The System That Cried Wolf: Sensor Security Analysis of Wide-area Smoke Detectors for Critical Infrastructure

Fire alarm and signaling systems are a networked system of fire detectors, fire control units, automated fire extinguishers, and fire notification appliances. Malfunction of these safety-critical cyber-physical systems may lead to chaotic evacuations, property damages, and even loss of human life. Therefore, reliability is one of the most crucial factors for fire detectors. Indeed, even a single report of a fire cannot be ignored considering the importance of early fire detection and suppression. In this paper, we show that wide-area smoke detectors, which are globally installed in critical infrastructures such as airports, sports facilities, and auditoriums, have significant vulnerabilities in terms of reliability; one can remotely and stealthily induce false fire alarms and suppress real fire alarms with a minimal attacker capability using simple equipment. The practicality and generalizability of these vulnerabilities has been assessed based on the demonstration of two types of sensor attacks on two commercial-off-the-shelf optical beam smoke detectors from different manufacturers. Further, the practical considerations of building stealthy attack equipment has been analyzed, and an extensive survey of almost all optical beam smoke detectors on the market has been conducted. In addition, we show that the current standards of the fire alarm network connecting the detector and a control unit exacerbate the problem, making it impossible or very difficult to mitigate the threats we found. Finally, we discuss hardware and software-based possible countermeasures for both wide-area smoke detectors and the fire alarm network; the effectiveness of one of the countermeasures is experimentally evaluated

Inverse Design of Solid-State Materials via a Continuous Representation

The non-serendipitous discovery of materials with targeted properties is the ultimate goal of materials research, but to date, materials design lacks the incorporation of all available knowledge to plan the synthesis of the next material. This work presents a framework for learning a continuous representation of materials and building a model for new discovery using latent space representation. The ability of autoencoders to generate experimental materials is demonstrated with vanadium oxides via rediscovery of experimentally known structures when the model was trained without them. Approximately 20,000 hypothetical materials are generated, leading to several completely new metastable V_xO_y materials that may be synthesizable. Comparison with genetic algorithms suggests computational efficiency of generative models that can explore chemical compositional space effectively by learning the distributions of known materials for crystal structure prediction. These results are an important step toward machine-learned inverse design of inorganic functional materials using generative models

Unveiling new stable manganese based photoanode materials via theoretical high-throughput screening and experiments

With the increasing energy demand, developing renewable fuel production strategies such as photoelectrocatalytic hydrogen production is critical to mitigating the global climate change. In this work, we experimentally validate a new stable and photoactive material, Mg₂MnO₄, from the exhaustive theoretical exploration of the chemical space of X (=Mg and Ca), Mn and O

Inverse Design of Solid-State Materials via a Continuous Representation

The non-serendipitous discovery of materials with targeted properties is the ultimate goal of materials research, but to date, materials design lacks the incorporation of all available knowledge to plan the synthesis of the next material. This work presents a framework for learning a continuous representation of materials and building a model for new discovery using latent space representation. The ability of autoencoders to generate experimental materials is demonstrated with vanadium oxides via rediscovery of experimentally known structures when the model was trained without them. Approximately 20,000 hypothetical materials are generated, leading to several completely new metastable V_xO_y materials that may be synthesizable. Comparison with genetic algorithms suggests computational efficiency of generative models that can explore chemical compositional space effectively by learning the distributions of known materials for crystal structure prediction. These results are an important step toward machine-learned inverse design of inorganic functional materials using generative models

Accelerated chemical science with AI

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of ‘Accelerated Chemical Science with AI’ at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: ‘Data’, ‘New applications’, ‘Machine learning algorithms’, and ‘Education’. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions

ConfigurationStabilityPredictionML

* Trained model parameters - RandomSeed_[random_seed]/[model_name]/Model_[num_dat]_ [id,ood_ads,ood_cat,ood_both].pth.tar  - [random_seed]: 1111,2222,3333,4444,5555 (five independent trials for random extraction of training set) - [model_name]: CGCNN,SchNet,MPNN,MPNN_A,Matforer - [num_dat]: 10000(2%),46032(10%),115082(25%),230164(50%),345246(75%) * Trained model parameters with whole training dataset (#: 460328) can be found in RandomSeed_1111/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar </p

A structure translation model for crystal compounds

Abstract High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively. During this process, the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory (DFT) calculations. Here, we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains, enabling data-driven structural translations. The model predicts the materials formation energy with a small mean absolute error without DFT relaxations, and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures. The utility of the proposed concept is not restricted to the structural domains, and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties