arXiv subjects

Federico Rossi

Publications and source records attributed to Federico Rossi.

At least 19 recordsLinked to original sources

arXiv2026

Resolving the ground state intersection problem in coupled cluster theory

A physically correct description of ground state conical intersections is essential for simulating non-adiabatic dynamics of radiationless decay to the electronic ground state. Coupled cluster theory offers a particularly balanced treatment of static and dynamic correlation in excited states. However, coupled cluster methods and other single-reference approaches fail to describe ground state intersection regions, and several workarounds have been proposed to address this shortcoming. Here we present convex similarity constrained coupled cluster theory (CVX-SCC), a framework that resolves the ground state intersection problem by construction. We have applied this theory to two different coupled cluster methods, and tested the resulting models on ethylene, uracil, PSB3, and HeH$_2$, showing that both variants produce physically correct ground state conical intersections. With future development of nuclear gradients and non-adiabatic coupling vectors, this approach will provide single-reference electronic structures suitable for non-adiabatic dynamics simulations all the way to the ground state.

physics.chem-ph↗

arXiv2026

Robustness Analysis of POMDP Policies to Observation Perturbations

Policies for Partially Observable Markov Decision Processes (POMDPs) are often designed using a nominal system model. In practice, this model can deviate from the true system during deployment due to factors such as calibration drift or sensor degradation, leading to unexpected performance degradation. This work studies policy robustness against deviations in the POMDP observation model. We introduce the Policy Observation Robustness Problem: to determine the maximum tolerable deviation in a POMDP's observation model that guarantees the policy's value remains above a specified threshold. We analyze two variants: the sticky variant, where deviations are dependent on state and actions, and the non-sticky variant, where they can be history-dependent. We show that the Policy Observation Robustness Problem can be formulated as a bi-level optimization problem in which the inner optimization is monotonic in the size of the observation deviation. This enables efficient solutions using root-finding algorithms in the outer optimization. For the non-sticky variant, we show that when policies are represented with finite-state controllers (FSCs) it is sufficient to consider observations which depend on nodes in the FSC rather than full histories. We present Robust Interval Search, an algorithm with soundness and convergence guarantees, for both the sticky and non-sticky variants. We show this algorithm has polynomial time complexity in the non-sticky variant and at most exponential time complexity in the sticky variant. We provide experimental results validating and demonstrating the scalability of implementations of Robust Interval Search to POMDP problems with tens of thousands of states. We also provide case studies from robotics and operations research which demonstrate the practical utility of the problem and algorithms.

cs.AI↗

arXiv2026

Restoring the Conical Intersection Topology using Convex Density Functional Theory

Conical intersections are central to the description of photophysics and photochemistry. Nevertheless, in non-adiabatic molecular dynamics simulations, they are fundamentally challenging for single-reference electronic structure methods. Density functional theory (DFT) and its time-dependent extension (TDDFT) represent the most widely used theoretical approaches in physics, chemistry, and biology. However, the treatment of ground and excited states as separate problems leads to breakdowns in the topological structure of potential energy surfaces near conical intersections. In this work, we solve this long-standing issue by presenting Convex DFT, a framework that, by explicitly enforcing convexity of the variational problem within an appropriately defined subspace, guarantees a unique and continuous electronic solution across regions of degeneracies. We demonstrate that Convex DFT yields smooth and physically meaningful intersection seams by comparison with multireference wave function methods. In this way, we establish the method as a robust and computationally efficient DFT approach for treating electronically degenerate regions. These developments represent a critical step toward reliable non-adiabatic simulations beyond the limitations of conventional TDDFT.

physics.chem-ph↗

arXiv2026

Coupled cluster theory for positron binding in anions and polyatomic molecules

We present the positron coupled cluster singles and doubles (POS-CCSD) method to calculate positron binding energies in molecules. This framework treats electrons and positrons on an equal footing and includes up to simultaneous double-electron-single-positron excitations. We benchmark the approach by computing binding energies for atomic anions and several polar and non-polar polyatomic systems, comparing the results with independent theoretical studies and, where available, experimental data. The fully converged results for H$^{-}$ are in excellent agreement with quantum Monte Carlo and multi-reference configuration interaction results. Quantitative agreement with experiments is not reached in the present study due to the slow convergence of the binding energy with respect to the size of the orbital bases for the electrons and the positron. However, the POS-CCSD results underscore the critical role of electron correlation in the description of electron-positron systems required for a balanced description of these complex systems. In addition, we examine nuclear relaxation effects following positron attachment in LiH.

physics.chem-ph↗

arXiv2025

eT 2.0: An efficient open-source molecular electronic structure program

The eT program is an open-source electronic structure program with emphasis on performance and modularity. As its name suggests, the program features extensive coupled cluster capabilities, performing well compared to other electronic structure programs, and, in some cases, outperforming commercial alternatives. However, eT is more than a coupled cluster program; other models based on wave function theory (such as full and reduced space configuration interaction and a variety of self-consistent field models) and density functional theory are supported. The second major release of the program, eT 2.0, has specialized functionality for strong light-matter coupling conditions. In addition, it includes a wide range of optimizations and algorithmic improvements, as well as new capabilities for exploring potential energy surfaces and for modeling experiments in the UV and X-ray regimes. Molecular gradients are now available at the coupled cluster level, and high-accuracy spectroscopic simulations are available at reduced computational cost within the multilevel coupled cluster and multiscale frameworks. We present the modifications to the program since its first major release, eT 1.0, highlighting some notable new features and demonstrating the performance of the new version relative to the first release and to other established electronic structure programs.

physics.chem-ph↗

arXiv2025

Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks

We present a fully decentralized routing framework for multi-robot exploration missions operating under the constraints of a Lunar Delay-Tolerant Network (LDTN). In this setting, autonomous rovers must relay collected data to a lander under intermittent connectivity and unknown mobility patterns. We formulate the problem as a Partially Observable Markov Decision Problem (POMDP) and propose a Graph Attention-based Multi-Agent Reinforcement Learning (GAT-MARL) policy that performs Centralized Training, Decentralized Execution (CTDE). Our method relies only on local observations and does not require global topology updates or packet replication, unlike classical approaches such as shortest path and controlled flooding-based algorithms. Through Monte Carlo simulations in randomized exploration environments, GAT-MARL provides higher delivery rates, no duplications, and fewer packet losses, and is able to leverage short-term mobility forecasts; offering a scalable solution for future space robotic systems for planetary exploration, as demonstrated by successful generalization to larger rover teams.

stat.ML↗

arXiv2025

Microscaling Floating Point Formats for Large Language Models

The increasing computational and memory demands of large language models (LLMs) necessitate innovative approaches to optimize resource usage without compromising performance. This paper leverages microscaling floating-point formats, a novel technique designed to address these challenges by reducing the storage and computational overhead associated with numerical representations in LLMs. Unlike traditional floating-point representations that allocate a dedicated scale for each value, microscaling employs a shared scale across a block of values, enabling compact one-byte floating-point representations while maintaining an extended dynamic range. We explore the application of microscaling in the context of 8-bit floating-point formats to significantly reduce memory footprint and computational costs. We tested several configurations of microscaling floats within the GPT-2 LLM architecture, demonstrating that microscaling data formats can achieve competitive accuracy during training and inference, proving its efficacy as a resource-efficient alternative for deploying LLMs at scale. The source code is publicly available at: https://github.com/unipi-dii-compressedarith/llm.c-sve

cs.NE↗

arXiv2025

Convex Hartree-Fock theory: A simple framework for ground state conical intersections

Accurate modeling of conical intersections is crucial in nonadiabatic molecular dynamics, as these features govern processes such as radiationless transitions and photochemical reactions. Conventional electronic structure methods, including Hartree-Fock, density functional theory, and their time-dependent extensions, struggle in this regime. Due to their single reference nature and separate treatment of ground and excited states, they fail to capture ground state intersections. Multiconfigurational approaches overcome these limitations, but at a prohibitive computational cost. In this work, we propose a modified Hartree-Fock framework, referred to as Convex Hartree-Fock, that optimizes the reference within a tailored subspace by removing projections along selected Hessian eigenvectors. The ground and excited states are then obtained through subsequent Hamiltonian diagonalization. We validate the approach across several test cases and benchmark its performance against time-dependent Hartree-Fock within the Tamm-Dancoff approximation.

physics.chem-ph↗

arXiv2025

SpinAdaptedSecondQuantization.jl 1.0 -- A Simple and Pedagogical Approach to Symbolic Quantum Chemistry

The development of new electronic structure methods is a very time consuming and error prone process when done by hand. SpinAdaptedSecondQuantization is an open-source Julia package we have developed for working with automated electronic structure theory development. The code focuses on being user-friendly and extensible, allowing for easy use of both user- and pre-defined fermionic and/or bosonic operators, tensors, and orbital spaces. This allows the code to be used to efficiently investigate and prototype new electronic structure methods for many different types of systems. This includes both exotic systems with wave functions consisting of different kinds of particles at once, as well as new parametrizations for traditional many-electron systems. The code is spin-adapted, working directly with spin-adapted fermionic operators, and can easily be used to derive common electronic structure theory equations and expressions, such as the coupled cluster energy, ground and excited state equations, one- and two-electron density matrices, etc. Additionally, the code can translate expressions into code, accelerating the process of going from ideas to implemented methods.

physics.chem-ph↗

arXiv2025

Microstructure of Silicon Anodes in Solid-State Batteries -- From Crystalline to Amorphous

Silicon offers great promise as a potential anode active material and the optimum alternative to lithium metal in all-solid-state lithium-ion batteries. However, its practical application is limited by severe volume expansion (~300%) during lithiation, leading to cracking upon delithiation. In this study, we investigated the microstructural evolution of microcrystalline silicon electrodes in a solid-electrolyte-free environment using cryogenic scanning transmission electron microscopy (STEM) during electrochemical cycling. A controlled workflow prevents ambient exposure, and cryo-TEM ensures structural integrity. After the first lithiation, the electrode shows a heterogeneous mix of crystalline Li15Si4, various amorphous LixSi phases, and residual crystalline silicon. After delithiation, the structure becomes predominantly amorphous with thread-like features and minimal remaining crystallinity. By the 10th delithiation, the microstructure is more uniform, with thread-like regions mainly at grain boundaries. Our results reveal that starting from a crystalline phase, a stationary microstructure emerges in bulk silicon only after several cycles. Thus, to have a more controlled behavior of the electrode and minimize cracking, the starting material should be carefully chosen along with an optimized electrode architecture to help stabilize the microstructure throughout cycling.

cond-mat.mtrl-sci↗

arXiv2025

Planning, scheduling, and execution on the Moon: the CADRE technology demonstration mission

NASA's Cooperative Autonomous Distributed Robotic Exploration (CADRE) mission, slated for flight to the Moon's Reiner Gamma region in 2025/2026, is designed to demonstrate multi-agent autonomous exploration of the Lunar surface and sub-surface. A team of three robots and a base station will autonomously explore a region near the lander, collecting the data required for 3D reconstruction of the surface with no human input; and then autonomously perform distributed sensing with multi-static ground penetrating radars (GPR), driving in formation while performing coordinated radar soundings to create a map of the subsurface. At the core of CADRE's software architecture is a novel autonomous, distributed planning, scheduling, and execution (PS&E) system. The system coordinates the robots' activities, planning and executing tasks that require multiple robots' participation while ensuring that each individual robot's thermal and power resources stay within prescribed bounds, and respecting ground-prescribed sleep-wake cycles. The system uses a centralized-planning, distributed-execution paradigm, and a leader election mechanism ensures robustness to failures of individual agents. In this paper, we describe the architecture of CADRE's PS&E system; discuss its design rationale; and report on verification and validation (V&V) testing of the system on CADRE's hardware in preparation for deployment on the Moon.

cs.RO↗

arXiv2024

Generalized coupled cluster theory for ground and excited state intersections

Coupled cluster theory in the standard formulation is unable to correctly describe conical intersections among states of the same symmetry. This limitation has restricted the practical application of an otherwise highly accurate electronic structure model, particularly in nonadiabatic dynamics. Recently, the intersection problem among the excited states was fully characterized and resolved. However, intersections with the ground state remain an open challenge, and addressing this problem is our objective here. We present a generalized coupled cluster framework that correctly accounts for the geometric phase effect and avoids bifurcations of the solutions to the ground state equations. Several applications are presented that demonstrate the correct description of ground state conical intersections. We also propose how the framework can be used for other electronic-structure methods.

physics.chem-ph↗

arXiv2024

Distributed Instruments for Planetary Surface Science: Scientific Opportunities and Technology Feasibility

In this paper, we assess the scientific promise and technology feasibility of distributed instruments for planetary science. A distributed instrument is an instrument designed to collect spatially and temporally correlated data from multiple networked, geographically distributed point sensors. Distributed instruments are ubiquitous in Earth science, where they are routinely employed for weather and climate science, seismic studies and resource prospecting, and detection of industrial emissions. However, to date, their adoption in planetary surface science has been minimal. It is natural to ask whether this lack of adoption is driven by low potential to address high-priority questions in planetary science; immature technology; or both. To address this question, we survey high-priority planetary science questions that are uniquely well-suited to distributed instruments. We identify four areas of research where distributed instruments hold promise to unlock answers that are largely inaccessible to monolithic sensors, namely, weather and climate studies of Mars; localization of seismic events on rocky and icy bodies; localization of trace gas emissions, primarily on Mars; and magnetometry studies of internal composition. Next, we survey enabling technologies for distributed sensors and assess their maturity. We identify sensor placement (including descent and landing on planetary surfaces), power, and instrument autonomy as three key areas requiring further investment to enable future distributed instruments. Overall, this work shows that distributed instruments hold great promise for planetary science, and paves the way for follow-on studies of future distributed instruments for Solar System in-situ science.

astro-ph.EP↗

arXiv2024

Stochastic Guidance of Buoyancy Controlled Vehicles under Ice Shelves using Ocean Currents

We propose a novel technique for guidance of buoyancy-controlled vehicles in uncertain under-ice ocean flows. In-situ melt rate measurements collected at the grounding zone of Antarctic ice shelves, where the ice shelf meets the underlying bedrock, are essential to constrain models of future sea level rise. Buoyancy-controlled vehicles, which control their vertical position in the water column through internal actuation but have no means of horizontal propulsion, offer an affordable and reliable platform for such in-situ data collection. However, reaching the grounding zone requires vehicles to traverse tens of kilometers under the ice shelf, with approximate position knowledge and no means of communication, in highly variable and uncertain ocean currents. To address this challenge, we propose a partially observable MDP approach that exploits model-based knowledge of the under-ice currents and, critically, of their uncertainty, to synthesize effective guidance policies. The approach uses approximate dynamic programming to model uncertainty in the currents, and QMDP to address localization uncertainty. Numerical experiments show that the policy can deliver up to 88.8% of underwater vehicles to the grounding zone -- a 33% improvement compared to state-of-the-art guidance techniques, and a 262% improvement over uncontrolled drifters. Collectively, these results show that model-based under-ice guidance is a highly promising technique for exploration of under-ice cavities, and has the potential to enable cost-effective and scalable access to these challenging and rarely observed environments.

cs.RO↗

arXiv2024

Recursively-Constrained Partially Observable Markov Decision Processes

Many sequential decision problems involve optimizing one objective function while imposing constraints on other objectives. Constrained Partially Observable Markov Decision Processes (C-POMDP) model this case with transition uncertainty and partial observability. In this work, we first show that C-POMDPs violate the optimal substructure property over successive decision steps and thus may exhibit behaviors that are undesirable for some (e.g., safety critical) applications. Additionally, online re-planning in C-POMDPs is often ineffective due to the inconsistency resulting from this violation. To address these drawbacks, we introduce the Recursively-Constrained POMDP (RC-POMDP), which imposes additional history-dependent cost constraints on the C-POMDP. We show that, unlike C-POMDPs, RC-POMDPs always have deterministic optimal policies and that optimal policies obey Bellman's principle of optimality. We also present a point-based dynamic programming algorithm for RC-POMDPs. Evaluations on benchmark problems demonstrate the efficacy of our algorithm and show that policies for RC-POMDPs produce more desirable behaviors than policies for C-POMDPs.

cs.AI↗

arXiv2024

Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability Objectives

Partially Observable Markov Decision Processes (POMDPs) are powerful models for sequential decision making under transition and observation uncertainties. This paper studies the challenging yet important problem in POMDPs known as the (indefinite-horizon) Maximal Reachability Probability Problem (MRPP), where the goal is to maximize the probability of reaching some target states. This is also a core problem in model checking with logical specifications and is naturally undiscounted (discount factor is one). Inspired by the success of point-based methods developed for discounted problems, we study their extensions to MRPP. Specifically, we focus on trial-based heuristic search value iteration techniques and present a novel algorithm that leverages the strengths of these techniques for efficient exploration of the belief space (informed search via value bounds) while addressing their drawbacks in handling loops for indefinite-horizon problems. The algorithm produces policies with two-sided bounds on optimal reachability probabilities. We prove convergence to an optimal policy from below under certain conditions. Experimental evaluations on a suite of benchmarks show that our algorithm outperforms existing methods in almost all cases in both probability guarantees and computation time.

cs.AI↗

arXiv2024

PPU: Design and Implementation of a Pipelined Full Posit Processing Unit

By exploiting the modular RISC-V ISA this paper presents the customization of instruction set with posit\textsuperscript{\texttrademark} arithmetic instructions to provide improved numerical accuracy, well-defined behavior and increased range of representable numbers while keeping the flexibility and benefits of open-source ISA, like no licensing and royalty fee and community development. In this work we present the design, implementation and integration into the low-power Ibex RISC-V core of a full posit processing unit capable to directly implement in hardware the four arithmetic operations (add, sub, mul, div and fma), the inversion, the float-to-posit and posit-to-float conversions. We evaluate speed, power and area of this unit (that we have called Full Posit Processing Unit). The FPPU has been prototyped on Alveo and Kintex FPGAs, and its impact on the metrics of the full-RISC-V core have been evaluated, showing that we can provide real number processing capabilities to the mentioned core with an increase in area limited to $7\%$ for 8-bit posits and to $15\%$ for 16-bit posits. Finally we present tests one the use of posits for deep neural networks with different network models and datasets, showing minimal drop in accuracy when using 16-bit posits instead of 32-bit IEEE floats.

cs.AR↗

arXiv2023

Optimizing pre-scheduled, intermittently-observed MDPs

A challenging category of robotics problems arises when sensing incurs substantial costs. This paper examines settings in which a robot wishes to limit its observations of state, for instance, motivated by specific considerations of energy management, stealth, or implicit coordination. We formulate the problem of planning under uncertainty when the robot's observations are intermittent but their timing is known via a pre-declared schedule. After having established the appropriate notion of an optimal policy for such settings, we tackle the problem of joint optimization of the cumulative execution cost and the number of state observations, both in expectation under discounts. To approach this multi-objective optimization problem, we introduce an algorithm that can identify the Pareto front for a class of schedules that are advantageous in the discounted setting. The algorithm proceeds in an accumulative fashion, prepending additions to a working set of schedules and then computing incremental changes to the value functions. Because full exhaustive construction becomes computationally prohibitive for moderate-sized problems, we propose a filtering approach to prune the working set. Empirical results demonstrate that this filtering is effective at reducing computation while incurring only negligible reduction in quality. In summarizing our findings, we provide a characterization of the run-time vs quality trade-off involved.

cs.RO↗