The process of finding new materials, optimal for a given application, is lengthy, often
unpredictable, and has a low throughput. Here I will describe a collection of numerical methods,
merging advanced electronic structure theory and machine learning, for the discovery of novel
compounds, which demonstrates an unprecedented throughput and discovery speed. This is
applied here to magnetism, but it can be used for any materials class and potential application.
Firstly, I will discuss a machine-learning scheme for predicting the Curie temperature of
ferromagnets, which uses solely the chemical composition of a compound as feature and
experimental data as target. In particular, I will discuss how to develop meaningful feature
attributes for magnetism and how these can be informed by experimental and theoretical
results. Furthermore, I will show how the experimental data can be mined from published
scientific literature with the help of natural language processing tools.
Prof. Sanvito studied Physics in Milan, Italy, (“Laurea”) and Lancaster, UK (PhD). After two years
at the University of California Santa Barbara, in 2002 he joined the School of Physics at Trinity
College Dublin. In 2006 he became associated Professor and in 2012 Professor of Condensed
Matter Theory. Since 2013 he is the Director of the Center for Research on Adaptive
Nanostructures and Nanodevices (CRANN) and for the period 2013-2015 he has been the
Director of the AMBER Center.
Prof. Sanvito leads the internationally recognized Computational Spintronics Group, which
develops new algorithms for materials and device modeling and applies them to problems
underpinning information technology. One of Sanvito’s flagship achievements is the Smeagol
code, the world-leading software for simulating devices at the atomic scale. Smeagol, distributed
worldwide to more than 200 groups, has impacted a multitude of technologies ranging from data
storage to DNA sequencing. Prof. Sanvito is author of more than 300 papers (h-index 77, Google
Scholars), 3 books and numerous book chapters and he has attracted funding in excess of
70,000,000 Euro (including the 50M AMBER center).
In 2007 he received the Young Scientist Prize in Computational Physics from the International
Union of Pure and Applied Physics (IUPAP) and in 2012 the prestigious European Research
Council Award. Prof. Sanvito is a Fellow of the Institute of Physics, a fellow of Trinity College
Dublin and a Member of the Royal Irish Academy. In 2017 he was conferred the title of “Cavaliere
della Stella d’Italia” (knight of the star ofItaly), an Italian knighthood order given to Italians abroad,
who have contributed to enhance the prestige of Italy and to establish relations with foreign
countries. In 2020, 2021 and 2022 he was included in the list of the Highly Cited Researchers from
Clarivat TM.
The process of finding new materials, optimal for a given application, is lengthy, often
unpredictable, and has a low throughput. Here I will describe a collection of numerical methods,
merging advanced electronic structure theory and machine learning, for the discovery of novel
compounds, which demonstrates an unprecedented throughput and discovery speed. This is
applied here to magnetism, but it can be used for any materials class and potential application.
Firstly, I will discuss a machine-learning scheme for predicting the Curie temperature of
ferromagnets, which uses solely the chemical composition of a compound as feature and
experimental data as target. In particular, I will discuss how to develop meaningful feature
attributes for magnetism and how these can be informed by experimental and theoretical
results. Furthermore, I will show how the experimental data can be mined from published
scientific literature with the help of natural language processing tools.
Prof. Sanvito studied Physics in Milan, Italy, (“Laurea”) and Lancaster, UK (PhD). After two years
at the University of California Santa Barbara, in 2002 he joined the School of Physics at Trinity
College Dublin. In 2006 he became associated Professor and in 2012 Professor of Condensed
Matter Theory. Since 2013 he is the Director of the Center for Research on Adaptive
Nanostructures and Nanodevices (CRANN) and for the period 2013-2015 he has been the
Director of the AMBER Center.
Prof. Sanvito leads the internationally recognized Computational Spintronics Group, which
develops new algorithms for materials and device modeling and applies them to problems
underpinning information technology. One of Sanvito’s flagship achievements is the Smeagol
code, the world-leading software for simulating devices at the atomic scale. Smeagol, distributed
worldwide to more than 200 groups, has impacted a multitude of technologies ranging from data
storage to DNA sequencing. Prof. Sanvito is author of more than 300 papers (h-index 77, Google
Scholars), 3 books and numerous book chapters and he has attracted funding in excess of
70,000,000 Euro (including the 50M AMBER center).
In 2007 he received the Young Scientist Prize in Computational Physics from the International
Union of Pure and Applied Physics (IUPAP) and in 2012 the prestigious European Research
Council Award. Prof. Sanvito is a Fellow of the Institute of Physics, a fellow of Trinity College
Dublin and a Member of the Royal Irish Academy. In 2017 he was conferred the title of “Cavaliere
della Stella d’Italia” (knight of the star ofItaly), an Italian knighthood order given to Italians abroad,
who have contributed to enhance the prestige of Italy and to establish relations with foreign
countries. In 2020, 2021 and 2022 he was included in the list of the Highly Cited Researchers from
Clarivat TM.
