The phrase "are essential for predicting" is correct and usable in written English.
You can use it when discussing the importance of certain factors or variables in making predictions in various contexts, such as science, economics, or data analysis.
Example: "The data collected from previous experiments are essential for predicting future outcomes in the study."
Alternatives: "are crucial for forecasting" or "are vital for anticipating".
Wave-optical calculations are essential for predicting the X-ray focusing performance of precisely figured elliptical mirrors.
Implicit algorithms are essential for predicting the slow growth and saturation of global instabilities in today's magnetically confined fusion plasma experiments.
Therefore, detailed and reliable baseline data about the mountainous environment of the Japanese Alps, and its effects on the cryosphere, are essential for predicting the future hydrosphere of Japan.
Special attention is devoted to the description of release mechanisms and recent developments in modeling release which are essential for predicting nutrient release under real conditions and which can provide the technologists with tools for better design of CRFs.
Consistent conceptual models across disciplines are essential for predicting emergent processes in the subsurface, such as self-focusing of flow, time-dependent deformation (creep), fracture network development, and wellbore stability.
Updated incidence data are essential for predicting health care demand, for evaluating continuing needs for prevention programs such as asbestosis abatement, and to foster our understanding of possible changes in presumed risk factors over time.
This understanding not only makes it possible to explain quantitatively what is observed in laboratory experiments but is essential for predicting how new processes and devices work.
Therefore, the transformation kinetics formulation is essential for predicting the thermo-mechanical behavior of SMAs.
Thus, understanding fuel bed response to prescribed fire is essential for predicting future forest function.
A deep knowledge about factors affecting airborne pollen is essential for predicting and understanding its dynamics.
Knowledge of reaction kinetics is essential for predicting substrate utilization and product generation.
