TY - JOUR
T1 - Inverse Design of MXenes for High-Capacity Energy Storage Materials Using Multi-Target Machine Learning
AU - Li, Sichao
AU - Barnard, Amanda S.
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - There is significant interest in discovering high-capacity battery materials, prompting the investigation of the electrochemical energy storage potential of the two-dimensional early transition metal carbides known as MXenes. Predicting the relationship between the composition of a MXene and electrochemical properties is a focus of considerable research. In this paper we classify the specific MXene chemical formula using a new categorical descriptor and simultaneously predict multiple target electrochemical properties. We then invert the design challenge and predict the formula for MXenes based on a set of battery performance criteria. This approach involves a workflow that includes multi-target regression and multi-target classification, focusing on the physicochemical features most pertinent to battery design. The final inverse model recommends Li2M2C and Mg2M2C (M = Sc, Ti, Cr) as candidates for more focused research, based on desirable ranges of gravimetric capacity, voltage, and induced charge.
AB - There is significant interest in discovering high-capacity battery materials, prompting the investigation of the electrochemical energy storage potential of the two-dimensional early transition metal carbides known as MXenes. Predicting the relationship between the composition of a MXene and electrochemical properties is a focus of considerable research. In this paper we classify the specific MXene chemical formula using a new categorical descriptor and simultaneously predict multiple target electrochemical properties. We then invert the design challenge and predict the formula for MXenes based on a set of battery performance criteria. This approach involves a workflow that includes multi-target regression and multi-target classification, focusing on the physicochemical features most pertinent to battery design. The final inverse model recommends Li2M2C and Mg2M2C (M = Sc, Ti, Cr) as candidates for more focused research, based on desirable ranges of gravimetric capacity, voltage, and induced charge.
UR - http://www.scopus.com/inward/record.url?scp=85131718048&partnerID=8YFLogxK
U2 - 10.1021/acs.chemmater.2c00200
DO - 10.1021/acs.chemmater.2c00200
M3 - Article
SN - 0897-4756
VL - 34
SP - 4964
EP - 4974
JO - Chemistry of Materials
JF - Chemistry of Materials
IS - 11
ER -