42,772 articles
Mach–Zehnder modulator
The Mach–Zehnder modulator (MZM) is an electro-optic device that splits light into two arms and recombines it, converting a voltage-controlled phase shift into intensity modulation.
Mach's principle
Mach's principle is the proposition, named by Einstein in 1918 from Ernst Mach's 1883 critique of Newton, that inertia is determined by the distribution of all other matter in the universe.
Machine epsilon
Machine epsilon is the smallest positive number that, when added to 1.0 in a floating-point system, produces a result different from 1.0, measuring the system's precision.
Machine learning collective variables
Machine learning collective variables are low-dimensional functions of a molecular system's atomic coordinates, learned from simulation data rather than chosen by hand, used mainly to guide enhanced sampling.
Machine learning earthquake detection
Machine learning earthquake detection uses trained neural networks to find earthquake signals in seismic recordings and pick P and S wave arrival times, replacing manual analysis.
Machine learning in catalysis
Machine learning in catalysis is the use of statistical learning algorithms, trained on experimental or computational data, to screen catalysts, predict performance, and optimize reaction conditions.
Machine learning in chemistry
Machine learning in chemistry applies statistical learning algorithms to predict molecular and material properties, plan syntheses, and interpret chemical data, aided by large chemical databases.
Machine learning in materials discovery
Machine learning in materials discovery, also called materials informatics, uses statistical models trained on experimental, computational, or literature-derived data to predict material properties and screen candidate compounds.
Machine learning in physics
Machine learning in physics applies supervised, unsupervised, and physics-informed learning to classify phases of matter, locate phase transitions, and learn order parameters, from lattice models to polymers and quantum many-body systems.
Machine learning in quantum chemistry
Machine learning in quantum chemistry trains statistical models on quantum-mechanical calculations to predict molecular and materials properties, replacing expensive electronic-structure computations like density functional theory.
Machine learning interatomic potential
Machine-learning interatomic potentials (MLIPs) are models trained on quantum-mechanical data such as density functional theory to predict atomic energies and forces, driving molecular dynamics simulations of millions of atoms.
Machine learning phases of matter
Machine learning phases of matter is a condensed-matter research program in which neural networks identify phases and phase transitions directly from configurations, without a prescribed order parameter.
Machine learning prediction of solid-state reaction products
Machine learning prediction of solid-state reaction products uses models trained on literature synthesis data and thermodynamics to forecast reaction outcomes, including target compounds, impurity phases, and heating conditions.
Machine learning QM/MM
Machine learning QM/MM is a molecular simulation strategy that replaces the quantum region of a QM/MM model with a machine learning potential, keeping near-quantum accuracy at far lower cost.
Machine learning-guided catalyst optimization
Machine learning-guided catalyst optimization uses algorithms, typically Bayesian optimization, to propose and evaluate catalysts and reaction conditions, often in closed-loop self-driving laboratories that replace human trial-and-error screening.
Machine-learned coarse-graining
Machine-learned coarse-graining uses neural networks to build coarse-grained molecular models trained on atomistic simulation data, extending simulations beyond all-atom reach, with CGnets (2019) the founding method.
Machine-learned density functional theory
Machine-learned density functional theory (ML-DFT) replaces approximate DFT functionals like exchange-correlation with learnable models, or trains fast surrogates on DFT datasets to predict material properties orders of magnitude faster.
Machine-learned density functionals
Machine-learned density functionals are exchange-correlation or kinetic-energy functionals of density functional theory fitted to reference data by machine learning, aiming to improve accuracy at low cost.
Machine-learned interatomic potential
A machine-learned interatomic potential (MLIP) is a model trained on quantum-mechanical data, usually density functional theory, to predict atomic energies and forces, combining first-principles accuracy with classical force field speed.
Machine-learned softness of glasses
Machine-learned softness of glasses is a machine-learning descriptor computed per particle that predicts where disordered solids will undergo rearrangement, introduced in 2016 by Liu and colleagues.
MACHO
MACHO, short for massive astrophysical compact halo object, is a non-luminous compact object proposed as dark matter in the Milky Way halo, tested by microlensing surveys in the 1990s.
Maciej Lewenstein
Maciej Lewenstein, born in Warsaw in 1955, is a Polish theoretical physicist in quantum optics and ultracold atoms, best known for the 1994 Lewenstein model of high-harmonic generation.
Mackinawite
Mackinawite is a soft, black iron sulfide mineral, FeS, first described in 1964 from the Mackinaw mine in Washington and acting as the precursor to greigite and pyrite.
MacMahon partition analysis
MacMahon partition analysis is a combinatorial method introduced by Percy MacMahon in 1915 that counts solutions of linear Diophantine inequalities via the Omega operator, revived by George Andrews in the late 1990s.
Macquart relation
The Macquart relation is the observed link between a fast radio burst's dispersion measure and its host galaxy's redshift, established in a 2020 Nature study led by J.-P. Macquart.
Macrogol
Macrogol, sold as MiraLax, Movicol, and Laxido, is the pharmacopoeial name for polyethylene glycol (PEG), a water-soluble polymer used as an osmotic laxative, bowel preparation, and pharmaceutical excipient.
Macrolactonization
Macrolactonization is a macrocyclization reaction that forms a large-ring lactone, usually by intramolecular esterification of a hydroxy acid, and closes the ring in syntheses of macrolide antibiotics like erythromycin.
Macromolecular crystallography
Macromolecular crystallography is a diffraction method that determines the three-dimensional atomic structures of proteins, nucleic acids, and their complexes from X-ray diffraction patterns of crystallized samples, the workhorse of structural biology.
Macromolecule
A macromolecule, or large molecule, is a molecule of high molecular mass made of repeated units, including plastics and biological molecules like proteins and DNA.
Macroscopic quantum coherence
Macroscopic quantum coherence is the phenomenon in which a large object, typically a superconducting circuit, holds a coherent quantum superposition of two classically distinct states, proposed by Anthony Leggett in 1980.