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Unlocking Information Security I: From Cryptography to Buffer Overflows
Quantum Mechanics for Everyone
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Discover how the AIMD-L at Johns Hopkins University enables high-throughput automated characterization of materials in extreme environments, featuring unique capabilities like laser microflyer impact studies and advanced x-ray diffraction systems.
Explore how computational tools provide insights into monomer-to-monomer recycling of polymers, focusing on poly(dikeotenamine)s (PDKs) that achieve over 90% monomer yield through acid-catalyzed hydrolysis mechanisms.
Explore AniSOAP, an anisotropic generalization of SOAP for machine learning in atomistic simulations, enabling better representation of macromolecular systems and orientation-dependent interactions between atom groups.
Explore how machine learning and quantum chemistry combine to create faster, more accurate molecular property predictions, with applications in pKa prediction and solar cell performance.
Discover how machine learning models can accurately reconstruct conical intersections by learning globally smooth invariant quantities, crucial for understanding molecular dynamics upon light excitation.
Explore how machine learning and AI-driven microscopy techniques are revolutionizing materials science by uncovering structure-property relationships, extracting physical laws, and enabling autonomous research workflows for accelerated materials discover…
Explore advanced techniques for modeling chemical reactions in solution, focusing on explicit solvent interactions and their impact on transition metal complexes and catalysis.
Explore algorithms for constructing phase diagrams and predicting morphology in multicomponent liquid mixtures, with applications from industry to cellular biology.
Explore advanced modeling of solvated electrons using machine learning, overcoming challenges in accuracy and efficiency to study structure, dynamics, and properties across temperatures.
Explore a quantum algorithm for accelerating Metropolis sampling in multidimensional systems, leveraging wave function collapses for non-local configuration space moves.
Explore crystallographic analysis of lunar iron crystals, examining unique deltoidal icositetrahedron faceting and its implications for understanding crystal growth in the lunar environment.
Explore quantum information scrambling, its quantification through Lyapunov exponents, and the role of path integrals in understanding universal bounds on chaos in thermal quantum systems.
Explore quantum-mechanical and machine learning advances in molecular simulations, their potential impact, and challenges in achieving fully quantum dynamics of complex biomolecular systems.
Explore ab initio charge transport in ionic fluids, combining invariance principles and topological quantization to redefine oxidation numbers and electrical conductivity calculations.
Explore weak convergence in invertible coarse-graining, combining force-matching with neural networks to recover fine-grained free energy surfaces in biomolecular systems.
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