A machine learning algorithm was able to make completely new scientific discoveries just
by going through the old scientific papers.
Humans can not read all the research papers.
But machine learning is not having that limitation.
So the AI is able to do new scientific discovery just by seeing the connection between the
Papers which the Humans miss to see.
Researchers at the U.S. Department of Energy’s Berkeley Lab have shown that an algorithm
with no training in materials science can scan the text of millions of papers and uncover
new scientific knowledge.
The Research Team collected 3.3 million abstracts of published materials science papers and
fed them into an algorithm called Word2vec.
By analyzing relationships between words the algorithm was able to predict discoveries
of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates
for thermoelectric materials.
Without telling it anything about materials science, it learned concepts like the periodic
table and the crystal structure of metals.
The findings were published in the journal Nature.
The paper establishes that text mining of scientific literature can uncover hidden knowledge,
and that pure text-based extraction can establish basic scientific knowledge.
In every research field there’s 100 years of past research literature, and every week
dozens more studies come out.
A researcher can access only fraction of that.
The Berkeley Lab researchers thought, can machine learning do something to make use
of all this collective knowledge in an unsupervised manner – without needing guidance from human.
The team collected the 3.3 million abstracts from papers published in more than 1,000 journals
between 1922 and 2018.
Word2vec took each of the approximately 500,000 distinct words in those abstracts and turned
each into a 200-dimensional vector, or an array of 200 numbers.
When trained on materials science text, the algorithm was able to learn the meaning of
scientific terms and concepts such as the crystal structure of metals based simply on
the positions of the words in the abstracts and their co-occurrence with other words.
Word2vec was even able to learn the relationships between elements on the periodic table when
the vector for each chemical element was projected onto two dimensions.
So if Word2vec is so smart, could it predict novel thermoelectric materials?
A good thermoelectric material can efficiently convert heat to electricity and is made of
materials that are safe, abundant and easy to produce.
The Berkeley Lab team took the top thermoelectric candidates suggested by the algorithm, which
ranked each compound by the similarity of its word vector to that of the word “thermoelectric.”
Then they ran calculations to verify the algorithm’s predictions.
Of the top 10 predictions, they found all had computed power factors slightly higher
than the average of known thermoelectrics; the top three candidates had power factors
at above the 95th percentile of known thermoelectrics.
Next they tested if the algorithm could perform experiments “in the past” by giving it
abstracts only up to, say, the year 2000.
The results were surprising.
This study shows that if this algorithm were in place earlier, some materials could have
conceivably been discovered years in advance.
Along with the study the researchers are releasing the top 50 thermoelectric materials predicted
by the algorithm.
Up next, the team is working on a smarter, more powerful search engine, allowing researchers
to search abstracts in a more useful way.