How Machine Learning
Enables Accurate Predictionof Precursor Volatility
Challenges inpredicting volatility
A crucial process in manufacturing CPUs and otherhigh-tech devices is the deposition of solid material
from reactive vapors. Different precursor vapors areused for chemical vapor deposition, vapor phase
epitaxy, atomic layer deposition — and indeed thereverse process of atomic layer etching — with theprecursor chemistry carefully designed for each caseso as to control material quality at the nanoscale. But
what all these techniques have in common is that theprecursor chemicals must evaporate or sublime ata low enough temperature. Too much heating whenvaporizing a precursor can make it decompose,causing it to be undeliverable to the growing surface.
With volatility playing such a central role in thistechnology (and in other fields like distillation,
refrigeration, inkjet printing, food and perfumes),
it is surprising that we understand so little about it.
Volatility is the product of a remarkably fine balanceof interatomic forces, dictating the extent to whichmolecules condense together as a solid or liquid, orbounce apart into a vapor and deliver a certain vapor
pressure at any given temperature. These interatomic
forces can be computed very precisely with quantum
mechanics for one molecule or a group of molecules,
but not at the scale of a liquid or solid. Even withtoday’s computing power, routinely and accurately
predicting precursor volatility ‘from first principles’
remains unfortunately out of reach.

Machine learning approaches
Could an alternative more empirical approach prove
useful? Does enough experimental data exist to findthe relation between volatility and chemical structure?
The vaporization of some organic molecules, such as
alcoholic fractions or natural fragrances, has been ofinterest for centuries and high-quality vapor pressure
data are available in the literature. Over the last
decade, these data have been analyzed with advanced
fitting algorithms that come under the umbrella of‘machine learning’. Schrödinger has leveraged thelatest machine learning techniques to develop a highlyaccurate model that predicts the volatility of organic
molecules up to C20.
However, when building machine learning models topredict volatility of precursor molecules, which aretypically organometallic complexes, the situation is
not so straightforward. New precursor molecules areconstantly being proposed and evaluated. Commercial
sensitivity sometimes means that data are partiallywithheld, or plagued by experimental configurationdifferences from laboratory to laboratory. Additionally,
for the common aim of material processing, completepressure-temperature curves are rarely measured,as it is more pragmatic to focus on the temperature
for vapor to transport successfully to the reactor. Asa result, datasets for building predictive models aresparse and incomplete.

Prediction of volatility
for inorganic andorganometallic complexes
Schrödinger scientists embarked on the challenge ofbuilding machine learning models to predict the volatilityof precursor molecules. Using in-house expertise inmachine learning and advanced informatics, Schrödinger
scientists collated and digitized information aboutorganometallic precursors from disparate literaturesources and applied a variety of machine learningalgorithms (such as Random Forest and Neural Networks)
in conjunction with different chemoinformatic descriptors
and fingerprints. The result is the first capability ofits kind for accurately and efficiently predicting thevolatility for inorganic and organometallic complexes
from their chemical structures. For complexes of thefifty most common metals and semimetals, the model
predicts the evaporation or sublimation temperature ata given vapor pressure with an average accuracy of ±9°C
(which is about 3% of the absolute temperature). As a
trained model, the turnaround time is fast with the ability
to compute hundreds of complexes per second.

New avenues for precursor development
This predictive model opens a new path for designing novel precursors with improved performance,
not only improving their deposition or etch chemistry, but also optimizing the temperature at which
they evaporate or sublime and can be delivered as a vapor. This advance will allow a much wider rangeof structural modifications to be screened computationally than before and will produce candidate
precursors for experimental synthesis and testing that are both less risky and more innovative. This
volatility model, together with Schrödinger’s quantum mechanics-based workflows for computation ofreactivity and decomposition, gives scientists a complete design kit for vapor-phase deposition or etch,delivering a faster pace of research into materials and processes for new technologies.

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