Although ferromagnets are found in all kinds of technological applications, only few substances are known to be intrinsically ferromagnetic at room temperature. In the past twenty years, a plethora of new artificial ferromagnetic materials have been found by introducing defects into non-magnetic host materials. In contrast to the intrinsic ferromagnetic materials, they offer an outstanding degree of material engineering freedom, provided one finds a type of defect to functionalize every possible host material to add magnetism to its intrinsic properties. Still, one controversial question remains: Are these materials really technologically relevant ferromagnets? To answer this question, in this work the emergence of a ferromagnetic phase upon ion irradiation is systematically investigated both theoretically and experimentally. Quantitative predictions are validated against experimental data from the literature of SiC hosts irradiated with high energy Ne ions and own experiments on low energy Ar ion irradiation of TiO$_2$ hosts. In the high energy regime, a bulk magnetic phase emerges, which is limited by host lattice amorphization, whereas at low ion energies an ultrathin magnetic layer forms at the surface and evolves into full magnetic percolation. Lowering the ion energy, the magnetic layer thickness reduces down to a bilayer, where a perpendicular magnetic anisotropy appears due to magnetic surface states.
Artificial Intelligence and Generative Models for Materials Discovery -- A Review
Institute of Sustainability for Chemicals, Energy and Environment (ISCE 2 )（化学、能源与环境可持续性研究所）；Agency for Science, Technology and Research (A*STAR)（科技研究局）；Institute of Materials Research and Engineering (IMRE)（材料研究与工程研究所）
When Literature Data Mislead Artificial Intelligence in Materials Discovery
Advanced Institute for Materials Research (WPI-AIMR), Tohoku University（东北大学材料研究高等研究院（WPI-AIMR））；The University of Tokyo（东京大学）；Institute for Materials Research, Tohoku University（东北大学材料研究所）
Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation
For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural and man-made materials in response to mechanical loading. Now, computer science offers a universal solution: Neural Networks. Neural Networks are powerful function approximators that can learn constitutive relations from large data without any knowledge of the underlying physics. However, classical Neural Networks ignore a century of research in constitutive modeling, violate thermodynamic considerations, and fail to predict the behavior …
Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models
Tohoku University（东北大学）；National University of Singapore（新加坡国立大学）；University at Buffalo（布法罗纽约州立大学）；London School of Economics（伦敦政治经济学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of Minho（米尼奥大学）；UPV/EHU（巴斯克大学）；Suzhou MatSource Technology Co., Ltd.（苏州迈特源科技有限公司）；Gusu Laboratory of Materials（姑苏材料实验室）；Indian Institute of Technology Hyderabad（印度海得拉巴理工学院）；Yonsei University（延世大学）
Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
University of Toronto（多伦多大学）；University of Tennessee（田纳西大学）；Vector Institute for Artificial Intelligence（人工智能矢量研究所）；Schwartz Reisman Institute for Technology and Society（技术与社会斯瓦茨-雷曼研究所）
Sustainable Materials Discovery in the Era of Artificial Intelligence
Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi（印度理工学院德里分校土木与环境工程系）；Department of Civil and Environmental Engineering, Imperial College London（帝国理工学院伦敦分校土木与环境工程系）；Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras（印度理工学院马德拉斯分校冶金与材料工程系）；Department of Computer Science and Engineering, Chalmers University of Technology & University of Gothenburg（查尔姆斯理工大学与哥德堡大学计算机科学与工程系）；Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度理工学院德里分校亚里学校人工智能系）
Materials Expert-Artificial Intelligence for Materials Discovery
Department of Physics, Cornell University, Ithaca, NY 14853, USA；Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States；Department of Chemistry, North Carolina State University, Raleigh, NC, USA；New York University, New York, NY 10011, USA；Fraunhofer Research Institution for Materials Recycling；Department of Physics, Ewha Womans University, Seoul, South Korea
Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
Laboratory for Atomistic and Molecular Mechanics（原子分子力学实验室）；Department of Biological Engineering（生物工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Civil and Environmental Engineering（土木与环境工程系）；Department of Mechanical Engineering（机械工程系）；Center for Computational Science and Engineering（计算科学与工程中心）；Schwarzman College of Computing（施瓦茨曼计算学院）
Knowledge Lab, University of Chicago（芝加哥大学知识实验室）；Santa Fe Institute（圣塔菲研究所）
Data-driven artificial intelligence models fed with published scientific findings have been used to create powerful prediction engines for scientific and technological advance, such as the discovery of novel materials with desired properties and the targeted invention of new therapies and vaccines. These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention. As a result, AI hypotheses are designed to substitute for human experts, failing to complement them for punctuated collecti…
Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, USA（跨学科研究中心，田纳西大学，诺克斯维尔，美国）；University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, USA（田纳西大学橡树岭创新研究所，田纳西大学，诺克斯维尔，美国）
AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence
Massachusetts Institute of Technology（麻省理工学院）；Center for Computational Science and Engineering（计算科学与工程中心）；Schwarzman College of Computing（计算学院（施瓦曼计算学院））
