All articles starting with M
36,687 articles
Machine learning engineer
Machine learning engineer (ML engineer): software professional who designs, trains, deploys, and maintains machine learning models in production, distinct from data scientist and AI engineer; role emerged pre-2011, defined in frameworks like UK GDD, with median US salary near $165,000.
Machine learning for Earth observation
Machine learning for Earth observation is the use of deep neural networks to turn satellite and airborne imagery into products like land cover maps and flood extents.
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 materials science
Machine learning in materials science, often called materials informatics, uses models trained on materials data to predict properties, screen candidates, and guide synthesis and processing.
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 nonlinearity compensation
Machine learning nonlinearity compensation is the use of machine learning, chiefly neural networks, to reverse nonlinear signal distortion in optical fiber systems, mostly Kerr-effect distortion that limits long-haul transmission distance and capacity.
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 perfusion
Machine perfusion is a transplantation technique that circulates oxygenated fluid through a donor organ between retrieval and implantation, reducing injury compared with static cold storage on ice.
Machine pistol
A machine pistol is a pistol-style firearm capable of fully automatic or burst fire, firing pistol cartridges. Early examples appeared in Europe before 1918, and the best-known service example is the Soviet Stechkin APS.
Machine quilting
Machine quilting is the practice of stitching a quilt's top, batting, and backing together with a sewing machine rather than by hand, on a domestic machine or a longarm.
Machine taper
A machine taper is a standardized conical fitting that locates and holds tools on machine tool spindles, either self-holding by friction or self-releasing with a drawbar.
Machine to machine
Machine to machine (M2M) is direct communication between devices over a network with little or no human intervention, for tasks such as metering, monitoring, or control.
Machine tool
Machine tool: stationary power-driven machine that shapes metal and other materials by cutting, grinding, forming, or EDM; also called werkzeugmaschine. Key facts: JIS B 0105 excludes hand-held tools; tolerances to ±0.0025 mm; lathes, mills, machining centers, grinders; 'machines that produce machines.'
Machine translation
Machine translation (MT) is software that converts text or speech between natural languages without human translation; since the mid-2010s it has been dominated by neural approaches, following rule-based and statistical eras.
Machine unlearning
Machine unlearning is a family of techniques for removing specific knowledge or capabilities from a trained model's weights without retraining, pursued mainly as an AI safety method.
Machine vision
Machine vision is the industrial use of computer vision, combining cameras, lighting, sensors, and software to automate quality inspection, defect detection, and robotic guidance on production lines.
Machine vision for in situ monitoring of additive manufacturing
Machine vision for in situ monitoring of additive manufacturing uses cameras and machine-learning algorithms to detect melt pool behavior and defects during 3D printing in real time.
Machine-check exception
A machine-check exception is an x86 processor exception, delivered as interrupt 18, that tells the operating system the CPU detected a hardware error such as a bus, cache, or ECC error.
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.