Microscopic Origin of Nonlinear Optical Properties of 2D Materials: A First Principles Study

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have provided a unique materials platform with a variety of interesting optoelectronic properties and great potential for device applications. Janus 2D TMDCs represent a new class of 2D materials whose crystalline symmetry and physical properties can be tailored via Janus structure engineering. Here we present our first-principles study of nonlinear optical properties in Janus 2D TMDCs. Electronic structures such as linear and nonlinear optical properties were calculated using first-principles density functional theory and analyzed in combination with group theory. The microscopic origin of these nonlinear optical properties of Janus TMDCs is elaborated by k-point resolved optical absorption, shift current, and shift vector. We found that the absence of horizontal mirror plane in Janus 2D materials enables the out-of-plane second harmonic generation (SHG) and other nonlinear optical phenomena, such as shift photocurrent and circular photocurrent. Janus 2D materials, therefore, offer a unique platform for exploring nonlinear optical phenomena and designing configurable layered nonlinear optical materials

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmark

Detecting Illicit Drug Ads in Google+ Using Machine Learning

Opioid abuse epidemics is a major public health emergency in the US. Social media platforms have facilitated illicit drug trading, with significant amount of drug advertisement and selling being carried out online. In order to understand dynamics of drug abuse epidemics and design efficient public health interventions, it is essential to extract and analyze data from online drug markets. In this paper, we present a computational framework for automatic detection of illicit drug ads in social media, with Google+ being used for a proof-of-concept. The proposed SVM- and CNN-based methods have been extensively validated on the large dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that our methods can efficiently identify illicit drug ads with high accuracy. Both approaches have been extensively validated using the dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that both methods allow for accurate identification of illicit drug ads

Apaf-1 and caspase-9 do not act as tumor suppressors in myc-induced lymphomagenesis or mouse embryo fibroblast transformation

Based on experiments with cultured fibroblasts, the apoptosis regulators caspase-9 and Apaf-1 are hypothesized to function as tumor suppressors. To investigate their in vivo role in lymphomagenesis, an IgH enhancer-driven c-myc transgene was crossed onto Apaf-1−/− and caspase-9−/− mice. Due to perinatal lethality, Eμ-myc transgenic Apaf-1−/− or caspase-9−/− fetal liver cells were used to reconstitute lethally irradiated recipient mice. Surprisingly, no differences were seen in rate, incidence, or severity of lymphoma with loss of Apaf-1 or caspase-9, and Apaf-1 was not a critical determinant of anticancer drug sensitivity of c-myc–induced lymphomas. Moreover, loss of Apaf-1 did not promote oncogene-induced transformation of mouse embryo fibroblasts. Thus, Apaf-1 and caspase-9 do not suppress c-myc–induced lymphomagenesis and embryo fibroblast transformation

Shock compaction heating and collisional processes in the production of type 3 ordinary chondrites: Lessons from the (nearly) unique L3 chondrite melt breccia Northwest Africa 8709*

Northwest Africa (NWA) 8709 is a rare example of a type 3 ordinary chondrite melt breccia and provides critical information for the shock compaction histories of chondrites. An L3 protolith for NWA 8709 is inferred on the basis of oxygen isotope composition, elemental composition, diverse mineral chemistry, and overall texture. NWA 8709 is among the most strongly shocked type 3 chondrites known, and experienced complete melting of the matrix and partial melting of chondrules. Unmelted phases underwent FeO reduction and partial homogenization, with reduction possibly occurring by reaction of olivine and low‐Ca pyroxene with an S‐bearing gas that was produced by vaporization. Chondrules and metal grains became foliated by uniaxial compaction, and during compression, chondrules and fragments became attached to form larger clumps. This process, and possibly also melt incorporation into chondrules to cause “inflation,” may have contributed to anomalously large chondrule sizes in NWA 8709. The melt breccia character is attributed to strong shock affecting a porous precursor. Data‐model comparisons suggest that a precursor with 23% porosity that was impacted by a 3 km/s projectile could have produced the meteorite. The rarity of other type 3 ordinary chondrite melt breccias implies that the immediate precursors to such chondrites were lower in porosity than the NWA 8709 precursor, or experienced weaker shocks. Altogether, the data imply a predominantly “quiet” dynamical environment to form most type 3 ordinary chondrites, with compaction occurring in a series of relatively weak shock events

Transitions towards new economies? A transformative social innovation perspective

There are numerous social innovation networks and initiatives worldwide with the ambition to contribute to transformative change towards more sustainable, resilient and just societies. Many of these have a specific vision on the economy and relate to alternative visions of a ‘New Economy’. This paper highlights four prominent strands of new economy thinking in state-of-the-art discussions: degrowth, collaborative economy, solidarity economy, and social entrepreneurship. Taking a perspective of transformative social innovation, the paper draws on case studies of 12 social innovation initiatives to analyse how these relate to new economies and to transitions toward new economic arrangements. The 12 cases are analysed in terms of a) how they relate to narratives of change on new economies, b) how they renew social relations, and c) how their new economy arrangements hold potential to challenge established institutional constellations in the existing economy

TRANSIT Working Paper # 7

A previous version of this paper has been part of TRANSIT Deliverable 3.3 (July 2016), the second prototype of TSI theory.[Abstract] This working paper presents a set of propositions about the agency and dynamics of transformative social innovation (TSI) that have been developed as part of an EU-funded research project entitled “TRANsformative Social Innovation Theory” (TRANSIT; 2014-2017). These TSI propositions represent first steps towards the development of a new theory of TSI, taking the form of proto-explanations of the agency and dynamics of TSI, based on the bringing together of our empirical observations on TSI and the project's theoretical reviews and theoretical framings. Ideally this working paper should be read in conjunction with the working paper entitled “A framework for transformative social innovation” (Haxeltine et al 2016) which presents in skeletal terms the theoretical and conceptual framing of TSI developed in the TRANSIT project. This TSI framework builds on sustainability transition studies, social innovation research, social psychology studies of empowerment and other several other areas of social theory to deliver a bespoke theoretical and conceptual framework that is grounded in a relational ontology and which is being employed as a platform for the development of a middle-range theory of TSI. Next we provide a very brief overview of some key elements of the framework, in particular how we conceptualise social innovation, transformative change, and transformative social innovation. Propositions were developed for each of four relational dimensions implied by the TSI framework with also a brief statement of the topic addressed by each of the twelve propositions.This article is based on research carried out as part of the Transformative Social Innovation Theory (“TRANSIT”) project, which is funded by the European Union's Seventh Framework Programme (FP7) under grant agreement 61316

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