Researchers at New York University have developed “Crystal Math,” a new mathematical method that can predict the structure of molecular crystals in hours using a standard laptop. This innovative framework stands to transform multiple industries, from pharmaceuticals to electronics.
A revolutionary mathematical framework from New York University researchers promises to drastically reduce the time required to predict the structures of molecular crystals. Published in the journal Nature Communications, this method — dubbed “Crystal Math” — can achieve in hours what once took supercomputers weeks or even months.
“The time to solution is no longer weeks to months — we can get a solution overnight because solving the equations is relatively quick,” senior author Mark Tuckerman, a professor of chemistry and mathematics at NYU, said in a news release.
Crystal Structures and Their Importance
Crystals, which are integral to numerous products such as pharmaceuticals, semiconductors and electronic devices, pose a significant challenge in predicting their structures due to their complex nature. The ability to quickly and accurately predict these structures is a critical step in developing new drugs, electronics and even explosives.
In the 1990s, for instance, the HIV drug ritonavir had to be pulled from the market after its capsules transformed into a more stable but unknown crystal form, rendering the medicine ineffective. This underscores the necessity for robust and speedy crystal structure prediction.
Limitations of Traditional Methods
Traditional methods to predict crystal structures often rely on physics-based approaches that are not only time-consuming and computationally expensive but also introduce biases and errors. They can also predict more crystal forms than actually occur, leading to inefficiencies.
“These physics-based approaches — which are costly and time-consuming — produce predictions that are only as accurate as the physics you put into them, which is why there has been a push toward computational methods that can address this shortcoming,” added Tuckerman.
The Birth of Crystal Math
To address these challenges, Tuckerman and Nikolaos Galanakis, an NYU postdoctoral researcher, developed Crystal Math.
This method utilizes mathematical rules and simple physical descriptors to predict how molecules pack into crystals. It effectively solves 13 basic parameters related to the arrangement of molecules in the crystal, such as molecular location, orientation and geometry.
The researchers validated their approach using the Cambridge Crystal Data Centre, a comprehensive database of known molecular crystal structures. They tested their hypothesized rules against this database to refine principles that are most likely to predict real-world structures.
Wider Applications and Industry Implications
From common pharmaceuticals, like aspirin and paracetamol, to more complex molecular crystals, Crystal Math has shown high accuracy. The pharmaceutical industry, in particular, stands to benefit significantly from this advancement, as the ability to predict crystal structures swiftly can expedite the development of new drugs.
“The very ability to develop new products relies on knowing if the compounds that constitute them will crystallize, how many crystal forms are possible and the stability of these various forms,” Tuckerman added. “With our mathematical approach, it is possible to test the ability of many compounds to crystallize and determine if these structures are suitable for ultimate deployment on the market.”
This pioneering solution is the result of seven years of meticulous work by Tuckerman and Galanakis. Their protocol, inspired by a 1967 paper by Swiss mathematician Johann Jakob Burckhardt, has drawn significant interest from the pharmaceutical industry and beyond.
Looking Forward
As Crystal Math continues to prove its efficacy, the research community anticipates further innovations and applications across various industries. The advent of such rapid, accurate predictions may well spearhead a new era in material science, pharmaceuticals and electronic device manufacturing.