Materials Design & Discovery

We design and discover materials through high-throughput computation combined with machine learning and AI. Adv. Energy Mater. 9, 1902078 (2019); Nat. Commun. 10, 5260 (2019); Nat. Commun. 14, 7615 (2023); Adv. Energy Mater. 10, 2002356 (2020); Joule 2, 2016-2046 (2018)

Unlike many computational studies that consider only a single property, our materials design and discovery accounts for the full, comprehensive set of aspects that determine whether a material actually works in application:

• Phase stability. Joule 2, 2016-2046 (2018)
• Electrochemical stability. ACS Appl. Mater. Interfaces 7, 23685-23693 (2015); J. Mater. Chem. A 4, 3253-3266 (2016); Adv. Sci. 1600517 (2017)
• Interface stability and compatibility, essential for materials to work in a device. ACS Appl. Mater. Interfaces 7, 23685-23693 (2015); J. Mater. Chem. A 4, 3253-3266 (2016)
• Interface coatings, to further stabilize interfaces so materials can work in a device. Adv. Sci. 1600517 (2017); ACS Energy Lett. 4, 2444-2451 (2019); Energy Storage Mater. 41, 571-580 (2021)
• Synthesizability. J. Am. Chem. Soc. 140, 17290-17296 (2018)
• Thermal stability, relevant to safety and processing. Joule 4(4), 812-821 (2020)
• Moisture stability, relevant to cost and processing. Angew. Chem. Int. Ed. 59, 17472 (2020)
• Doping, substitution, and mixture, often needed to improve materials properties and performance — computation can help prescreen candidates and narrow down the composition space. Ionics 24, 1139-1151 (2018); ACS Energy Lett. 9, 5334-5340 (2024); Adv. Energy Mater. 10, 2002356 (2020); Chem. Mater. 25, 3048-3055 (2013); Energy Environ. Sci. 6, 148-156 (2013); arXiv:2604.27120

Topics

• High-Throughput Computation and ML Materials Discovery Adv. Energy Mater. 9, 1902078 (2019); Nat. Commun. 10, 5260 (2019); Nat. Commun. 14, 7615 (2023); Adv. Energy Mater. 10, 2002356 (2020)
• Li-ion Conductors (Solid electrolytes) Adv. Energy Mater. 9, 1902078 (2019); Adv. Energy Mater. 9, 1803821 (2019)
• Na-ion Conductors (Solid electrolytes) Nat. Commun. 14, 7615 (2023)
• Halide Angew. Chem. Int. Ed. 59, 8039-8043 (2019); Adv. Energy Mater. 10, 2002356 (2020)
• Mixed-Anion Chemistries Science 390, 199-204 (2025)
• Mixed Ionic-Electronic Conductors (MIECs) Ionics 24, 1139-1151 (2018); ACS Energy Lett. 9, 5334-5340 (2024)
• Nitride Nat. Nanotechnol. 20, 265-275 (2024); Adv. Sci. 1600517 (2017); US Patents 12,278,332 & 11,581,572
• Oxygen-Ion Conductors (for SOFC/SOEC) Materials Today 86, 247-254 (2025)
• Proton Conductors (for SOFC/SOEC) Chem. Mater. 32, 5028-5035 (2020); Chem. Mater. 33, 8278-8288 (2021); Chem. Mater. 34, 5938-5948 (2022)

Computation Prediction Confirmed

• Computationally discovered the design principle of face-sharing high-coordination sites for fast Na-ion conduction by applying it to discover the UCl3-type Na_xM_yCl₆ (M = La–Sm) chloride family with conductivities Nat. Commun. 14, 7615 (2023) → experimentally validated as a Na-ion conductor of ~1–2 mS/cm (highest in Na halide) Adv. Mater. 36, 230812 (2024), and confirmed to also enable fast Li-ion conduction J. Am. Chem. Soc. 145, 2183-2194 (2023)
• Computationally predicted LiTaSiO5 (sphene-structured) as a new fast Li-ion conductor, guided by our concerted-migration and crystal-framework design principles Nat. Commun. 8, 15893 (2017); Adv. Energy Mater. 9, 1902078 (2019) → confirmed experimentally Adv. Energy Mater. 9, 1803821 (2019); Adv. Funct. Mater. 29, 1904232 (2019)
• Computationally established design principles for chloride superionic conductors — cation concentration and configuration govern Li-ion conduction in halides, with low cation concentration and sparse/disordered cation distribution enhancing Li-ion conductivity by reducing cation blocking of diffusion pathways Adv. Energy Mater. 10, 2002356 (2020) → confirmed experimentally: increasing cation concentration was shown to increase blocking of Li-ion migration in Li_xScCl_3+x J. Am. Chem. Soc. 142(15), 7012-7022 (2020); and reducing cation concentration raised conductivity in trigonal Li3YCl6 Science 382, 573-579 (2023)
• Computationally predicted that halide spinel structures could be converted into good Li-ion conductors by reducing cation concentration, as revealed by computational insights into the cation-blocking effect Adv. Energy Mater. 10, 2002356 (2020) → confirmed experimentally J. Am. Chem. Soc. 148, 692-704 (2026); Energy Environ. Sci. 13, 2056-2063 (2020)
• Revealed the trends in cation moisture stability from systematic first-principles thermodynamic screening of hydrolysis reactions, guiding cation selection for air-stable Li/Na solid electrolytes Angew. Chem. Int. Ed. 59, 17472 (2020) → confirmed industrially: a chloride solid electrolyte using the predicted best-moisture-stability cation combination (In/Zn/Cd) was patented for improved air stability Chinese Patent CN121748511A

Highlights

• Discovered mixed-anion oxyhalide Li-ion conductors and derivatives, realizing a structural framework not available in oxides or halides through the mixed-anion strategy — achieving the highest room-temperature conductivity ever reported for a mixed-anion halide (up to 13.7 mS/cm), and the first crystalline oxyhalide superionic conductor discovered, preceding the report by Tanaka et al. Science 390, 199-204 (2025)
• Achieved a world-record room-temperature ionic conductivity for halide solid electrolytes by tuning collective anion motion through mixed-anion (Cl/Br) substitution Nat. Chem. 16, 1584-1591 (2024), with mechanisms corroborated by our Density of Atomistic States (DOAS) simulations Angew. Chem. Int. Ed. 62, e202215544 (2023)

Related Talks

• Computational Design Principles for Na-Ion Conductors
• Machine-Learning Discovery of Li-Ion Conductors
• High-throughput Discovery of Li-Ion Conductors
• Computation Materials Design of Solid Electrolytes
• Electrochemical Stabilities of Solid Electrolytes
• Computational Design of Stable Coating for Solid-State Li-ion Batteries
• Computational Study on Interface Stability of All-Solid-State Li-ion Batteries