Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Investigating the Structure-Property Relationship of Relaxor Ferroelectrics via Machine Learning

Relaxors ferroelectrics are an intensively studied field of research that are of great interest owing to their large dielectric permittivity and electromechanical coupling. The polarization response of relaxors is believed to be correlated with the presence of polar nanoregions (PNRs) in the material, which give origin of their unique behavior. After decades of research, however, PNRs and their relationship to relaxor dynamics is a discussion that is still actively disputed. Given both the computational and experimental challenges that impede progress on the atomistic insight into PNRs dynamics, it is hypothesized that machine learning (ML), a nontraditional computational approach, is the way to tackle the problem. We expect that ML can be used to analyze the thousands of dipole patterns within PNRs produced by Molecular Dynamics (MD) simulations of relaxors and provide insight into their intrinsic dynamics. We begin by testing various ML toy models to classify or group the electric response of relaxors, which will allow for assessment of the ML algorithm performance for the given problem. The ML algorithms with the most promising performance will be applied to study the structure-property relationship in relaxors. The aims of this work are therefore to (i) gain insight into the presence and properties of polar nanoregions in relaxor ferroelectrics via ML and atomistic MD, (ii) demonstrate the potential of ML as a predictive tool in relaxor ferroelectrics research, and (iii) develop a multifunctional ML model that can be applied to a wide range of material properties originating from dipolar interactions

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands

Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multi-reference (MR) character that complicates property evaluation. We compute MR diagnostics for over 5,000 ligands present in previously synthesized transition metal complexes in the Cambridge Structural Database (CSD). To accomplish this task, we introduce an iterative approach for consistent ligand charge assignment for ligands in the CSD. Across this set, we observe that MR character correlates linearly with the inverse value of the averaged bond order over all bonds in the molecule. We then demonstrate that ligand additivity of MR character holds in TMCs, which suggests that the TMC MR character can be inferred from the sum of the MR character of the ligands. Encouraged by this observation, we leverage ligand additivity and develop a ligand-derived machine learning representation to train neural networks to predict the MR character of TMCs from properties of the constituent ligands. This approach yields models with excellent performance and superior transferability to unseen ligand chemistry and compositions

Temperature Dependence of Three-Dimensional Domain Wall Arrangement in Ferroelectric K0.9_{0.9}Na0.1_{0.1}NbO3_{3} Epitaxial Thin Films

The three-dimensional arrangement and orientation of domain walls in ferroelectric K$_{0.9}$Na$_{0.1}$NbO$_{3}$/(110)NdScO$_{3}$ epitaxial thin films wereinvestigated at different temperatures both experimentally by means of piezoresponse force microscopy and three-dimensional x-ray diffractionand theoretically by three-dimensional phase-field simulations. At room temperature, a well-ordered herringbone-like domain patternappears in which there is a periodic arrangement of a$_1$a$_2$/M$_C$ monoclinic phases. Four different types of domain walls are observed, whichcan be characterized by out-of-plane tilt angles of ±45° and in-plane twist angles of ±21°. For the orthorhombic high-temperature phase, aperiodic $_1$a$_2$ stripe domain pattern with exclusive in-plane polarization is formed. Here, two different types of domain walls are observed,both of them having a fixed out-of-plane domain wall angle of 90° but distinguished by different in-plane twist angles of ±45°. The experimentalresults are fully consistent with three-dimensional phase-field simulations using anisotropic misfit strains. The qualitative agreementbetween the experiment and the theory applies, in particular, to the wide phase transition range between about 180 °C and 260 °C. In thistemperature range, a complex interplay of coexisting monoclinic $_1$a$_2$/M$_C$ and orthorhombic $_1$a$_2$ phases takes place