Adam A. Holmes
Hi! I’m a Computational Physicist and AI Researcher (Ph.D. Theoretical Physics, Cornell). I build accurate and efficient computational systems by combining paradigms: deterministic with stochastic, symbolic with neural, exact with approximate.
This thread runs through all my work. In quantum chemistry, I combined deterministic wavefunction selection with stochastic perturbation theory to make exact calculations tractable at scale (SHCI, 2,100+ citations). Now I apply the same thinking to AI, pairing symbolic reasoning with learned neural components so that each handles what it’s best at. I’ve been building production AI systems since 2018 (Transformer-based semantic search, pre-BERT), deployed my algorithms on some of the largest supercomputers in the world at Lawrence Livermore National Lab, and built quantitative models for systematic trading at Citadel.
Neurosymbolic AI
| Project | Description | Tech |
|---|---|---|
| Geometry Theorem Prover | Neurosymbolic prover that combines exhaustive symbolic deduction (49 rules to fixed point) with neural-guided MCTS over auxiliary constructions. A 4M-parameter transformer learns the creative step that deduction alone can’t do. Solves 189/231 problems on AlphaGeometry’s JGEX benchmark, including Morley’s theorem and the 9-point circle. | Rust PyO3 PyTorch |
| Neurosymbolic Chess Engine | Chess engine where symbolic reasoning (mate search, material-aware quiescence search) gates neural evaluation. Symbolic knowledge dramatically accelerates learning: +600 Elo over the pure neural baseline (AlphaZero-style) in 20 vs 30 generations of self-play. | Rust MCTS PyTorch |
Algorithms & Optimization
| Project | Description | Tech |
|---|---|---|
| MMR-Elites | Quality-Diversity algorithm that reformulates archive maintenance as submodular maximization via Maximum Marginal Relevance from information retrieval. Fixed O(K) memory, O(K log K) selection, 12x better uniformity than MAP-Elites in 20-dimensional behavior spaces. | Rust PyO3 Python |
Quantum Chemistry
| Project | Description | Tech |
|---|---|---|
| Arrow / SHCI | Reference implementation of Semistochastic Heat-Bath Configuration Interaction, the method I invented during my Ph.D. Combines deterministic selection of important wavefunction components with stochastic perturbative corrections. Hybrid MPI+OpenMP. | C++ MPI OpenMP |
| RISQ | Rust implementation of SHCI for near-exact electronic structure calculations. Bitstring determinant representation, Davidson eigensolver, and alias sampling for O(1) stochastic draws. | Rust |
Foundational Research
My Ph.D. research introduced Heat-Bath Configuration Interaction (HCI) (Holmes et al., JCTC 2016) and Semistochastic HCI (Sharma, Holmes et al., JCTC 2017), methods that replaced inefficient generate-and-test approaches with deterministic selection of the most significant wavefunction components, combined with stochastic sampling for perturbative corrections. This deterministic + stochastic combination made previously intractable calculations routine.
These methods enabled the first near-exact potential energy surfaces for fourteen electronic states of the carbon dimer (Holmes et al., JCP 2017) and the ground-state binding curve of the chromium dimer (Li, Yao, Holmes et al., Phys. Rev. Res. 2020), a grand-challenge problem that had remained outstanding for decades. SHCI is now a leading benchmark method implemented in major quantum chemistry packages.
Contact
I’m always happy to chat about research, projects, or opportunities. Reach me via email or on LinkedIn.