracks can rapidly evolve and cause catastrophic damage. The interaction of cracks with discontinuities, such as…

Machine Learning · Computer Science 2025-09-12 Elham Kiyani , Venkatesh Ananchaperumal , Ahmad Peyvan , Mahendaran Uchimali , Gang Li , George Em Karniadakis

The control of condensed matter systems out of equilibrium by laser pulses allows us to investigate the system trajectories through symmetry-breaking phase transitions. Thus the evolution of both collective modes and single particle…

Mesoscale and Nanoscale Physics · Physics 2015-06-15 Dragan Mihailovic , Tomaz Mertelj , Viktor V Kabanov , Serguei Brazovskii

The stability of interfaces and the mechanisms of thin film growth on semiconductors are issues of central importance in electronic devices. These issues can only be understood through detailed study of the relevant microscopic processes.…

mtrl-th · Physics 2016-09-07 Efthimios Kaxiras

We review recent machine-learning (ML) approaches for point defects in non-metallic materials, with an emphasis on defect formation energies. Existing studies largely fall into two categories: direct ML models that predict defect energetics…

Materials Science · Physics 2026-05-19 Yu Kumagai , Shin Kiyohara

This work presents a rigorous mathematical formulation for topology optimization of a macrostructure undergoing ductile failure. The prediction of ductile solid materials which exhibit dominant plastic deformation is an intriguingly…

Numerical Analysis · Mathematics 2023-03-22 Nima Noii , Hassan Ali Jahangiry , Haim Waisman

In recent years, machine learning interatomic potentials (MLIPs) have attracted significant attention as a method that enables large-scale, long-time atomistic simulations while maintaining accuracy comparable to electronic structure…

Materials Science · Physics 2025-03-27 Yuta Yoshimoto , Naoki Matsumura , Yuto Iwasaki , Hiroshi Nakao , Yasufumi Sakai

Boron subphthalocyanine chloride is an electron donor material used in small molecule organic photovoltaics with an unusually large molecular dipole moment. Using first-principles calculations, we investigate enhancing the electronic and…

Materials Science · Physics 2017-05-08 Michael J. Waters , Daniel Hashemi , Guangsha Shi , Emmanouil Kioupakis , John Kieffer

Photonic technologies have shown a promising way to build high-speed and high-energy-efficiency neural network accelerators. In previously presented photonic neural networks, architectures are mainly designed for fully-connected layers.…

Signal Processing · Electrical Eng. & Systems 2020-03-02 Shaofu Xu , Jing Wang , Weiwen Zou

Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-distribution accuracy after fine-tuning, their reliability under…

Computational Physics · Physics 2026-01-14 Chengqian Zhang , Duo Zhang , Anyang Peng , Mingyu Guo , Yuzhi Zhang , Lei Wang , Guolin Ke , Linfeng Zhang , Tiejun Li , Han Wang

The thermodynamics of the Stranski-Krastanov mode of epitaxial growth and the effect of the sign of the lattice misfit are discussed. The Stranski-Krastanov mode of growth represents a sequence of layer-by-layer or Frank-van der Merwe…

Materials Science · Physics 2017-11-10 J. E. Prieto , I. Markov

Designing metal-organic frameworks (MOFs) synthesis protocols is currently largely driven by trial-and-error, since we lack fundamental understanding of the molecular level mechanisms that underlie their self-assembly processes. Previous…

Materials Science · Physics 2025-12-04 Sahar Andarzi Gargari , Emilio Méndez , Rocio Semino

Solution of Ordinary Differential Equation (ODE) model of dynamical system may not agree with its observed values. Often this discrepancy can be attributed to unmodeled forcings in the evolution rule of the dynamical system. In this…

Computational Engineering, Finance, and Science · Computer Science 2021-08-13 Saurabh Dixit , Soumyendu Raha

The accuracy of the information that can be extracted from electron diffraction patterns is often limited by the presence of optical distortions. Existing distortion characterization techniques typically require knowledge of the reciprocal…

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT)…

Non-ideal MHD effects are thought to be a crucial component of the star-formation process. Numerically, several complications render the study of non-ideal MHD effects in 3D simulations extremely challenging and hinder our efforts of…

Astrophysics of Galaxies · Physics 2025-02-12 E. Agianoglou , A. Tritsis , K. Tassis

Using kinetic Monte-Carlo simulations of a Solid-on-Solid model we investigate the influence of step edge diffusion (SED) and evaporation on Molecular Beam Epitaxy (MBE). Based on these investigations we propose two strategies to optimize…

Materials Science · Physics 2009-10-31 S. Schinzer , M. Sokolowski , M. Biehl , W. Kinzel

Chemical vapor deposition (CVD) has been established as a versatile route for the large-scale synthesis of transition metal dichalcogenides, such as tungsten disulfide (WS2). Yet, the role of the precursor composition on the efficiency of…

Advancing Machine Learning (ML)-based perception models for autonomous systems necessitates addressing weak spots within the models, particularly in challenging Operational Design Domains (ODDs). These are environmental operating conditions…

Machine Learning · Computer Science 2024-09-02 Ahmed Hammam , Bharathwaj Krishnaswami Sreedhar , Nura Kawa , Tim Patzelt , Oliver De Candido