It's a strong contender for the geekiest video ever made: a
close-up of a smartphone with line upon line of numbers and
symbols scrolling down the screen. But when visitors stop by
Nicola Marzari's office, which overlooks Lake Geneva, he can hardly
wait to show it off. “It's from 2010,” he says, “and this is my
cellphone calculating the electronic structure of silicon in real time!”
Even back then, explains Marzari, a physicist at the Swiss Federal
Institute of Technology in Lausanne (EPFL), Switzerland, his now-ancient
handset took just 40 seconds to carry out quantum-mechanical calculations
that once took many hours on a supercomputer — a feat that not only
shows how far such computational methods have come in the past decade
or so, but also demonstrates their potential for transforming the way
materials science is done in the future.
Instead of continuing to develop new materials the old-fashioned
way — stumbling across them by luck, then painstakingly measuring their
properties in the laboratory — Marzari and like-minded researchers are
using computer modelling and machine-learning techniques to generate
libraries of candidate materials by the tens of thousands. Even data
from failed experiments can provide useful input1. Many of these candidates
are completely hypothetical, but engineers are already beginning to shortlist
those that are worth synthesizing and testing for specific applications by
searching through their predicted properties — for example, how well they
will work as a conductor or an insulator, whether they will act as a
magnet, and how much heat and pressure they can withstand.
The hope is that this approach will provide a huge leap in the speed and
efficiency of materials discovery, says Gerbrand Ceder, a materials
scientist at the University of California, Berkeley, and a pioneer in
this field. “We probably know about 1% of the properties of existing
materials,” he says, pointing to the example of lithium iron
phosphate: a compound that was first synthesized2 in the 1930s, but
was not recognized3 as a promising replacement material for current-generation
lithium-ion batteries until 1996. “No one had bothered to measure its voltage before,” says Ceder.
At least three major materials databases already exist around the
world, each encompassing tens or hundreds of thousands of compounds.
Marzari's Lausanne-based Materials Cloud project is scheduled to launch
later this year. And the wider community is beginning to take notice.
“We are now seeing a real convergence of what experimentalists want and
what theorists can deliver,” says Neil Alford, a materials scientist who
serves as vice-dean for research at Imperial College London, but who has
no affiliation with any of the database projects.
As even the proponents are quick to point out, however, the journey from
computer predictions to real-world technologies is not an easy one. The
existing databases are far from including all known materials, let alone
all possible ones. The data-driven discovery works well for some
materials, but not for others. And even after an interesting material
is singled out on a computer, synthesizing it in a laboratory can still
take years. “We often know better what we should be making than how to
make it,” says Ceder.
Still, researchers in this field are confident that there is a trove of
compounds waiting to be discovered, which could kick-start innovations in
electronics, energy, robotics, health care and transportation.“Our community
is putting together a lot of different parts of the puzzle,” says Giulia
Galli, a computational materials scientist at the University of Chicago
in Illinois. “And when they all click into place, materials prediction will become a reality.”
Genetic inspiration
The idea for this high-throughput, data-driven approach to materials
discovery hit Ceder in the early 2000s, when he was at the Massachusetts
Institute of Technology (MIT) in Cambridge and found himself inspired by
the nearly completed Human Genome Project. “By itself, the human genome
was not a recipe for new treatments,” he says, “but it gave medicine
amazing amounts of basic, quantitative information to start from.” Could
materials scientists learn some lessons from geneticists, he wondered. Could
they identify a “materials genome” that encodes the properties of various
compounds in the same way that biological information is encoded in DNA base pairs?
If so, he reasoned, that encoding must lie in the atoms and electrons that
make up a given material, and in their crystal structure: the way they are
arranged in space. In 2003, Ceder and his team first showed4 how a database
of quantum-mechanics calculations could help to predict the most likely
crystal structure of a metal alloy — a key step for anyone in the business
of inventing new materials.
In the past, these calculations had been long and difficult, even for
supercomputers. The machine had to go through an inordinate amount of
trial and error to find the 'ground state': the crystal structure and
electron configuration in which the energy was at a minimum and all the
forces were in equilibrium. But in their 2003 paper4, Ceder's team described
a shortcut. The researchers calculated the energies of common crystal
structures for a small library of binary alloys — mixes of two different
metals — and then designed a machine-learning algorithm that could extract
patterns from the library and guess the most likely ground state for a new
alloy. The algorithm worked well, slashing the computer time required for
the calculations (see 'Intelligent search').
“That paper introduced the idea of a public library of materials properties,
and of using data mining to fill the missing parts,” says Stefano Curtarolo,
who that same year left Ceder's group to start his own laboratory at Duke
University in Durham, North Carolina. The idea then gave birth to two
separate projects. In 2006, Ceder started the Materials Genome Project at
MIT, using improved versions of the algorithm to predict lithium-based
materials for electric-car batteries. By 2010, the project had grown to
include around 20,000 predicted compounds. “We started from existing
materials and modified their crystal structure — changing one element
here or another one there and calculating what happens,” says Kristin
Persson, a former member of Ceder's team who continued to collaborate on
the project after she moved to the Lawrence Berkeley National Laboratory
in California in 2008.
At Duke, meanwhile, Curtarolo set up the Center for Materials Genomics, which focused on
research on metal alloys. Teaming up with researchers from Brigham Young University in
Provo, Utah, and Israel's Negev Nuclear Research Center, he gradually expanded the 2003
algorithm and library into AFLOW, a system that can perform calculations on known crystal
structures and predict new ones automatically5.
Researchers from outside the original group were getting interested in high-throughput
computations as well. One such researcher was chemical engineer Jens Nørskov, who
started using them to study catalysts for breaking down water into hydrogen and
oxygen6 while he was at the Technical University of Denmark in Lyngby, and later
expanded the work as director of the SUNCAT Center for the computational study of
catalysis at Stanford University in California. Another was Marzari, who was part
of a large team developing Quantum Espresso: a program for quantum-mechanics calculations
that was launched7 in 2009. That is the code running on his mobile phone in the video.
Materials genomics
Still, computational materials science did not become mainstream until June 2011, when
the White House announced the multimillion-dollar Materials Genome Initiative (MGI).
“When people at the White House became familiar with Ceder's work they got very excited,”
says James Warren, a materials scientist at the US National Institute of Standards and
Technology and executive secretary of the MGI. “There was a general awareness that computer
simulations had got to the point where they could have a real impact on innovation and
manufacturing,” he says — not to mention the 'genomics' name, “which was evocative
of something grand.”
Since 2011, the initiative has invested more than US$250 million into software tools,
standardized methods to collect and report experimental data, centres for computational
materials science at major universities and partnerships between universities and the
business sector for research on specific applications. But it is unclear how far this
largesse has actually advanced the science. “The initiative brought a lot of good things,
but also some re-branding,” says Ceder. “Some groups started calling their research
genomics this and genomics that, even though it had little to do with it.”
One thing the MGI definitely did do, however, was to help Ceder and others realize their
vision of an online database of materials properties. In late 2011, Ceder and Persson
relaunched their Materials Genome Project as the Materials Project — having been asked
by the White House to give up the 'genome' label to avoid confusion with the national
effort. The following year, Curtarolo posted his own database, called AFLOWlib, based
on the software he had developed at Duke8. And in 2013, Chris Wolverton, a materials
researcher at Northwestern University in Evanston, Illinois, launched the Open Quantum
Materials Database (OQMD)9. “We borrowed the general idea from the Materials Project
and AFLOWlib,” says Wolverton, “but our software and data are homegrown.”
All three of these databases share a core of around 50,000 known materials taken from a
widely used experimental library, the Inorganic Crystal Structure Database. These are
solids that have been created at least once in a laboratory and described in a paper,
but whose electronic or magnetic properties may have never been fully tested; they are
the starting point from which new materials can be derived.
We are now seeing a real convergence of what experimentalists want and what theorists can deliver.
Where the three databases differ is in the hypothetical materials they include. The
Materials Project has relatively few, starting with some 15,000 computed structures
derived from Ceder's and Persson's research on lithium batteries. “We only include
them in the database if we're confident the calculations are accurate, and if there is
a reasonable chance that they can be made,” says Persson, who is now director of the
Materials Project and has a joint affiliation with the University of California, Berkeley.
Another 130,000 or so entries are structures predicted by the Nanoporous Materials Genome
Center at the University of Minnesota in Minneapolis. The latter focuses on zeolites and
metal–organic frameworks: sponge-like materials with regularly repeating holes in their
crystal structures that can trap gas molecules
and could be used to store methane or carbon dioxide.
AFLOWlib is the largest database, featuring more than a million different materials
and about 100 million calculated properties. That's because it also includes hundreds
of thousands of hypothetical materials, many of which would exist for only a fraction
of a second in the real world, says Curtarolo. “But it pays off when you want to predict
how a material can actually be manufactured,” he says. For example, he is using data
from AFLOWlib to study why some alloys can form metallic glass — a peculiar form of
metal with a disordered microscopic structure that gives it special electric and magnetic
properties. It turns out that the difference between good glass formers and bad ones depends
on the number and energies of unstable crystal structures that 'compete' with the ground
state while the alloy cools down10.
Wolverton's OQMD includes around 400,000 hypothetical materials, calculated by taking a
list of crystal structures commonly observed in nature and 'decorating' them with elements
chosen from almost every part of the periodic table9. It has a particularly wide coverage
of perovskites — crystals that often display attractive properties such as superconductivity
and that are being developed for use in solar cells as microelectronics. As the name
suggests, this project is the most open of the three: users can download the entire
database, not just individual search results, onto their computer.
All of these databases are works in progress, and their curators still spend a
good share of their time adding more compounds and refining the calculations — which,
they admit, are far from perfect. The codes tend to be quite good at predicting whether
a crystal is stable or not, but less good at predicting how it absorbs light or conducts
electricity — to the point of sometimes making a semiconductor look like a metal.
Marzari notes that even for battery materials, an area in which computational materials
science is having its best success stories, standard calculations still have an average
error of half a volt, which makes a lot of difference in terms of performance. “The
truth is, some errors come with the theory itself: we may never be able to correct
them,” says Curtarolo.
Each group is developing its own techniques to adjust the calculations and make up for
these systematic errors. But in the meantime they are already doing science with the data — and
so are users from other groups. The Materials Project has identified several promising cathodes
that may work better than existing ones in lithium batteries11, as well as metal oxides that
could improve the efficiency with which solar cells capture sunlight and turn it into
energy12. And earlier this year, researchers from Trinity College Dublin used the AFLOWlib
database to predict 20 Heusler alloys, a class of magnets that can be used for sensors or
computer memories, and managed to synthesize two of them, confirming that their magnetic
properties are very close to the predictions.
European expansion
Materials genomics has also crossed over to Europe — although usually by other
names. Switzerland, for example, has created MARVEL, a network of institutes for
computational materials science with the EPFL as its lead and Marzari as
director. Using a new computational platform13, he is creating a database called
Materials Cloud that he is using to search for 'two-dimensional' materials, such
as graphene, that are made from just a single layer of atoms or molecules. Such
materials could be used in applications ranging from nanoscale electronics to biomedical
devices. To find good candidates, Marzari is subjecting more than 150,000 known materials
to what he calls 'computational peeling': calculating how much energy it would take to
separate a single layer from the surface of an ordinary crystal. By the time the database
is ready for public release later this year, he expects that preliminary runs will have
yielded some 1,500 potential two-dimensional structures that can then be tested in experiments.
A few kilometres away in Sion, high in the Swiss Alps, computational chemist Berend
Smit has set up another EPFL centre that develops algorithms for predicting hundreds
of thousands of nanoporous zeolites and metal–organic frameworks. Other
algorithms — including one that scans for certain pore shapes using techniques
derived from facial-recognition software — then seek out the best candidates for
absorbing carbon dioxide from the flues of fossil-fuel power plants14.
The truth is, some errors come with the theory itself: we maynever be able to correct them.
Smit's work also shows that materials genomics can bring bad news. Many researchers
had hoped to use nanoporous materials to build car tanks that could store more methane
in less space. But after screening more than 650,000 computed materials, Smit's group
concluded that most of the best ones have already been made15. New ones could bring
only minor improvements, and energy targets currently set by US agencies — which bet
on major technological improvements in methane storage — may be unrealistic.
As intriguing as these examples are, there are still many hurdles to overcome before
materials genomics can live up to its promises. One of the largest is that computer
simulations still give few clues on how an interesting material can be made in a
lab — let alone mass produced. “We come up with interesting ideas for new compounds
all the time,” says Ceder. “Sometimes it takes two weeks to make it. Other times we
still can't make it after six months, and we don't know whether we haven't done the
right thing, or it just can't be made.”
Both Ceder and Curtarolo are trying to develop machine-learning algorithms to extract
rules from known manufacturing processes to guide the synthesis of compounds.
Another limitation is that materials genomics has been hitherto applied almost
exclusively to what engineers call functional materials — compounds that can
perform a task such as absorbing light in a solar cell or letting electrical
current pass in transistor. But the technique does not lend itself well to studying
structural materials, such as steel, that are needed to build, for example, aircraft
wings, bridges or engines. This is because mechanical properties such as a material's
springiness and hardness depend on how it is processed — something that quantum-mechanical
codes by themselves can not describe.
Even in the case of functional materials, current computer codes work well
only for perfect crystal structures — which are only a small part of the
materials realm. “The most interesting materials of the future will probably
be assembled at the microscopic level in creative ways,” says Galli. They may
be assemblies of nanoparticles, crystals with strategically placed defects in
their structures, or heterogenous materials made by intertwining different compounds
and phases. To predict such materials, says Galli, “you need to calculate many
properties at once and how the system will evolve in time and at specific
temperatures”. There are methods to do that, she says, “but they are still
too computationally expensive to be used in high-throughput studies”.
In the short term, more data exchange with experiments can give computations
a reality check and help to refine them. To that end, Ceder is working with a
group at MIT on software that reads papers in experimental materials science
and automatically extracts information on crystal structures in a standard
format. “We plan to begin adding these data to the Materials Project in a few months,” he says.
And in the long run, some help will come from Moore's law: as computational
power continues to increase, some techniques that are out still of reach for
current computers may soon become viable.
“We've moved away from the artisanal era of computational materials science,
and into the industrial phase,” says Marzari. “We can now create assembly
chains of simulations, put them to work, and explore problems in totally new
ways.” No computationally predicted material is on the market just yet. “But
let's talk again in ten years,” says Galli, “and I think there will be many.”